Blogs by Draxlr

Best BigQuery Reporting Tools

hello@draxlr.com (Draxlr) — Sat, 20 Jun 2026 00:00:00 GMT

BigQuery is one of the most powerful cloud data warehouses available — serverless, massively scalable, and built for analytical queries across billions of rows. But storing and querying data is only part of the equation. Once your team starts running analyses in BigQuery, the next question quickly becomes: how do you turn those results into dashboards, share insights across your organization, and report on data without writing SQL every time? Engineering and data teams often start by building dashboards themselves — pulling results from BigQuery, writing frontend components, and wiring up charts. That process works initially, but it rarely stays manageable. Queries evolve, schema changes break charts, and nontechnical teammates end up dependent on engineering for every data question. What starts as a weekend project becomes a maintenance burden. **BigQuery reporting tools** solve this by sitting between your warehouse and your team. They connect directly to BigQuery, allow you to build reusable dashboards and queries, and make data accessible without engineering involvement for every request. Whether you need internal visibility, customer-facing analytics, or embedded dashboards inside your product, the right tool eliminates the need to build and maintain your own reporting layer. In this article, we break down the most reliable BigQuery reporting tools worth considering for 2026. --- ## What are BigQuery reporting tools? **BigQuery reporting tools** are analytics and dashboarding platforms that connect directly to your BigQuery data warehouse and let teams build charts, dashboards, and automated reports without running SQL queries manually for every request. Instead of exporting data or duplicating it into another system, these tools query BigQuery in place — keeping your data live, consistent, and governed by the same access controls your warehouse already enforces. For teams working at scale, this matters. BigQuery is designed to handle massive datasets efficiently, and the best reporting tools take advantage of that by pushing queries down to the warehouse rather than extracting data into a separate engine. This approach avoids data staleness, eliminates redundant storage costs, and ensures every dashboard reflects the most current state of your data. --- ## How to choose a BigQuery reporting tool Choosing the right BigQuery reporting tool depends on the goals your team is optimizing for. A startup that wants quick internal dashboards will have very different priorities than a SaaS company embedding analytics inside a customer-facing product. Start by identifying your primary goal. ### 1. If your goal is internal reporting for your team Look for a tool with strong SQL support, a visual query builder, and straightforward sharing. You want dashboards your team can actually use without engineering involvement on every question. ### 2. If your goal is customer-facing analytics Prioritize embeddability, row-level filtering for multi-tenant applications, and white-label controls. Your customers should experience analytics that feel native to your product. ### 3. If your goal is replacing an existing analytics stack Look for tools with flexible permissions, reusable metric definitions, scheduling, and scalable embedding. These features streamline your entire analytics layer rather than just adding another chart-building tool. ### 4. If your goal is minimal engineering involvement Pick a tool that gives product managers, operations teams, and business analysts the ability to explore BigQuery data independently. Features like visual query builders, AI text-to-SQL, and saved reports reduce the number of ad-hoc requests engineers get pulled into. The right BigQuery reporting tool depends on whether you're optimizing for speed, customer experience, or long-term scalability. --- ## Key features to look for in BigQuery reporting tools Not every reporting tool handles BigQuery equally well. Some are designed for general SQL databases and treat BigQuery as an afterthought. Others are built with cloud data warehouses in mind and take full advantage of BigQuery's performance. Here's what to evaluate when comparing your options. ### 1. Native BigQuery compatibility Your reporting tool should connect directly to BigQuery using your existing project credentials — no ETL pipelines, no data duplication, and no separate storage layer. Native support means: 1. Queries push down to BigQuery's execution engine 2. No data movement or sync delays 3. Consistent access controls 4. No duplicate storage costs Tools that extract data before rendering dashboards will slow you down and undermine the scale advantages BigQuery provides. ### 2. Ease of dashboard building Most teams using BigQuery don't have dedicated BI analysts managing every dashboard. Look for features that make reporting accessible: 1. Visual query building 2. [Text to SQL AI](https://www.draxlr.com/blogs/ai-sql-tool/) 3. Reusable charts and saved queries 4. Filters and drill-down controls 5. Clear layout and sharing options Anyone on your team should be able to answer basic data questions without writing SQL or depending on engineering. ### 3. Embeddability (for customer-facing dashboards) If you're building analytics into your product, choose a tool designed for [embedded analytics](https://www.draxlr.com/blogs/best-embedded-analytics-tools/). Key requirements include: 1. Secure, stateless embed tokens 2. Row-level filtering for multi-tenant applications 3. White-label styling controls 4. SDK components for React and Vue Customer-facing dashboards should feel like part of your product, not an iframe dropped on top of it. ### 4. API access for automation BigQuery teams often automate workflows — scheduled reports, data alerts, query triggers, and embedding logic. An API-first reporting tool lets you: 1. Control reports programmatically 2. Integrate with your authentication layer 3. Automate recurring queries and exports 4. Trigger alerts when data crosses a threshold Developers should be able to manage reporting via API just like any other part of their stack. ### 5. Data security and access control BigQuery holds your most sensitive analytical data. Your reporting tool needs to respect that. Important security features include: 1. No data copying outside your warehouse 2. User and team-scoped dashboard permissions 3. Secure embed tokens with user context 4. [Self-host option](https://www.draxlr.com/blogs/ensuring-seamless-adoption-of-self-service-bi/) for sensitive environments Access control is not an optional feature — it's foundational. ### 6. Documentation, support, and reliability Reporting tools get implemented under time pressure and often by teams without dedicated BI resources. Good documentation, responsive support, and a stable, performant UI save hours when things go wrong. Pick a tool that won't leave you debugging dashboards when your team needs answers. --- ## The best BigQuery reporting tools to consider in 2026 Without further delay, here's a breakdown of the top BigQuery reporting tools worth exploring in 2026 and beyond. ### 1. Draxlr: Best BigQuery reporting tool for dashboards, team insights, and embedded analytics ![Draxlr — BigQuery reporting tool](/images/blogs/_tools/draxlr.png) #### Best for SaaS teams and data analysts using BigQuery who want a fast, reliable way to build dashboards, share reports, and deliver embedded analytics without adding a complex BI stack. Connect your Database [Draxlr](https://www.draxlr.com/) connects directly to BigQuery and gives teams a clean interface for creating reports, running SQL, and building dashboards without setup overhead. It's built for modern data-driven products — helping teams ship reporting fast whether the goal is internal visibility or customer-facing analytics embedded inside an app. For technical users, Draxlr provides full SQL control, virtual columns, filters, drill-through features, and database-aware autocomplete tuned for BigQuery's SQL dialect. For nontechnical teammates, the visual query builder and AI text-to-SQL make it easy to explore data without touching the SQL editor. You can [build BigQuery dashboards](https://www.draxlr.com/databases/bigquery/) with interactive charts, define dashboard-level filters that control multiple charts simultaneously, and drill into specific segments with a single click. If your product includes customer analytics, Draxlr supports secure embedded dashboards with row-level filtering, white-label options, and React/Vue components that integrate naturally into your application. You can pass customer context at embed time to ensure each user sees only their own data. Teams can also automate reporting with scheduled email reports, Slack alerts, and CSV/Excel exports. Draxlr runs on your existing BigQuery infrastructure — no pipelines, additional warehouses, or modeling layers required. #### Key features 1. Connects directly to BigQuery with no ETL or data movement 2. Visual Query Builder, full SQL editor, and AI Chat for text-to-SQL 3. Dashboard builder with filters, drill-through, and virtual columns 4. Embedded dashboards with secure tokens, row-level filters, and white-label controls 5. React and Vue SDKs for in-product analytics 6. Export options (CSV, Excel, PDF) and scheduled reports via Slack or email 7. Team-level permissions for safe access to warehouse data 8. Supports both internal reporting and customer-facing analytics from a single workspace #### What users say about Draxlr > Draxlr offers a clean, modern interface and makes it incredibly easy to build dashboards — even for non-technical users. I especially like the visual quality and flexibility of the available graph types. It's intuitive, fast to set up, and has excellent usability from the start. The ability to securely share dashboards via link is a game-changer for our client reporting. — [Review from a SaaS Founder on G2](https://www.g2.com/products/draxlr/reviews/draxlr-review-11185807) #### Pricing Draxlr offers multiple plans depending on team size, embedding needs, and data volume. Pricing starts at **$75/month**. --- ### 2. Looker Studio: Best free BigQuery reporting tool for Google-native teams ![Looker Studio — BigQuery reporting tool](/images/blogs/_tools/looker-studio.png) #### Best for Teams already in the Google ecosystem who want a free, browser-based reporting tool with a native BigQuery connector and no infrastructure to manage. [Looker Studio](https://lookerstudio.google.com/) (formerly Google Data Studio) is Google's free BI and reporting platform, and it has the tightest native integration with BigQuery of any tool on this list. Authentication happens through your Google account, queries run directly on BigQuery using its execution engine, and the tool supports BigQuery BI Engine acceleration for sub-second dashboard loading on frequently queried data. The editing experience is browser-based and drag-and-drop, making it accessible to nontechnical users. You can connect to GA4, Google Ads, Google Sheets, and BigQuery from a single workspace. Dashboards can be shared freely across your organization. Looker Studio Pro, at $9/user/month, adds enterprise features like team workspaces, scheduled delivery, and dedicated support. The main limitations are its lack of native row-level security, no built-in alerting, and performance that can degrade on complex or high-volume BigQuery queries. For teams that need embedding, multi-tenant support, or advanced permissions, it falls short. #### Key features 1. Native BigQuery connector with direct query execution — no ETL required 2. BigQuery BI Engine support for accelerated dashboard loading 3. Drag-and-drop report builder accessible to nontechnical users 4. Integrations with GA4, Google Ads, Sheets, and 800+ data sources via connectors 5. Free base tier; Looker Studio Pro at $9/user/month for enterprise controls #### What users say about Looker Studio > Looker Studio makes it incredibly easy to build interactive, shareable dashboards and reports — all without coding. The native connectors including BigQuery and Google Analytics work seamlessly out of the box. #### Pricing Free. Looker Studio Pro is $9/user/month. Note that BigQuery query costs ($6.25/TiB processed after the free tier) apply separately. --- ### 3. Tableau: Best BigQuery reporting tool for advanced data visualization ![Tableau — BigQuery reporting tool](/images/blogs/_tools/tableau.png) #### Best for Organizations that need sophisticated visualizations, analyst-driven workflows, and have the BI resources to manage a full-featured enterprise platform. [Tableau](https://www.tableau.com/) connects to BigQuery using a native JDBC connector and offers one of the most comprehensive visualization libraries in the market — 80+ chart types, advanced mapping, and statistical analysis functions. For teams that need complex analytical exploration across large datasets, Tableau's depth is hard to match. The trade-off is complexity and cost. Tableau requires meaningful setup, modeling, and BI analyst expertise to get the most out of. For fast-moving SaaS teams or companies without dedicated BI staff, the operational overhead often outweighs the visualization benefits. It's a strong fit for enterprise teams that already have BI workflows in place and want to point them at BigQuery. Tableau also offers Tableau Cloud for managed hosting and Tableau Server for on-premises deployments, giving organizations flexibility over their data governance model. #### Key features 1. Native BigQuery JDBC connector with live query support 2. 80+ chart types including advanced maps, statistical charts, and custom visuals 3. Tableau Prep for data modeling and transformation before dashboarding 4. Tableau Cloud and Tableau Server deployment options 5. Enterprise governance, row-level security, and version control #### What users say about Tableau > What I like best about Tableau is its design philosophy — it emphasizes data exploration and visual storytelling over rigid reporting. The depth of visualization options means I can always find the right way to present our BigQuery data. #### Pricing Tableau Creator licenses start at $75/user/month. Explorer and Viewer tiers available at lower price points. Enterprise pricing negotiated separately. --- ### 4. Metabase: Best open-source BigQuery reporting tool for self-hosted teams ![Metabase — BigQuery reporting tool](/images/blogs/_tools/metabase.png) #### Best for Engineering-led teams that want an open-source BI tool they can self-host, and that need to make BigQuery data accessible to nontechnical teammates with minimal setup. [Metabase](https://www.metabase.com/) is one of the most widely used open-source BI tools and has solid native BigQuery support. It connects through your BigQuery project credentials and lets both technical and nontechnical users explore data — technical users through a full SQL editor, and nontechnical users through Metabase's "Questions" interface, which generates SQL behind the scenes from a visual query flow. For teams that want to self-host their analytics layer, Metabase is free and open-source. The hosted cloud version starts at a reasonable price for small teams. Dashboards can be shared internally, scheduled for email delivery, or embedded in external applications using signed URLs. The embedding capability is more limited than purpose-built tools but works for many straightforward use cases. The main limitation is that Metabase's UX can feel dated for complex analytical work, and the embedded analytics features are less polished than dedicated embedding-focused tools. #### Key features 1. Native BigQuery connector — no ETL or data movement 2. Visual "Questions" builder for nontechnical users alongside a full SQL editor 3. Dashboard builder with filters, drill-down, and scheduled reports 4. Open-source self-hosted option (free) and Metabase Cloud starting at $85/month 5. Embedding via signed URLs for basic customer-facing analytics #### What users say about Metabase > I love the Question feature of Metabase, which allows for the creation of no-code SQL queries that can be easily and intuitively answered even by non-technical users. It's great that you can access joinable fields without an active join — this makes it a breeze to create entire dashboards in a short amount of time. #### Pricing Open-source and free to self-host. Metabase Cloud starts at $85/month. Pro and Enterprise tiers available with additional embedding and governance features. --- ### 5. Sigma Computing: Best BigQuery reporting tool for business users who want a spreadsheet interface ![Sigma Computing — BigQuery reporting tool](/images/blogs/_tools/sigma-computing.png) #### Best for Data and business teams who want to explore large BigQuery datasets using a familiar spreadsheet interface without needing SQL expertise. [Sigma Computing](https://www.sigmacomputing.com/) is a cloud-native analytics platform that connects directly to BigQuery (as well as Snowflake, Redshift, and Databricks) and executes all queries live against the warehouse. What sets Sigma apart is its interface — instead of a dashboard builder or a SQL editor, Sigma presents data in a spreadsheet-style grid that business users find immediately familiar. This interface is deceptively powerful. Business users can pivot, filter, and aggregate BigQuery data using point-and-click interactions, while data teams can drop into SQL or Python when needed. Sigma also supports dashboard publishing, data applications, and embedded analytics. For organizations with large business user populations that need BigQuery access, the unlimited viewer model on some plans provides strong value. Performance can be uneven on very large tables or complex queries, and costs can escalate without careful query governance. But for teams where analyst-to-business-user collaboration is the priority, Sigma's interface reduces the barrier to BigQuery exploration significantly. #### Key features 1. Native BigQuery connection with live query push-down — no data extraction 2. Spreadsheet-style interface for business users alongside SQL and Python editors 3. Dashboard publishing and embedded analytics support 4. Unlimited viewers on Essentials and higher plans 5. Native AI features for automated insights and query assistance #### What users say about Sigma Computing > I like Sigma's spreadsheet interface — it makes it easy for me to explore data without needing advanced technical skills, and it integrates well with cloud data warehouses like BigQuery. Even without SQL expertise, I can answer complex questions about our data. #### Pricing Essentials starts at $300/month with unlimited users. Professional and Enterprise tiers are custom-priced. --- ### 6. Holistics: Best BigQuery reporting tool for SQL-centric data teams with a modeling workflow ![Holistics — BigQuery reporting tool](/images/blogs/_tools/holistics.png) #### Best for Data teams that want a code-based, model-driven BI workflow — central metric definitions, Git integration, and reusable datasets — built on top of BigQuery. [Holistics](https://www.holistics.io/) connects natively to BigQuery and takes a semantic-layer approach to BI. Rather than building charts directly against raw tables, data teams define metrics, dimensions, and relationships in a central modeling layer using Holistics' analytics-as-code framework. Those definitions are version-controlled in Git and reused across dashboards — meaning metrics stay consistent organization-wide. For organizations that care about data governance, reproducibility, and avoiding the "which dashboard is correct" problem, Holistics' approach is compelling. Business users get clean, governed dashboards without needing to understand raw schema. Data teams maintain a single source of truth. The tool also supports scheduled email reports, Slack integrations, and self-service exploration through a visual query builder. The trade-off is the upfront investment in modeling. Teams that want to connect BigQuery and build dashboards immediately will find Holistics slower to get started with than tools like Draxlr or Looker Studio. But for mature data teams managing a growing BI stack, the governance model pays off. #### Key features 1. Native BigQuery connector with SQL pass-through support 2. Analytics-as-code semantic modeling layer with Git integration 3. Reusable metric and dimension definitions across dashboards 4. Scheduled email reports and Slack-based alerts 5. Visual query builder for business users on top of the governed data model #### What users say about Holistics > Out of all BI tools we explored, Holistics hit the sweet spot. It was the most robust and user-friendly — and they support SQL pass-throughs. We were able to build reports in a fraction of the time it took with Tableau. — Capterra review #### Pricing Holistics offers team-based pricing with tiers based on usage and feature requirements. Contact their team for current pricing. --- ## Implementing your BigQuery reporting tool effectively Choosing the right BigQuery reporting tool is only half the job. A tool can give you clean dashboards and powerful querying, but without a thoughtful rollout, adoption will stall and the investment won't deliver value. Here's how to get the most out of whichever tool you choose. ### 1. Onboard your team properly Invest time in proper onboarding before handing tools to the full team. Developers, product managers, analysts, and operations staff all have different mental models for working with data. A short, focused kickoff session prevents future bottlenecks, avoids permission misconfigurations, and ensures users build dashboards against the right tables. ### 2. Start with a pilot Roll out dashboards to a small group first rather than the entire organization. A controlled pilot helps you validate that queries run efficiently against BigQuery, that permissions are configured correctly, and that the dashboards actually answer the questions your team is asking. It's faster to refine a small set of dashboards than to fix issues at scale. ### 3. Collect feedback continuously Reporting is not a "set it and forget it" system. Once dashboards go live, actively ask your users — internal or customer-facing — what works and what needs improvement. As your BigQuery schema evolves and your product grows, your reporting layer needs to keep up. ### 4. Monitor performance and query costs BigQuery charges per byte scanned, so the reporting tools and queries you deploy have a direct impact on cost. Monitor which dashboards are running expensive queries, which reports are unused, and where performance can be improved through caching or query optimization. Understanding usage patterns helps you manage both cost and user experience over time. --- ## Conclusion The BigQuery reporting tool you choose shapes how your team interacts with data — and for products with customer-facing analytics, it shapes how your users understand their own results. It needs to perform reliably at the query volumes BigQuery enables, fit the technical capabilities of the people using it, and deliver a clear, consistent experience whether it's powering internal dashboards or embedded inside your product. If you want a reporting layer that works naturally with BigQuery without extra infrastructure, pipelines, or a complex modeling setup, tools like Draxlr help you ship dashboards quickly while keeping your stack simple. Whether you need internal visibility, embedded analytics, or customer-facing dashboards, the right BigQuery reporting tool should help you move fast without compromising on data accuracy or user experience. If you'd like to explore a BigQuery-native approach to dashboards and reporting, you can [try Draxlr](https://app.draxlr.com/register/) and connect your warehouse in minutes. --- ## FAQs ### 1. What is a BigQuery reporting tool? A BigQuery reporting tool connects directly to your Google BigQuery data warehouse and lets you build dashboards, charts, and reports without writing SQL for every request. These tools help teams explore live warehouse data safely, share insights across the organization, and automate recurring reports — without needing to set up ETL pipelines or duplicate data. ### 2. Can I build customer-facing dashboards with BigQuery data? Yes. Several BigQuery reporting tools support embedding dashboards inside your product. Look for options with secure embed tokens, row-level filtering for multi-tenant applications, and white-label styling so the analytics feel native to your app rather than like a third-party add-on. ### 3. Does BigQuery have built-in reporting or dashboarding? BigQuery includes a SQL editor and basic data exploration in the Google Cloud Console, but it's not designed for dashboards or repeatable reporting for business users. Looker Studio is Google's free dashboarding layer for BigQuery, though purpose-built tools like Draxlr offer more flexibility for product and team analytics use cases. ### 4. Is it safe to connect a reporting tool directly to BigQuery? Yes, as long as the tool uses secure credential management, respects your IAM roles, and supports user-scoped filtering when embedding dashboards. Avoid tools that require exporting or duplicating your BigQuery data into a separate storage layer — this creates unnecessary cost and data governance risk. ### 5. Can non-technical team members use BigQuery reporting tools? Yes. Several tools are specifically designed to make BigQuery data accessible without SQL knowledge. Look for visual query builders, AI text-to-SQL features, saved report templates, and easy sharing — so product, operations, and support teams can explore data without developer involvement. ### 6. Do I need to move my data out of BigQuery to use a reporting tool? No. The best BigQuery reporting tools query your data warehouse directly without extracting, copying, or moving data. This keeps your dashboards live, avoids double storage costs, and ensures that permissions and governance policies remain consistent with your BigQuery setup.

Best Amazon Redshift Reporting Tools

hello@draxlr.com (Draxlr) — Fri, 19 Jun 2026 00:00:00 GMT

Amazon Redshift is one of the most widely adopted cloud data warehouses in use today, and for good reason. It handles petabyte-scale analytical workloads, integrates seamlessly with the AWS ecosystem, and gives data teams a powerful SQL interface for complex queries. But raw warehouse access is not the same as actionable reporting. As Redshift deployments mature, the SQL console starts showing its limits. Analysts spend time answering repetitive questions, product teams want to see live data without learning SQL, and executives need dashboards that update without manual exports. What starts as a powerful data asset quickly becomes a bottleneck if teams don't have the right reporting layer on top of it. **Redshift reporting tools** bridge the gap between your warehouse and the people who need to understand what's inside it. They give teams a clean, governed layer for building dashboards, running saved queries, sharing reports, and — when needed — embedding analytics directly into products. In this article, we break down the best Redshift reporting tools worth evaluating in 2026. --- ## What are Amazon Redshift reporting tools? **Amazon Redshift reporting tools** are analytics and dashboard platforms that connect directly to your Redshift cluster or serverless endpoint and let you build reports, charts, and dashboards without writing raw SQL for every question. They sit between your data warehouse and your users, giving teams structured access to Redshift data through visual interfaces, saved queries, and governed dashboards. Unlike general BI tools that require you to first move data into a proprietary store, the best Redshift reporting tools query your warehouse directly. Metrics stay fresh, your existing column-level and row-level permissions apply, and there's no secondary sync to manage. Whether your goal is internal team reporting, executive visibility, or customer-facing embedded analytics, these tools let you put Redshift data to work without writing a new application from scratch. --- ## How to choose an Amazon Redshift reporting tool Choosing the right Redshift reporting tool depends on how your team is structured, what kind of reporting you need, and how much technical overhead you're willing to manage. The options range from lightweight tools that non-technical users can operate independently to deeply configurable platforms designed for analyst-heavy workflows. Start by identifying your primary goal. ### 1. If your goal is internal reporting for your team Pick a tool with strong SQL support, an intuitive dashboard builder, and simple sharing controls. You want analysts and product managers to be able to answer questions without involving engineering every time. ### 2. If your goal is customer-facing analytics Look for embedding, white-label controls, and row-level filtering. Your customers should see analytics that feel like a natural part of your product, not an embedded third-party tool. ### 3. If your goal is replacing an existing analytics stack Prioritize tools with flexible permissions, reusable query definitions, scheduling, and scalable embedding. These features ensure your reporting layer grows with your Redshift investment rather than becoming another silo. ### 4. If your goal is minimal engineering involvement Choose a tool that empowers product, operations, and support teams to explore Redshift data on their own. Visual query builders, natural language interfaces, and AI-assisted SQL reduce the number of ad-hoc data requests landing on your engineering team. --- ## Key features to look for in Redshift reporting tools Not all reporting tools handle a data warehouse like Redshift equally. Some are optimized for transactional databases, others for smaller datasets. Choosing a tool built for warehouse-scale workloads makes a meaningful difference in both performance and developer experience. Here are the core features to evaluate when comparing Redshift reporting tools. ### 1. Native Redshift compatibility Your reporting tool should connect to Redshift directly — via JDBC or ODBC, using your cluster credentials or IAM role — without requiring a middleware sync or proprietary connector. Native support means: 1. Queries execute against your live warehouse 2. No secondary data store to maintain 3. Column-level and row-level permissions are honored 4. Redshift-specific SQL extensions work as expected Tools that require you to replicate or reshape data before building reports will slow you down and introduce data freshness problems. ### 2. Ease of dashboard building Redshift sits at the center of many organizations' data infrastructure, but most of the people who need access to that data aren't comfortable writing SQL. Look for features like: 1. Visual query building without SQL 2. [AI-assisted text-to-SQL](https://www.draxlr.com/blogs/ai-sql-tool/) 3. Reusable chart templates 4. Filters and drilldown controls 5. Shareable dashboard links 6. Scheduled reports and alerts The right tool should reduce the analytics bottleneck on your engineering team, not create a new one. ### 3. Embeddability If your product includes analytics for end users, your Redshift reporting tool needs to support secure embedding. Key capabilities include: 1. Token-based embed authentication 2. Row-level filtering per customer or tenant 3. White-label styling to match your product's look 4. React or Vue SDKs for clean in-product integration Dashboards embedded from a [Redshift-backed reporting tool](https://www.draxlr.com/blogs/best-embedded-analytics-tools/) should feel native inside your application, not bolted on. ### 4. API access for automation Redshift-backed reporting should integrate with the rest of your stack. An API-first tool lets you: 1. Trigger report generation programmatically 2. Automate scheduled exports 3. Build custom alerting pipelines 4. Embed authentication flows into your app's auth layer This is especially important for SaaS teams who want to automate reporting as part of their broader product. ### 5. Data security and access control Redshift typically stores your most sensitive business data — revenue, customer activity, operational metrics. Your reporting layer needs to match those expectations: 1. No copying or caching data outside your Redshift cluster (where avoidable) 2. User-scoped and role-scoped dashboard access 3. Row-level security for multi-tenant reporting 4. Secure embed tokens that expire appropriately ### 6. Documentation, support, and performance A warehouse query tool needs to handle complex joins, wide result sets, and high-concurrency access. Before choosing, evaluate whether the tool can handle your query volume and what happens when something breaks. Good documentation, active support channels, and proven reliability under load matter as much as the feature list. --- ## The best Amazon Redshift reporting tools to consider in 2026 Here's a breakdown of the top Redshift reporting tools worth evaluating in 2026. ### 1. Draxlr: Best Redshift reporting tool for dashboards, team insights, and embedded analytics ![Draxlr — Amazon Redshift reporting tool](/images/blogs/_tools/draxlr.png) #### Best for SaaS teams and data-forward companies using Amazon Redshift who want a fast, practical way to build dashboards, explore warehouse data, and offer embedded analytics without managing a heavy BI stack. Connect your Database [Draxlr](https://www.draxlr.com/) connects directly to your Amazon Redshift cluster and gives your team a clean interface for creating reports, running SQL, and building dashboards without setup overhead. It's designed for modern SaaS and data teams that want to ship reporting fast — whether the goal is internal visibility into warehouse data or customer-facing analytics embedded inside their product. For technical users, Draxlr offers full SQL control, virtual columns, filters, drill-through features, and warehouse-aware autocomplete. For nontechnical teammates, the visual query builder and AI text-to-SQL make Redshift data exploration accessible without needing to write a single query. If your product includes customer analytics, Draxlr supports secure embedded dashboards with row-level filtering, white-label options, and React/Vue components that integrate naturally into your application. Teams can also automate reporting with scheduled emails, Slack alerts, and CSV/Excel exports — without building a custom pipeline. Draxlr runs directly on your existing Redshift infrastructure. No ETL pipelines, no modeling layers, and no extra warehouses to manage. #### Key features 1. Connects directly to Amazon Redshift with no ETL or data prep required 2. Visual Query Builder, full SQL editor, and AI Chat for text-to-SQL 3. Dashboard builder with filters, drill-through, and virtual columns 4. Embedded dashboards with secure tokens, row-level filters, and white-label controls 5. React and Vue SDKs for easy in-product analytics 6. Export options (CSV, Excel, PDF) and scheduled reports 7. Team-level permissions for safe, governed access to warehouse data 8. Supports both internal reporting and customer-facing analytics from the same workspace #### What users say about Draxlr > Draxlr offers a clean, modern interface and makes it incredibly easy to build dashboards — even for non-technical users. I especially like the visual quality and flexibility of the available graph types. It's intuitive, fast to set up, and has excellent usability from the start. The ability to securely share dashboards via link is a game-changer for our client reporting. — [Review from a SaaS Founder on G2](https://www.g2.com/products/draxlr/reviews/draxlr-review-11185807) #### Pricing Draxlr offers multiple plans depending on team size, embedding needs, and data volume. Pricing starts at **$75/month** --- ### 2. Amazon QuickSight: Best for teams already inside the AWS ecosystem ![Amazon QuickSight — Amazon Redshift reporting tool](/images/blogs/_tools/amazon-quicksight.png) #### Best for Organizations that want AWS-native reporting directly on top of Redshift with minimal configuration and native IAM integration. [Amazon QuickSight](https://aws.amazon.com/quicksight/) is AWS's built-in business intelligence service and the most natural pairing with Amazon Redshift. Because both live inside your AWS account, connecting QuickSight to your Redshift cluster takes minutes, and permissions can be managed through IAM roles rather than separate credential management. QuickSight includes SPICE — an in-memory computation engine — for fast dashboard loading, as well as QuickSight Q for natural language queries. It scales automatically to thousands of readers and supports row-level security for multi-tenant scenarios. For teams who are already managing infrastructure in AWS and don't want to introduce an external BI vendor, QuickSight keeps everything consolidated inside a single cloud account. That said, QuickSight's visualization library is more limited than Tableau or Power BI, and its embedding model requires more setup work than purpose-built embedded analytics tools. #### Key features 1. Native AWS integration — connects to Redshift via IAM in minutes 2. SPICE in-memory engine for fast, cached queries at scale 3. QuickSight Q for natural language data exploration 4. Row-level security for multi-tenant or role-based access 5. Automatic scaling for thousands of readers without infrastructure management #### What users say about Amazon QuickSight > QuickSight's integration with the AWS ecosystem is seamless. Connecting to S3, Redshift, or Athena happens in minutes without complex configuration or workarounds. — G2 reviewer #### Pricing QuickSight uses per-session pricing starting at approximately $0.30 per session for the Standard edition and $0.50 per session for Enterprise. Author seats run around $18/month. Pricing scales with usage and is generally lower for high-reader, low-author deployments. --- ### 3. Metabase: Best open-source Redshift reporting tool for teams that want flexibility ![Metabase — Amazon Redshift reporting tool](/images/blogs/_tools/metabase.png) #### Best for Engineering-forward teams and startups that want a capable, self-hostable reporting tool with a low barrier to entry for business users. [Metabase](https://www.metabase.com/) connects directly to Amazon Redshift and lets both technical and non-technical users explore warehouse data through a visual query interface or a full SQL editor. Setup is fast — you can have Redshift connected and your first dashboard built in under 15 minutes. Metabase's question builder is one of the best-designed visual interfaces for ad-hoc exploration without SQL. Metabase also supports database permissions impersonation specifically for Redshift, meaning it can honor the access controls already defined in your Redshift instance rather than layering a separate permissions model on top. Teams that want an open-source BI foundation with solid Redshift support tend to start here before deciding whether to upgrade to paid plans or self-host indefinitely. Embedding is supported on paid plans. The open-source edition is fully functional for internal dashboards and exploratory analytics. #### Key features 1. Direct Redshift connection via JDBC with 5-minute setup 2. Visual question builder for no-code Redshift querying 3. Database permission impersonation for Redshift-native access control 4. Drill-through, filters, and breakout functionality out of the box 5. Open-source edition free to self-host; paid plans add SSO and advanced embedding #### What users say about Metabase > I love the Question feature of Metabase, which allows for the creation of no-code SQL queries that can be easily and intuitively answered even by non-technical users. It makes it a breeze to create entire dashboards in a short amount of time. — G2 reviewer #### Pricing Metabase's open-source edition is free. Starter plans begin at approximately $1,080/year plus $65/year per user. Pro plans start around $6,210/year plus $130/year per user. Enterprise pricing is custom. --- ### 4. Tableau: Best for analyst teams that need deep visualization on Redshift ![Tableau — Amazon Redshift reporting tool](/images/blogs/_tools/tableau.png) #### Best for Organizations with dedicated BI analysts who need sophisticated, custom visualizations and are willing to invest in a full-featured enterprise platform. [Tableau](https://www.tableau.com/) has a long-standing native connector for Amazon Redshift and is one of the most established tools in the enterprise BI landscape. It supports live queries directly against your Redshift cluster or extract-based workflows where data is cached for performance. Tableau's drag-and-drop interface makes it accessible for analysts, while its underlying VizQL engine handles highly complex visualization logic that simpler tools can't match. Tableau works well for organizations that have existing Tableau Server or Tableau Cloud deployments and want to add Redshift as a data source. For greenfield implementations without dedicated BI resources, the learning curve and licensing cost can be significant. But for analyst-heavy teams with complex visualization needs, Tableau remains one of the most capable Redshift reporting tools available. #### Key features 1. Native Redshift connector with live query and extract modes 2. Sophisticated drag-and-drop visualization builder with hundreds of chart types 3. Tableau Prep for data shaping before it reaches dashboards 4. Enterprise governance, version control, and workspace-level sharing 5. Tableau AI for automated analytics and natural language exploration #### What users say about Tableau > Its native connector for databases like AWS Redshift is being used for data visualization and streamline data analysis. Tableau really brings multiple data sources together and makes it easy to share real-time dashboards across teams. — G2 reviewer #### Pricing Tableau pricing varies by deployment. Tableau Cloud plans start at approximately $70/user/month for Viewer and $115/user/month for Creator. Enterprise and site-wide licensing is available through custom quotes. --- ### 5. Holistics: Best for data teams that want governed, model-driven Redshift reporting ![Holistics — Amazon Redshift reporting tool](/images/blogs/_tools/holistics.png) #### Best for Data engineering teams and analytics teams that want a SQL-first, semantic-layer approach to reporting on Redshift with self-service access for business users. [Holistics](https://www.holistics.io/) connects to Amazon Redshift and offers a structured, model-driven approach to reporting that sits well above raw SQL. Rather than building charts directly on tables, Holistics lets data teams define metrics and dimensions in code using AQL (Analytics Query Language), version-control those definitions in Git, and expose them through a drag-and-drop interface for business users. The result is a governed analytics layer where metric definitions are centralized and consistent across every dashboard. For teams that are already using Redshift alongside dbt, Holistics integrates naturally on top of your dbt models. It also supports dashboard-as-code workflows, automated scheduling, and email delivery — without requiring analysts to rebuild reports manually each time someone asks a question. Holistics does not store or copy your Redshift data, keeping your security posture intact. Support is consistently highlighted as one of the platform's standout qualities. #### Key features 1. Direct Redshift connection with a SQL-based semantic modeling layer (AQL) 2. Git-version-controlled metrics and dimension definitions 3. Drag-and-drop dashboard interface for business users built on top of modeled data 4. Scheduled report delivery via email with no manual intervention 5. Compatible with dbt model workflows and Redshift-specific SQL features #### What users say about Holistics > After evaluating over 40 BI tools, we chose Holistics for its robust semantic modeling layer, which enabled us to create a centralized source of truth for our users. The benefits have been transformative. — Capterra reviewer #### Pricing Holistics offers usage-based pricing that scales with your team. Specific tiers are available on their pricing page. A free trial is available. --- ### 6. Looker Studio: Best free Redshift reporting tool for lightweight use cases ![Looker Studio — Amazon Redshift reporting tool](/images/blogs/_tools/looker-studio.png) #### Best for Small teams, early-stage companies, or individual analysts who need a free, accessible way to build reports on Redshift data without licensing overhead. [Looker Studio](https://lookerstudio.google.com/) (formerly Google Data Studio) offers a free, browser-based reporting tool that connects to Amazon Redshift through its native connector. For teams that need basic dashboards, shareable reports, and simple chart-building on top of Redshift data without a budget for dedicated BI tools, Looker Studio is often the easiest starting point. The tool is particularly well-suited for marketing, operations, or product teams that want to slice warehouse data and share reports without involving engineering. Because it's free and browser-based, there's no installation or licensing process to navigate. The trade-offs are real, however: Looker Studio's row limit per query caps at 150,000 rows, queries can time out on complex joins, and the embedding options are limited compared to purpose-built analytics tools. For straightforward reporting needs without budget constraints, it remains a practical starting point. #### Key features 1. Free native connector to Amazon Redshift 2. Browser-based report builder with drag-and-drop chart creation 3. Easy report sharing via Google account-based permissions 4. Integration with other Google Workspace data sources 5. Scheduled email delivery of reports to stakeholders #### What users say about Looker Studio > Looker Studio is the easiest way to get started with visual reporting on warehouse data. The free tier is genuinely useful for non-technical teams who just need to share some charts without setting up an entire BI stack. — G2 reviewer #### Pricing Looker Studio is free. Looker Studio Pro, which adds team workspaces and SLA-backed support, is available at $9/user/month. --- ## Implementing your Redshift reporting tool effectively Choosing the right Redshift reporting tool is only the first step. A tool can give you dashboards and SQL access, but it won't deliver value unless your team knows how to use it and your implementation is set up for long-term success. Here's how to get the most out of whichever Redshift reporting tool you choose. ### 1. Onboarding your team Invest in proper onboarding before rolling dashboards out broadly. Analysts, product managers, and operations teams should understand how to navigate the tool, create saved queries, and share dashboards. A focused onboarding session prevents ad-hoc misconfigurations and reduces the number of duplicate reports that accumulate over time. ### 2. Start with a pilot Rather than deploying to your whole organization at once, start with a focused pilot group. A controlled rollout lets you validate metric definitions, confirm that Redshift query performance holds up, and identify which dashboards actually get used versus which ones go stale. It also gives you time to refine permissions and access controls before wider deployment. ### 3. Collect feedback continuously Reporting layers need to evolve alongside your product and your data. After launch, actively collect feedback from the people using dashboards daily. Questions they can't answer in the current interface, metrics that are missing, and visualizations that don't tell the right story should all feed back into your reporting roadmap. ### 4. Monitor performance and usage Track dashboard load times, query execution patterns, and active report usage. Redshift is a powerful warehouse, but poorly optimized queries — full table scans, missing sort keys, cartesian joins — will create slow dashboards that teams stop trusting. Understanding which reports are used versus ignored helps you focus optimization effort where it matters most. --- ## Conclusion The Redshift reporting tool you choose becomes the lens through which your team and your customers understand what's happening in your business. It needs to surface the right data at the right time, load reliably, and give users enough control to answer their own questions without routing everything through a data analyst. If you want a reporting layer that connects to Redshift without extra infrastructure, pipelines, or modeling layers, [Draxlr](https://www.draxlr.com/) helps you ship dashboards quickly while keeping your stack simple. Whether you need internal visibility for your team, customer-facing embedded analytics, or scheduled reports delivered to stakeholders, Draxlr works directly with your Redshift cluster — no new infrastructure required. If you'd like to explore a Redshift-native approach to dashboards and reporting, you can [try Draxlr](https://app.draxlr.com/register/) and see how it fits your workflow. --- ## FAQs ### 1. What is an Amazon Redshift reporting tool? An Amazon Redshift reporting tool connects directly to your Redshift cluster and lets you build dashboards, charts, and reports without writing SQL for every request. These tools help teams explore live warehouse data safely and share insights without requiring everyone to have direct SQL access to production. ### 2. Can I build customer-facing dashboards on top of Redshift data? Yes. Several reporting tools support embedding dashboards inside your product using data sourced from Redshift. Look for options like secure embed tokens, row-level filtering for multi-tenant applications, and styling controls so the dashboards match your product's UI. Draxlr, for example, supports all three. ### 3. Does Amazon Redshift have built-in reporting? Amazon Redshift includes a query editor and integrates natively with Amazon QuickSight, but it doesn't include a general-purpose reporting or dashboard layer. For teams that need shared dashboards, visual query building, or embedded analytics, a dedicated reporting tool is required. ### 4. Is it safe to connect a reporting tool directly to Amazon Redshift? Yes, as long as the tool uses secure connections, respects your Redshift IAM or credential-based access controls, and supports row-level security where needed. Tools that don't copy or cache your data outside the cluster are generally the safest option. Always use read-only credentials for reporting workloads. ### 5. Can non-technical team members use Redshift reporting tools? Yes. Tools like Draxlr, Metabase, and Holistics all offer visual query builders, saved dashboards, and sharing features that allow product managers, support teams, and operations staff to work with Redshift data without writing SQL. ### 6. Do I need to move my Redshift data somewhere else to build dashboards? No. The best Redshift reporting tools query your cluster directly, so there's no need to export or replicate data into a separate store. Direct connections are faster to set up, keep your data fresher, and avoid the complexity of managing a secondary data pipeline.

Best CockroachDB Reporting Tools

hello@draxlr.com (Draxlr) — Thu, 18 Jun 2026 00:00:00 GMT

CockroachDB gives you a resilient, distributed SQL database that survives node failures and scales horizontally across regions — but the built-in console only takes you so far. As your product or data team grows, you need dashboards, shared reports, and structured data access without writing ad-hoc SQL for every question. Engineering teams often start by querying CockroachDB directly through the CLI or using the DB Console for basic monitoring. Eventually that workflow breaks down. You end up gatekeeping data access, writing throwaway queries for stakeholders, and building one-off scripts to export metrics. Without a proper reporting layer, your data team becomes a bottleneck rather than an asset. **CockroachDB reporting tools** solve this by connecting directly to your database and giving your team clean, controlled access to dashboards, charts, and scheduled reports — without rebuilding analytics from scratch. In this article, we cover the most reliable options worth considering in 2026. --- ## What are CockroachDB reporting tools? **CockroachDB reporting tools** are applications that connect to your CockroachDB cluster and let you build dashboards, run queries, and share data insights without writing SQL for every request. They sit on top of your existing database and provide a visual interface that works for both technical users and those who prefer not to touch the query editor. Because CockroachDB is wire-compatible with PostgreSQL, most tools that support Postgres can connect to CockroachDB with minimal configuration. This makes the ecosystem of available reporting tools quite broad — you get the resilience and distributed scale of CockroachDB while benefiting from the mature Postgres tooling landscape. Queries run directly against your live schema, no pipelines or data duplication required. --- ## How to choose a CockroachDB reporting tool The right CockroachDB reporting tool depends on what problem you're actually trying to solve. A startup team tracking product metrics has very different needs from an ops team monitoring SLAs across a multi-region cluster. Start by identifying your primary goal. ### 1. If your goal is internal reporting for your team Pick a tool with strong SQL support, an intuitive dashboard builder, and simple sharing. You want your team to answer data questions quickly without relying on engineers for every request. ### 2. If your goal is customer-facing analytics Look for embedding, white-label controls, and row-level filtering. Your customers should see dashboards that feel like a native part of your product, not a third-party tool bolted on. ### 3. If your goal is replacing an existing analytics stack Prioritize flexible permissions, reusable query definitions, scheduled exports, and scalable embedding. These features reduce duplication and help standardize how your organization defines and shares metrics. ### 4. If your goal is minimal engineering involvement Choose a tool that non-technical teammates can use independently. Visual query builders, AI-assisted SQL, and self-serve filters reduce the number of reporting requests that land in engineering backlogs. Ultimately, the right CockroachDB reporting tool depends on whether you need speed, customer experience, operational visibility, or long-term scalability. --- ## Key features to look for in CockroachDB reporting tools Not every reporting tool is equally suited to CockroachDB's distributed architecture. Here are the core features to evaluate when comparing your options. ### 1. Native PostgreSQL compatibility CockroachDB uses the PostgreSQL wire protocol, so your reporting tool should connect the same way it would to a Postgres database. This means: 1. No specialized drivers required 2. No ETL pipelines or schema duplication 3. Queries run directly on your live CockroachDB cluster 4. Permissions stay consistent with your existing setup Avoid tools that require you to mirror or export your CockroachDB data before you can build reports. ### 2. Ease of dashboard building Most CockroachDB teams don't have a dedicated analytics department, so reporting tools need to be accessible. Look for: 1. Visual query building for non-technical users 2. [AI text-to-SQL support](https://www.draxlr.com/blogs/ai-sql-tool/) 3. Reusable chart definitions 4. Filters, grouping, and drilldowns 5. Clear layout controls 6. [Easy dashboard sharing](https://www.draxlr.com/blogs/create-a-public-dashboard-url-for-your-sql-database/) The goal is that any member of your team should be able to answer a basic data question without filing a request with engineering. ### 3. Embeddability (for customer-facing dashboards) If you plan to [embed analytics](https://www.draxlr.com/blogs/best-embedded-analytics-tools/) inside your product, look for: 1. Secure embed tokens 2. Row-level filtering for multi-tenant architectures 3. White-label styling options Dashboards embedded inside your app should feel native, not like a third-party iframe. ### 4. API access for automation Teams running on CockroachDB often automate workflows like scheduled reports, alerting, and dashboard generation. A reporting tool with API access gives you: 1. Programmatic control over dashboards and queries 2. Integration with your existing automation stack 3. Scheduled delivery via email or Slack 4. Flexibility as your reporting needs evolve ### 5. Data security and access control CockroachDB is often used for multi-region, production-critical applications. Your reporting tool needs to match that standard. Look for: 1. No data copying or exporting to third-party servers 2. Role-based access control 3. Secure embedding with user-level filtering 4. [Self-hosting options](https://www.draxlr.com/blogs/ensuring-seamless-adoption-of-self-service-bi/) if required by your compliance posture ### 6. Documentation, support, and reliability Good documentation and responsive support matter, especially when you're integrating a reporting layer into a production-critical CockroachDB deployment. Pick a tool with clear setup docs, an active community or support channel, and a track record of stability. --- ## The best CockroachDB reporting tools to consider in 2026 Here's a breakdown of the top CockroachDB reporting tools worth exploring in 2026 and beyond. ### 1. Draxlr: Best CockroachDB reporting tool for dashboards, team insights, and embedded analytics ![Draxlr — CockroachDB reporting tool](/images/blogs/_tools/draxlr.png) #### Best for SaaS teams and data teams running on CockroachDB who want a fast way to build dashboards, explore data, and deliver embedded analytics without adding a complex BI stack. Connect your Database [Draxlr](https://www.draxlr.com/) connects directly to your CockroachDB cluster using the standard PostgreSQL connection string and gives your team a clean interface for building reports, running SQL, and sharing dashboards — without any infrastructure overhead. Because CockroachDB speaks the PostgreSQL wire protocol, Draxlr connects seamlessly without any special configuration. For technical users, Draxlr offers full SQL control, virtual columns, filters, drill-through navigation, and database-aware autocomplete. For non-technical teammates, the visual query builder and AI text-to-SQL make it easy to explore data without writing a single query. Both modes work against your live CockroachDB schema. If you need customer-facing analytics, Draxlr supports [embedded dashboards](https://www.draxlr.com/blogs/how-to-build-dashboards-from-cockroach-db-data/) with row-level filtering, secure embed tokens, white-label controls, and React and Vue SDKs that integrate naturally inside your product. Teams can also automate reporting with scheduled email delivery, Slack alerts, and CSV or Excel exports. Draxlr runs directly on your existing CockroachDB infrastructure — no pipelines, warehouses, or data modeling required. #### Key features 1. Connects directly to CockroachDB via PostgreSQL wire protocol — no ETL or extra setup 2. Visual Query Builder, full SQL editor, and AI Chat for text-to-SQL 3. Dashboard builder with filters, drill-through, and virtual columns 4. Embedded dashboards with secure tokens, row-level filters, and white-label controls 5. React and Vue SDKs for in-product analytics 6. Export options (CSV, Excel, PDF) and scheduled reports 7. Team-level permissions for controlled access to production data 8. Supports both internal reporting and customer-facing analytics from the same workspace #### What users say about Draxlr > Draxlr offers a clean, modern interface and makes it incredibly easy to build dashboards — even for non-technical users. I especially like the visual quality and flexibility of the available graph types. It's intuitive, fast to set up, and has excellent usability from the start. The ability to securely share dashboards via link is a game-changer for our client reporting. — [Review from a SaaS Founder on G2](https://www.g2.com/products/draxlr/reviews/draxlr-review-11185807) #### Pricing Draxlr offers multiple plans depending on team size, embedding needs, and data volume. Pricing starts at **$75/month** --- ### 2. Grafana: Best for infrastructure and operational monitoring on distributed CockroachDB clusters ![Grafana — CockroachDB reporting tool](/images/blogs/_tools/grafana.png) #### Best for Engineering and DevOps teams that need to monitor CockroachDB cluster health, query performance, and infrastructure metrics alongside application observability data. [Grafana](https://grafana.com/) has official, first-party support for CockroachDB through a dedicated enterprise data source plugin and pre-built dashboards for cluster monitoring. Grafana Labs released the CockroachDB Enterprise data source in 2024, enabling secure and seamless connectivity directly to CockroachDB's distributed SQL engine. For teams already using Grafana for observability, this makes it a natural extension for database-level visibility. The pre-built CockroachDB dashboards cover runtime metrics (node uptime, memory, CPU), storage availability, SQL query and transaction throughput, and replica information. CockroachDB also exposes a native Prometheus metrics endpoint, which makes Grafana-based monitoring straightforward to set up and maintain for teams managing multi-node clusters. Where Grafana is less well-suited is business-level reporting — custom dashboards, non-technical user access, and customer-facing analytics are not its strengths. It excels at operational visibility rather than product analytics. #### Key features 1. Official CockroachDB enterprise data source plugin with native connectivity 2. Pre-built dashboards for CockroachDB runtime, storage, SQL, and replica monitoring 3. Prometheus metrics endpoint integration for automated alerting #### What users say about Grafana > What I like most about Grafana Labs is its flexibility and strong visualization capabilities. Grafana makes it straightforward to create interactive, real-time dashboards by pulling data from multiple sources such as Prometheus, Elasticsearch, and various cloud platforms. — Review on G2 #### Pricing Grafana Cloud offers a free tier. The Pro plan starts at **$19/month** base plus usage-based pricing. Enterprise plans start at approximately $25,000/year. --- ### 3. Metabase: Best for self-serve business intelligence on CockroachDB data ![Metabase — CockroachDB reporting tool](/images/blogs/_tools/metabase.png) #### Best for Product and operations teams that want a user-friendly BI interface where non-technical users can explore CockroachDB data without SQL. [Metabase](https://www.metabase.com/) connects to CockroachDB using the PostgreSQL driver — since CockroachDB is wire-compatible with PostgreSQL, Metabase treats it like a Postgres database. This gives teams access to Metabase's full feature set including the visual query builder, question-based dashboards, and automated pulses. Metabase has a large user community and extensive documentation, which simplifies adoption. Metabase is particularly well-suited for teams that want business stakeholders — sales, marketing, support — to explore data independently. The no-SQL interface makes it accessible, and the dashboard sharing and embedding features make it easy to distribute insights. For more complex analytical workflows or customer-facing embedding, Metabase Pro and Enterprise tiers include advanced permissions and embedding controls. #### Key features 1. Visual query builder with no SQL required for basic reporting 2. Connects to CockroachDB via PostgreSQL driver with no extra configuration 3. Automated report delivery via email and Slack (pulse feature) #### What users say about Metabase > Metabase has one of the most intuitive interfaces I've used for BI. Even team members with no SQL background can build dashboards and answer their own data questions in minutes. — Review on G2 #### Pricing Metabase Open Source is free (self-hosted). Metabase Starter starts at **$100/month** (includes 5 users). Pro starts at **$575/month** (10 users). Enterprise plans are available. --- ### 4. Apache Superset: Best open-source BI for CockroachDB with engineering support ![Apache Superset — CockroachDB reporting tool](/images/blogs/_tools/apache-superset.png) #### Best for Companies that want an open-source BI platform, have internal engineering capacity, and prefer full control over their analytics infrastructure. [Apache Superset](https://superset.apache.org/) officially lists CockroachDB as a supported database. You connect using a CockroachDB-specific SQLAlchemy URI (`cockroachdb://`), which requires installing the `sqlalchemy-cockroachdb` package. Once connected, Superset provides a full SQL IDE, an interactive chart builder, and a broad library of visualizations — all running against your live CockroachDB cluster. Superset's open-source nature is its greatest strength and also its limitation. It's free and fully customizable, but deploying, securing, and maintaining your own Superset instance requires meaningful engineering time. For teams with that capacity, Superset offers exceptional flexibility — including support for semantic layers, role-based access control, and cross-database queries. For teams that want to move fast without managing infrastructure, a hosted solution may be a better fit. #### Key features 1. Officially supports CockroachDB via SQLAlchemy dialect 2. Full SQL IDE alongside a visual chart builder 3. Fully open-source, self-hosted, and extensible #### What users say about Apache Superset > We can combine data from different systems for thorough analysis because the platform supports an extensive amount of data sources, including distributed SQL databases. The open-source nature means we can customize it exactly to our needs. — Review on G2 #### Pricing Free and open-source. Hosting and maintenance costs vary by deployment size. --- ### 5. Power BI: Best for enterprise teams in the Microsoft ecosystem needing CockroachDB reports ![Power BI — CockroachDB reporting tool](/images/blogs/_tools/power-bi.png) #### Best for Enterprise organizations already invested in Microsoft tooling that want to bring CockroachDB data into their existing Power BI workflows. [Power BI](https://www.microsoft.com/en-us/power-platform/products/power-bi) connects to CockroachDB through the PostgreSQL connector — since CockroachDB is PostgreSQL wire-compatible, Power BI's native Postgres data source works without additional drivers. For teams that need deeper integration or real-time access, CData also offers a dedicated CockroachDB Power BI Connector. Once connected, users can leverage Power BI's full suite of DAX-based modeling, interactive reports, and enterprise sharing features. Power BI is a well-understood tool in enterprise environments, which simplifies stakeholder buy-in and reduces training overhead. That said, it is a heavy tool compared to the alternatives on this list — setup, data modeling, and maintenance require dedicated analyst capacity. It's not a good fit for small product teams or fast-moving SaaS environments that need quick access to CockroachDB data. #### Key features 1. Connects to CockroachDB via PostgreSQL connector with no extra setup 2. Advanced DAX modeling and a large library of visualizations 3. Deep integration with Microsoft 365, Azure, and Teams for enterprise sharing #### What users say about Power BI > What I like most about Power BI is how it turns complex data into something easy to understand. The ability to create interactive dashboards and dynamic reports is a game changer for teams that live inside the Microsoft ecosystem. — Review on G2 #### Pricing Power BI Desktop is free. Power BI Pro is **$10/user/month**. Power BI Premium starts at **$20/user/month** or a capacity-based plan from $4,995/month. --- ### 6. Redash: Best lightweight, open-source query and dashboard tool for CockroachDB ![Redash — CockroachDB reporting tool](/images/blogs/_tools/redash.png) #### Best for Small engineering teams that need a simple, SQL-driven dashboard tool and are comfortable self-hosting or using a managed provider. [Redash](https://redash.io/) connects to CockroachDB using the PostgreSQL data source option — because CockroachDB speaks the PostgreSQL wire protocol, Redash's built-in PostgreSQL connector works without additional configuration. Redash is designed around the SQL editor workflow: write a query, save it, visualize the result, and add it to a shared dashboard. It's lightweight, fast to set up, and gets out of the way of engineers who just want to share SQL results with their team. The hosted version of Redash was shut down, so you'll need to self-host or use a managed provider. For teams willing to manage their own instance, Redash is an easy way to give colleagues controlled access to CockroachDB data through shared dashboards and scheduled query refreshes without investing in a full BI platform. #### Key features 1. Connects to CockroachDB via the built-in PostgreSQL data source 2. SQL-first workflow with dashboard sharing and scheduled query refreshes 3. Fully open-source and self-hostable #### What users say about Redash > On Redash we store several data from our internal system and database that could be complicated to find with manual research or using other tools. Everything is in one single place and you can share the queries and dashboards with anyone. — Review on G2 #### Pricing Free and open-source (self-hosted). Managed hosting starts at approximately **$25–$40/month** for a small instance. Premium plans from third-party providers start at **$10/month**. --- ## Implementing your CockroachDB reporting tool effectively Choosing the right CockroachDB reporting tool is only the first step. Connecting to your cluster and building a few dashboards gets you started, but long-term value depends on how well you integrate reporting into your team's workflow. ### 1. Onboarding your team Invest in proper onboarding from the start. Whether your team includes developers, product managers, or support staff, everyone who will use dashboards should understand the tool, know which reports exist, and feel comfortable adding filters or adjusting views. A short onboarding session prevents bottlenecks and avoids situations where reporting still falls back on one engineer. ### 2. Start with a pilot Rather than rolling out dashboards company-wide immediately, start with one team or one use case. A focused pilot helps you validate that your CockroachDB queries return accurate results, your permissions are configured correctly, and the dashboards actually answer the questions stakeholders care about. It also reduces the risk of noisy, unused reports that undermine trust in your data. ### 3. Collect feedback continuously Reporting is a living part of your product, not a one-time setup. Actively collect feedback from the people using your dashboards — internal teams or customers — and treat it like product feedback. As your schema evolves and your business questions change, your dashboards should evolve alongside them. ### 4. Monitor performance and usage CockroachDB is designed to handle distributed, high-concurrency workloads, but poorly written reporting queries can still cause performance issues. Monitor which queries run most frequently, which dashboards get viewed regularly, and which reports are ignored. Optimize slow queries, retire dashboards that nobody opens, and use this data to improve the ROI of your reporting layer over time. --- ## Conclusion The CockroachDB reporting tool you choose determines how your team and your customers experience data. It shapes whether stakeholders can answer their own questions or depend on engineers for every insight. It determines whether your analytics layer is a drag on your infrastructure or a reliable part of your product. CockroachDB's PostgreSQL wire compatibility means you have a wide range of battle-tested reporting tools to choose from — from purpose-built dashboard tools to open-source BI platforms to enterprise suites. The right choice depends on your team's size, technical resources, and whether you need internal visibility, customer-facing dashboards, or operational monitoring. If you want a reporting layer that connects to CockroachDB without pipelines or extra infrastructure, [Draxlr](https://app.draxlr.com/register/) lets you ship dashboards quickly while keeping your stack simple. Whether your priority is team analytics, embedded customer dashboards, or scheduled reporting, it's worth exploring what fits your workflow. --- ## FAQs ### 1. What is a CockroachDB reporting tool? A CockroachDB reporting tool connects directly to your CockroachDB cluster and lets you build dashboards, charts, and reports without writing SQL for every request. These tools give teams safe, structured access to live data without requiring ETL pipelines or a separate analytics warehouse. ### 2. Can I connect standard Postgres reporting tools to CockroachDB? Yes. CockroachDB is wire-compatible with PostgreSQL, which means most tools that support Postgres — including Metabase, Redash, Superset, Power BI, and Draxlr — can connect to CockroachDB using standard PostgreSQL credentials and connection strings. ### 3. Does CockroachDB have built-in reporting? CockroachDB includes a DB Console with cluster monitoring dashboards for nodes, SQL performance, and storage. However, the DB Console is designed for operational visibility rather than business reporting or customer-facing dashboards. ### 4. Is it safe to connect a reporting tool directly to a CockroachDB cluster? Yes, provided the tool uses secure connections, respects your database-level permissions, and supports user-scoped access controls. Use read-only credentials for your reporting connection and avoid tools that require copying or exporting your data to external servers. ### 5. Can non-technical team members use CockroachDB reporting tools? Yes. Tools like Metabase and Draxlr offer visual query builders, saved reports, and interactive filters that let product, support, or operations teams explore CockroachDB data without writing SQL. ### 6. Do I need a data warehouse to report on CockroachDB? Not in most cases. CockroachDB's distributed SQL architecture handles complex queries well, and most reporting tools connect directly to it without requiring a separate warehouse. A warehouse becomes relevant only for very large-scale analytical workloads or when you need to combine CockroachDB data with other sources.

Best YugabyteDB Reporting Tools

hello@draxlr.com (Draxlr) — Wed, 17 Jun 2026 00:00:00 GMT

YugabyteDB gives you the resilience of a distributed SQL database with the full familiarity of PostgreSQL — and that combination has made it a go-to choice for teams building mission-critical applications at scale. But as YugabyteDB clusters grow and power more of your business logic, the absence of a proper reporting layer starts to show. Running ad-hoc queries in a terminal, sharing CSV exports over Slack, or building one-off dashboards in spreadsheets doesn't scale alongside your database. Engineering teams often underestimate how much work goes into building a proper internal analytics layer. Normalizing queries, managing permissions, creating visualizations, and keeping dashboards accurate as schemas evolve can easily consume sprint after sprint. What begins as a simple dashboard request quickly turns into a maintenance burden. **YugabyteDB reporting tools** solve this problem by connecting directly to your cluster and giving your team — technical and nontechnical alike — a clean interface for exploring data, building dashboards, and sharing insights. In this guide, we break down the best options worth considering in 2026. --- ## What are YugabyteDB reporting tools? **YugabyteDB reporting tools** are applications that connect directly to your YugabyteDB cluster and allow teams to build dashboards, run queries, and generate visual reports without writing SQL for every question. Because YugabyteDB's YSQL API is fully PostgreSQL-compatible, most modern BI and reporting tools can connect to it using a standard Postgres driver — no custom connectors or ETL pipelines required. These tools bridge the gap between your distributed SQL database and the people who need to understand what's inside it. Whether the goal is internal team reporting, customer-facing embedded analytics, or operational monitoring, a good YugabyteDB reporting tool eliminates the need to build analytics infrastructure from scratch. --- ## How to choose a YugabyteDB reporting tool The right YugabyteDB reporting tool depends on your goals, your team's technical capacity, and how you plan to use analytics day to day. A startup that needs internal visibility into key product metrics has very different requirements from a B2B SaaS company that wants to embed analytics directly inside its product. Start by identifying your primary goal. ### 1. If your goal is internal reporting for your team Look for tools with strong SQL support, an intuitive dashboard builder, and simple permission-based sharing. You want visibility into your YugabyteDB data without adding another complex system to maintain or requiring your engineering team to build a custom analytics layer. ### 2. If your goal is customer-facing analytics Prioritize tools with secure embedding, white-label controls, and row-level filtering. Your customers should be able to see their own data inside your product — not a third-party dashboard bolted on with visible branding. ### 3. If your goal is replacing an existing analytics stack Focus on tools with flexible permissions, reusable query definitions, scheduling, and scalable embedding. These capabilities give you a clean migration path and reduce the surface area of things you need to maintain going forward. ### 4. If your goal is minimal engineering involvement Choose a tool that lets product managers, support teams, and operations staff explore YugabyteDB data on their own. Visual query builders and AI-assisted SQL reduce the number of ad-hoc requests that land in your engineering backlog. --- ## Key features to look for in YugabyteDB reporting tools Not every reporting tool is built the same, and the options that sound comprehensive in a feature list may still fall short in practice. Here are the core capabilities to evaluate when comparing YugabyteDB reporting tools. ### 1. Native PostgreSQL compatibility Since YugabyteDB's YSQL API is PostgreSQL-compatible, your reporting tool should connect to it the same way it connects to Postgres — no custom drivers, no schema duplication, no ETL pipelines. Tools that require proprietary connectors or data movement will add unnecessary complexity to an already distributed setup. ### 2. Ease of dashboard building Most teams using YugabyteDB don't have a dedicated data team. Reporting needs to be accessible to developers, product managers, and analysts without requiring each of them to write raw SQL. Look for: 1. Visual query builders 2. [AI text-to-SQL](https://www.draxlr.com/blogs/ai-sql-tool/) 3. Reusable chart components 4. Dashboard filters and drill-through 5. [Simple sharing and permissions](https://www.draxlr.com/blogs/create-a-public-dashboard-url-for-your-sql-database/) ### 3. Embeddability for customer-facing dashboards If you plan to [embed analytics](https://www.draxlr.com/blogs/best-embedded-analytics-tools/) inside your product, make sure the tool offers: 1. Secure, stateless embed tokens 2. Row-level filtering for multi-tenant applications 3. White-label controls so dashboards feel native to your product ### 4. API access for automation Teams running YugabyteDB often automate reporting workflows — scheduled exports, Slack alerts, or embedding logic tied to their auth layer. An API-first reporting tool gives you programmatic control and the flexibility to integrate analytics with the rest of your stack. ### 5. Data security and access control Your YugabyteDB reporting tool must respect the same security expectations as your application. Look for: 1. No data duplication outside your cluster 2. User-scoped dashboards and data access 3. Secure embedding with token-based authorization 4. Role-based permission controls 5. [Self-hosting options](https://www.draxlr.com/blogs/ensuring-seamless-adoption-of-self-service-bi/) for sensitive environments ### 6. Documentation, support, and reliability Reporting is often deployed under pressure — late in a product cycle or when customers start asking for data. Good documentation, active communities, and responsive support teams make all the difference when you're stuck and need answers fast. --- ## The best YugabyteDB reporting tools to consider in 2026 Here is a breakdown of the top YugabyteDB reporting tools worth exploring in 2026 and beyond. ### 1. Draxlr: Best YugabyteDB reporting tool for dashboards, team insights, and embedded analytics ![Draxlr — YugabyteDB reporting tool](/images/blogs/_tools/draxlr.png) #### Best for SaaS and product teams running YugabyteDB who want a fast, intuitive way to build dashboards, explore data, and deliver embedded analytics without introducing a heavyweight BI stack. Connect your Database [Draxlr](https://www.draxlr.com/) connects to YugabyteDB using its native PostgreSQL-compatible YSQL interface, giving teams a clean interface for creating reports, running SQL, and building dashboards without any setup overhead. It's designed for modern SaaS products where engineering resources are limited and the goal is to ship reporting fast — whether for internal visibility or customer-facing analytics. For technical users, Draxlr offers full SQL control, virtual columns, filters, drill-through functionality, and database-aware autocomplete. For nontechnical teammates, the visual query builder and AI text-to-SQL make data exploration accessible without requiring anyone to touch the SQL editor. Since YugabyteDB is distributed across nodes, Draxlr's direct connection model means queries run against your actual live schema with no data replication or modeling layer required. If your product includes customer analytics, Draxlr supports secure embedded dashboards with row-level filtering, white-label options, and React/Vue components that integrate naturally into your app. Teams can also automate reporting with scheduled emails, Slack alerts, and CSV/Excel exports. #### Key features 1. Connects directly to YugabyteDB via the PostgreSQL-compatible YSQL API — no ETL or data movement required 2. Visual Query Builder, full SQL editor, and AI Chat for text-to-SQL 3. Dashboard builder with filters, drill-through, and virtual columns 4. Embedded dashboards with secure tokens, row-level filters, and white-label controls 5. React and Vue SDKs for in-product analytics 6. Export options (CSV, Excel, PDF) and scheduled reports 7. Team-level permissions for safe access to production data 8. Supports both internal reporting and customer-facing analytics from the same workspace #### What users say about Draxlr > Draxlr offers a clean, modern interface and makes it incredibly easy to build dashboards — even for non-technical users. I especially like the visual quality and flexibility of the available graph types. It's intuitive, fast to set up, and has excellent usability from the start. The ability to securely share dashboards via link is a game-changer for our client reporting. — [Review from a SaaS Founder on G2](https://www.g2.com/products/draxlr/reviews/draxlr-review-11185807) #### Pricing Draxlr offers multiple plans depending on team size, embedding needs, and data volume. Pricing starts at **$75/month** --- ### 2. Metabase: Best for self-service analytics and non-technical teams ![Metabase — YugabyteDB reporting tool](/images/blogs/_tools/metabase.png) #### Best for Teams that want an approachable, self-service analytics layer on top of YugabyteDB, particularly when non-developers need to explore data without writing SQL. [Metabase](https://www.metabase.com/) is one of the most widely adopted open-source BI tools available, and it has [official documentation for YugabyteDB integration](https://docs.yugabyte.com/stable/integrations/tools/metabase/). Because YugabyteDB is PostgreSQL-compatible, Metabase connects to it using the standard Postgres driver — select PostgreSQL as your database type, enter your YugabyteDB endpoint and credentials, and Metabase treats the distributed cluster just like a standard Postgres instance. Metabase's visual question builder lets non-technical users create reports by clicking through tables, filters, and aggregations without writing a single line of SQL. For more advanced users, native SQL queries are fully supported. It's a solid option when your primary goal is making YugabyteDB data accessible to the broader organization without a steep learning curve. However, its embedding capabilities are limited to paid tiers, and at scale the per-user billing model can become expensive. #### Key features 1. Connects to YugabyteDB via the standard PostgreSQL driver — officially documented 2. Visual question builder for non-technical users 3. Native SQL editor with parameterized queries #### What users say about Metabase > Great user experience, great user management, easy to use and everybody learns quickly how to find the information they need. #### Pricing Open source (free to self-host). Cloud Starter plan starts at $100/month for up to 5 users. Pro plan starts at $575/month. --- ### 3. Grafana: Best for operational monitoring and time-series dashboards ![Grafana — YugabyteDB reporting tool](/images/blogs/_tools/grafana.png) #### Best for Engineering and DevOps teams that need real-time operational dashboards, cluster health monitoring, and time-series visualization on top of YugabyteDB. [Grafana](https://grafana.com/) is one of the most popular open-source observability platforms, and it has a [dedicated YugabyteDB datasource plugin](https://grafana.com/grafana/plugins/grafana-yugabyte-datasource/) available through Grafana Labs. This plugin allows teams to connect Grafana directly to YugabyteDB instances, visualize query performance, track cluster health, and build operational dashboards on top of live YSQL data. YugabyteDB also provides pre-built Grafana dashboard templates that break down metrics by API type (YSQL, YCQL) and method (Insert, Select, Update), making it fast to get an observability layer stood up. Grafana excels at time-series data, infrastructure metrics, and Prometheus-compatible monitoring workflows. For teams running YugabyteDB in Kubernetes, it integrates naturally with the Prometheus Operator and standard Helm-based deployments. That said, Grafana is primarily an operational tool — it's not designed for business dashboards, customer-facing analytics, or self-service data exploration by non-technical users. #### Key features 1. Dedicated YugabyteDB datasource plugin with official support from Grafana Labs 2. Pre-built YugabyteDB dashboard templates for cluster health and query performance 3. Prometheus and Kubernetes integration for infrastructure monitoring #### What users say about Grafana > Grafana has made it incredibly easy to visualize complex operational data. The plugin ecosystem is excellent and the time-series dashboards are unmatched for infrastructure teams. #### Pricing Free and open-source (self-hosted). Grafana Cloud Free tier available. Cloud Pro starts at $19/month base plus usage-based pricing. --- ### 4. Apache Superset: Best open-source BI for teams with engineering capacity ![Apache Superset — YugabyteDB reporting tool](/images/blogs/_tools/apache-superset.png) #### Best for Organizations that want a powerful open-source BI platform with full control over their analytics infrastructure and have engineering capacity to host and maintain it. [Apache Superset](https://superset.apache.org/) has [official YugabyteDB support documented](https://superset.apache.org/user-docs/databases/supported/yugabytedb/) and [detailed integration guides](https://docs.yugabyte.com/stable/integrations/tools/superset/) in the YugabyteDB documentation. Superset connects to YugabyteDB using the standard PostgreSQL driver (psycopg2, which ships with Superset), making the setup straightforward: select PostgreSQL from the database menu, enter your YugabyteDB endpoint, and the connection is established. YugabyteDB even supports being used as a Superset metadata store from version 2.19 onwards. Superset offers a full SQL IDE, an interactive dashboard builder, and a broad library of chart types. It's a strong fit for data teams that want open-source flexibility, complete ownership over their analytics infrastructure, and don't mind the operational overhead of self-hosting. For early-stage teams or those without dedicated engineering support for tooling, the maintenance burden can outweigh the benefits. #### Key features 1. Official YugabyteDB support via PostgreSQL-compatible connection — documented by both projects 2. SQL IDE and visual dashboard builder with rich chart library 3. Fully open-source with active community and no licensing costs #### What users say about Apache Superset > We can combine data from different systems for thorough analysis because the platform supports an extensive amount of data sources, including distributed databases like YugabyteDB via its Postgres-compatible interface. #### Pricing Free and open-source. Operational costs depend on hosting and maintenance. --- ### 5. Tableau: Best for advanced visualization in analyst-driven organizations ![Tableau — YugabyteDB reporting tool](/images/blogs/_tools/tableau.png) #### Best for Enterprise teams with dedicated BI analysts who need sophisticated, custom visualizations and have existing Tableau infrastructure. [Tableau](https://www.tableau.com/) is one of the industry's most capable visualization platforms, and it connects to YugabyteDB using the standard PostgreSQL JDBC driver. Since YugabyteDB's YSQL API is fully PostgreSQL-compatible, teams can configure Tableau to point at their YugabyteDB endpoint (typically at port 5433) using a standard JDBC connection string and access the full power of Tableau's visualization engine against their distributed SQL cluster. Tableau excels at complex, multi-layered visualizations, narrative dashboards, and deep analytical exploration. However, it requires meaningful setup, data modeling work via Tableau Prep, and ongoing analyst resources to get value from. For fast-moving product teams or smaller SaaS companies, Tableau's weight and cost often make it a poor fit. It's best suited for organizations that already operate within a BI-analyst workflow and have an existing Tableau deployment. #### Key features 1. Connects to YugabyteDB via the PostgreSQL JDBC driver — no custom connector required 2. Advanced, publication-quality visualizations and chart types 3. Tableau Prep for data modeling and preparation #### What users say about Tableau > What I like best is Tableau's design philosophy — it emphasizes data exploration and visual storytelling over rigid reporting. For analyst-driven teams it remains one of the best tools available. #### Pricing Premium pricing tailored toward enterprise and team deployments. Contact sales for current pricing. --- ### 6. Power BI: Best for teams embedded in the Microsoft ecosystem ![Power BI — YugabyteDB reporting tool](/images/blogs/_tools/power-bi.png) #### Best for Organizations already using Microsoft tools like Azure, Teams, or Office 365 that want to add BI dashboards connected to YugabyteDB. [Power BI](https://www.microsoft.com/en-us/power-platform/products/power-bi) is Microsoft's flagship BI and reporting platform, and it connects to YugabyteDB through the PostgreSQL connector available in Power BI Desktop. Teams configure the connection by selecting the PostgreSQL connector, pointing it at the YugabyteDB endpoint, and using standard Postgres credentials — the distributed nature of the cluster is transparent at the connection layer. Power BI brings advanced data modeling with DAX, a large library of visualizations, and tight integration with Azure services and the broader Microsoft ecosystem. It's a strong option when your organization is already standardized on Microsoft tools and wants a unified analytics layer. The Power BI Desktop tooling can be complex for smaller teams, and enterprise-grade sharing and governance features require Power BI Premium. For SaaS teams or companies without existing Microsoft infrastructure, the overhead of adopting Power BI alongside YugabyteDB is usually harder to justify than lighter-weight alternatives. #### Key features 1. Connects to YugabyteDB via the PostgreSQL connector in Power BI Desktop 2. Advanced modeling with DAX and a large visualization library 3. Tight integration with Azure, Microsoft 365, and Teams #### What users say about Power BI > What I like most about Power BI is how it turns complex data into something easy to understand. The ability to create interactive dashboards and dynamic reports is a game changer for our organization. #### Pricing Power BI Desktop is free. Power BI Pro starts at $10/user/month. Power BI Premium Per User at $20/user/month. Premium Per Capacity pricing available for enterprise deployments. --- ## Implementing your YugabyteDB reporting tool effectively Choosing the right tool is only the first step. Getting real value from a YugabyteDB reporting layer requires a thoughtful implementation approach — one that respects the distributed nature of your database and the different ways your team will interact with data. ### 1. Onboard your team properly A reporting tool that only developers know how to use defeats the purpose. Invest time in onboarding product managers, support staff, and analysts alongside your engineering team. A focused two-hour session that covers connecting to YugabyteDB, creating a basic dashboard, and sharing it correctly prevents weeks of misconfigurations and bottlenecks later. ### 2. Start with a pilot Rather than rolling out dashboards to the entire organization at once, start with a small internal group. This lets you validate your metric definitions, confirm that queries perform well against your YugabyteDB cluster, and refine the dashboard layout before it becomes the source of truth for a wider audience. Distributed databases like YugabyteDB can surface subtle query performance differences depending on your topology, so piloting before full rollout is especially valuable. ### 3. Collect feedback continuously Reporting is not a one-time build. Once dashboards are live, actively gather feedback from the people using them. Which metrics are being ignored? Which charts aren't loading fast enough? As your product evolves and your YugabyteDB schema changes, your reporting layer needs to stay in sync. Treating feedback as a continuous loop — not a one-time configuration — keeps your dashboards accurate and useful. ### 4. Monitor performance and usage Track which dashboards are actually being opened, which queries run most frequently, and whether any reports are routinely ignored. On YugabyteDB, queries that run efficiently on a single-node Postgres might behave differently across distributed nodes — understanding which queries are slow helps you optimize both your reporting layer and your database schema over time. --- ## Conclusion YugabyteDB is built for demanding, distributed workloads — and the reporting layer you choose should be able to keep up. Whether your team needs internal visibility into product metrics, operational monitoring across nodes, or customer-facing embedded analytics, there's a tool in this list that fits your requirements. If you want a reporting layer that works naturally with YugabyteDB's PostgreSQL-compatible interface without ETL pipelines, infrastructure overhead, or modeling complexity, [Draxlr](https://app.draxlr.com/register/) connects directly to your cluster and helps you ship dashboards quickly. Whether you're building internal reports for your team or embedded analytics for your customers, Draxlr is designed to work alongside your YugabyteDB setup without adding unnecessary complexity to your stack. --- ## FAQs ### 1. What is a YugabyteDB reporting tool? A YugabyteDB reporting tool connects directly to your YugabyteDB cluster and lets you build dashboards, charts, and reports without writing SQL for every request. Because YugabyteDB is PostgreSQL-compatible, most tools connect using a standard Postgres driver — no custom connectors or data pipelines required. ### 2. Can I connect standard PostgreSQL BI tools to YugabyteDB? Yes. YugabyteDB's YSQL API is fully PostgreSQL-compatible, which means any reporting tool that supports a standard Postgres connection — including Metabase, Apache Superset, Grafana, Tableau, and Power BI — can connect to YugabyteDB using the same driver and credentials format. ### 3. Does YugabyteDB have built-in reporting or dashboards? YugabyteDB includes a cluster management UI and Prometheus-compatible metrics endpoints, but these are focused on operational monitoring rather than business reporting. For product analytics, internal dashboards, or customer-facing data exploration, you'll need a dedicated reporting tool. ### 4. Is it safe to connect a reporting tool directly to YugabyteDB? Yes, as long as the reporting tool respects PostgreSQL permissions, uses encrypted connections, and supports user-level access controls. Avoid tools that require exporting or duplicating your data outside the cluster. YugabyteDB's Postgres-compatible role system gives you precise control over what each reporting tool user can access. ### 5. Can non-technical team members use YugabyteDB reporting tools? Yes. Tools like Draxlr and Metabase include visual query builders, saved reports, dashboard filters, and easy sharing so non-technical users can explore YugabyteDB data without writing SQL. The key is choosing a tool that balances power for technical users with accessibility for the rest of your team. ### 6. Do I need a data warehouse for reporting on YugabyteDB? Not typically. YugabyteDB is a distributed SQL database capable of handling analytical queries directly. Most reporting tools connect to it without requiring a separate warehouse layer. Unless you're running extremely complex OLAP queries across massive datasets, a direct connection is faster to set up, easier to maintain, and sufficient for most reporting use cases.

Best PlanetScale Reporting Tools

hello@draxlr.com (Draxlr) — Tue, 16 Jun 2026 00:00:00 GMT

PlanetScale gives you a serverless MySQL-compatible database that scales without limits, but the moment your product starts generating real data, the built-in tooling stops being enough. Teams end up writing one-off queries, copy-pasting results into spreadsheets, or spending engineering time on custom reporting that should be much simpler. As products grow, the gap between your database and your team's understanding of what's happening inside it widens. Product managers need conversion metrics, support teams need order history, founders need MRR trends — and without dedicated **PlanetScale reporting tools**, all of those requests end up routed through an engineer with database access. The right reporting tool connects directly to your PlanetScale database, gives your team safe, controlled access to the data they need, and lets you build dashboards without writing ad-hoc SQL for every question. In this article, we break down the most reliable PlanetScale reporting tools to consider in 2026. --- ## What are PlanetScale reporting tools? **PlanetScale reporting tools** connect to your PlanetScale database and provide dashboards, charts, and shared reports without requiring direct database access for every query. Because PlanetScale is built on a MySQL-compatible foundation (powered by Vitess), most tools that support MySQL can connect to PlanetScale using standard credentials and SSL. These tools sit on top of your existing schema and let teams explore data, track key metrics, and share insights without introducing ETL pipelines or duplicated datasets. For SaaS teams who chose PlanetScale for its speed and scalability, reporting tools extend that investment by making the data inside it visible and actionable across the whole organization. --- ## How to choose a PlanetScale reporting tool Choosing the right PlanetScale reporting tool depends on what you're trying to accomplish. A startup tracking product KPIs internally has very different needs from a company embedding analytics into a customer-facing product. Start by identifying your primary goal. ### 1. If your goal is internal reporting for your team Pick a tool with solid SQL support, a visual dashboard builder, and easy sharing. Your team should be able to answer common questions — signups, churn, revenue, usage — without filing engineering requests. ### 2. If your goal is customer-facing analytics Look for embedding support, row-level data filtering for multi-tenant setups, and white-label options. Dashboards should feel native to your product, not like a third-party iframe dropped on top. ### 3. If your goal is replacing an existing analytics stack Prioritize tools with flexible permissions, scheduled reports, reusable query logic, and scalable embedding. These features let you consolidate your analytics layer rather than adding to it. ### 4. If your goal is minimal engineering involvement Choose a tool that empowers product, ops, or support teams to explore data independently. Visual query builders, filters, and AI text-to-SQL reduce the number of requests that land in engineering queues. --- ## Key features to look for in PlanetScale reporting tools A lot of tools claim to support MySQL and therefore PlanetScale, but their actual compatibility and usability varies. Here are the core features to evaluate when comparing PlanetScale reporting tools. ### 1. Native MySQL and PlanetScale compatibility PlanetScale is built on Vitess and has some differences from standard MySQL — particularly around foreign key constraints and certain DDL operations. Your reporting tool should connect over a standard MySQL connection with SSL support, and ideally have been tested against a live PlanetScale schema. Tools that connect cleanly without requiring data modeling or schema workarounds are the strongest fit. ### 2. Ease of dashboard building Most PlanetScale-backed teams are product-focused and moving fast. Reporting needs to be accessible to the whole team, not just whoever has database credentials. Look for: 1. Visual query building 2. [AI text-to-SQL](https://www.draxlr.com/blogs/ai-sql-tool/) 3. Reusable charts and saved queries 4. Filters and drill-through navigation 5. [Simple dashboard sharing](https://www.draxlr.com/blogs/create-a-public-dashboard-url-for-your-sql-database/) ### 3. Embeddability (if you need customer-facing dashboards) If you're building a SaaS product on PlanetScale and want to [embed analytics](https://www.draxlr.com/blogs/best-embedded-analytics-tools/) for your customers, you'll need secure embed tokens, row-level filtering per tenant, and white-label styling. Embedded dashboards should look like part of your product, not a separate tool. ### 4. API access for automation Teams building on PlanetScale often need to automate workflows: scheduled reports, Slack alerts when a metric crosses a threshold, or programmatic embedding for customer dashboards. An API-first tool gives you: 1. Programmatic dashboard and query management 2. Scheduled report delivery 3. Easier integration with your existing auth and alerting systems ### 5. Data security and access control Your PlanetScale database holds production data. Your reporting tool needs to respect that. Look for: 1. Read-only connection options 2. User-level permission controls 3. Secure embed tokens for customer-facing dashboards 4. No data copying or sync requirements 5. [Self-hosted options](https://www.draxlr.com/blogs/ensuring-seamless-adoption-of-self-service-bi/) for teams with strict data residency requirements ### 6. Documentation, support, and reliability Reporting is typically built under deadline pressure. Good documentation, active support channels, and a stable UI will save your team hours of frustration when things need to move fast. --- ## The best PlanetScale reporting tools to consider in 2026 Here's a breakdown of the top PlanetScale reporting tools worth exploring in 2026 and beyond. ### 1. Draxlr: Best PlanetScale reporting tool for dashboards, team insights, and embedded analytics ![Draxlr — PlanetScale reporting tool](/images/blogs/_tools/draxlr.png) #### Best for SaaS teams running on PlanetScale who want a fast, straightforward way to build dashboards, explore data, and deliver embedded analytics without adding a complicated BI layer. Connect your Database [Draxlr](https://www.draxlr.com/) connects directly to your PlanetScale database and gives teams a clean interface for creating reports, running SQL, and building dashboards without setup overhead. It was built specifically for modern SaaS products, making it one of the first reporting tools with full support for PlanetScale's serverless MySQL architecture. For technical users, Draxlr provides full SQL control, virtual columns, filters, drill-through features, and database-aware autocomplete. For nontechnical teammates, the visual query builder and AI text-to-SQL make data exploration accessible without needing to touch the editor. You can [build dashboards and set up Slack alerts](https://www.draxlr.com/blogs/build-dashboards-and-setup-slack-alerts-on-your-planetScale-database/) on your PlanetScale data without writing a single line of code. If your product includes customer analytics, Draxlr supports secure embedded dashboards with row-level filtering, white-label controls, and React/Vue components that integrate naturally into your app. Teams can also automate reporting with scheduled emails, Slack alerts, and CSV/Excel exports — all without any ETL or pipeline work. #### Key features 1. Connects directly to PlanetScale with SSL — no ETL or data sync required 2. Visual Query Builder, full SQL editor, and AI Chat for text-to-SQL 3. Dashboard builder with filters, drill-through, and virtual columns 4. Embedded dashboards with secure tokens, row-level filters, and white-label controls 5. React and Vue SDKs for in-product analytics 6. Scheduled reports, Slack alerts, and CSV/Excel/PDF exports 7. Team-level permissions for safe access to production data 8. Supports both internal reporting and customer-facing analytics from one workspace #### What users say about Draxlr > Draxlr offers a clean, modern interface and makes it incredibly easy to build dashboards — even for non-technical users. I especially like the visual quality and flexibility of the available graph types. It's intuitive, fast to set up, and has excellent usability from the start. The ability to securely share dashboards via link is a game-changer for our client reporting. — [Review from a SaaS Founder on G2](https://www.g2.com/products/draxlr/reviews/draxlr-review-11185807) #### Pricing Draxlr offers multiple plans depending on team size, embedding needs, and data volume. Pricing starts at **$75/month**. --- ### 2. Grafana: Best for operational and time-series dashboards on PlanetScale ![Grafana — PlanetScale reporting tool](/images/blogs/_tools/grafana.png) #### Best for Engineering and ops teams that want real-time observability dashboards and metric tracking directly from their PlanetScale MySQL data. [Grafana](https://grafana.com/) connects to PlanetScale through its native MySQL data source, making it one of the most technically capable options for teams that need time-series visualizations, alerting, and operational monitoring alongside their product data. Grafana is open-source with a hosted cloud option, and connecting it to PlanetScale requires configuring an SSL certificate — something that's well-documented and straightforward for engineering teams. Grafana excels at real-time metric panels, threshold-based alerts, and multi-source dashboards where you might combine PlanetScale data with infrastructure metrics. However, it's more engineering-oriented than BI-focused — nontechnical users typically need preconfigured dashboards to work with rather than building their own. #### Key features 1. Native MySQL data source with SSL support for PlanetScale connections 2. Rich library of panels, chart types, and visualization options 3. Alerting engine with notifications via Slack, email, PagerDuty, and more #### What users say about Grafana > What I like most about Grafana is the flexibility and clarity it offers for visualizing metrics. In our projects, we handle a lot of monitoring data, and Grafana makes it easy to turn raw data into meaningful, easy-to-read dashboards. The UI is clean, and we can customize panels to fit our exact requirements. #### Pricing Grafana OSS is free and self-hosted. Grafana Cloud offers a free tier, with paid plans starting at **$19/month** plus usage-based fees for larger data volumes. --- ### 3. Retool: Best for internal tools and admin panels on PlanetScale ![Retool — PlanetScale reporting tool](/images/blogs/_tools/retool.png) #### Best for Product and engineering teams that need to build internal dashboards, admin panels, and operational tools directly on top of their PlanetScale data. [Retool](https://retool.com/) connects to PlanetScale via a standard MySQL connection and is purpose-built for internal tool development. It provides a drag-and-drop UI builder on top of your database, letting teams create admin panels, approval workflows, order management dashboards, and other internal tools without starting from scratch. Retool has published detailed documentation on building admin panels specifically with PlanetScale, confirming its compatibility with PlanetScale's MySQL architecture. The tradeoff is that Retool is builder-focused rather than analyst-focused. It's ideal when you need a custom internal interface on your PlanetScale data, but if your goal is BI-style dashboards with charts, aggregations, and self-serve exploration, a dedicated reporting tool will serve you better. #### Key features 1. Direct MySQL connection to PlanetScale with UI components for tables, forms, and charts 2. Drag-and-drop interface builder with support for custom JavaScript logic 3. 70+ native data source integrations alongside PlanetScale MySQL #### What users say about Retool > The UI is intuitive, connecting to APIs or databases is fast, and I've used it to automate tasks, build dashboards, and link systems together that didn't talk to each other before. It significantly reduces the time required to build internal dashboards and admin panels. #### Pricing Retool offers a Free plan for up to 5 users. The Team plan starts at **$10/user/month**, with the Business plan at **$50/builder/month**. --- ### 4. Tableau: Best for enterprise-scale visualization on PlanetScale ![Tableau — PlanetScale reporting tool](/images/blogs/_tools/tableau.png) #### Best for Enterprise teams with dedicated BI analysts who need sophisticated visualizations and deep analytical exploration. [Tableau](https://www.tableau.com/) connects to PlanetScale via its MySQL connector, giving enterprise analytics teams access to Tableau's industry-leading visualization and exploration capabilities. With PlanetScale's MySQL-compatible interface, teams can build complex dashboards, run multi-dimensional analyses, and publish reports across large organizations using Tableau's well-established governance features. Tableau is one of the most powerful visualization tools available, but it's also one of the heaviest. It requires dedicated BI resources to set up, model, and maintain — making it a stronger fit for enterprise teams than for fast-moving SaaS products looking to ship dashboards quickly. #### Key features 1. MySQL connector compatible with PlanetScale's serverless architecture 2. Advanced visualization capabilities with drag-and-drop dashboard builder 3. Enterprise governance, workspace management, and publishing controls #### What users say about Tableau > What I like best is Tableau's design philosophy — it emphasizes data exploration and visual storytelling over rigid reporting. The depth of visualization options is unmatched. #### Pricing Tableau Cloud pricing starts at **$15/user/month** for Viewers, **$42/user/month** for Explorers, and **$75/user/month** for Creators, billed annually. --- ### 5. Apache Superset: Best open-source BI option for PlanetScale ![Apache Superset — PlanetScale reporting tool](/images/blogs/_tools/apache-superset.png) #### Best for Teams that want an open-source BI platform, have in-house engineering capacity, and want full control over their reporting infrastructure. [Apache Superset](https://superset.apache.org/) is a powerful open-source analytics platform that connects to PlanetScale via its SQLAlchemy MySQL connector. It supports SQL-based exploration, an interactive dashboard builder, and a wide range of visualizations — all without licensing costs. If your team is comfortable with self-hosting and maintenance, Superset gives you a capable reporting layer on top of your PlanetScale data with no vendor dependency. The main investment with Superset is operational: deploying, securing, and maintaining the platform requires ongoing engineering attention. Teams that want a managed, low-maintenance reporting layer will find Superset heavier than necessary, but those who want maximum control over their analytics infrastructure will appreciate the flexibility. #### Key features 1. SQLAlchemy MySQL connector for PlanetScale compatibility 2. SQL IDE and visual dashboard builder with broad chart library 3. Fully open-source with self-hosted deployment #### What users say about Apache Superset > We can combine data from different systems for thorough analysis because the platform supports an extensive range of data sources, including MySQL-compatible databases. For teams with engineering resources to manage it, the flexibility is hard to match. #### Pricing Free and open-source. Operational costs depend on hosting and ongoing maintenance resources. --- ### 6. Metabase: Best for self-service analytics on PlanetScale ![Metabase — PlanetScale reporting tool](/images/blogs/_tools/metabase.png) #### Best for Teams that want nontechnical users to explore PlanetScale data independently, without writing SQL. [Metabase](https://www.metabase.com/) is one of the most widely used self-service BI tools, and it connects to PlanetScale via the MySQL driver. Its standout feature is the question builder — a guided, click-based interface that lets nontechnical users filter, group, and summarize data without touching SQL. For teams with mixed technical backgrounds, Metabase makes data exploration genuinely accessible to product managers, support leads, and operations teams. Note that Metabase's compatibility with PlanetScale can require some configuration — specifically around the query planner settings. Teams should verify their connection carefully during setup. For production use, Metabase Cloud or a well-maintained self-hosted instance both work, with Metabase's active community providing guidance for common PlanetScale-specific configurations. #### Key features 1. Guided question builder for nontechnical users with no SQL required 2. MySQL driver for PlanetScale connection with SSL support 3. Shareable dashboards, scheduled email reports, and embedded analytics #### What users say about Metabase > Metabase is great at turning data access into something that doesn't require a data analyst for every question. Our ops and support teams can answer their own questions now, which has reduced the backlog on our data team significantly. #### Pricing Metabase Open Source is free and self-hosted. Metabase Cloud starts at **$500/month** for up to 5 users, with pricing scaling by user count. --- ## Implementing your PlanetScale reporting tool effectively Choosing the right PlanetScale reporting tool is only half the job. Even the best tool won't deliver value without a thoughtful rollout. Here's how to get the most out of whichever option you choose. ### 1. Onboarding your team Start with a proper onboarding session for anyone who will use the tool — developers, product managers, support leads, and operations teams. A short focused session upfront prevents the confusion and workarounds that emerge when people stumble through a new tool on their own. Establish clear expectations for who can access which dashboards and how new reports get created. ### 2. Start with a pilot Rather than rolling dashboards out to everyone at once, start with a small group and a focused set of metrics. A pilot validates your data model, confirms that your key metrics are calculated consistently, and gives you a chance to catch any schema-specific quirks in your PlanetScale setup before they reach a wider audience. ### 3. Collect feedback continuously Reporting is not static. As your product evolves, your data model changes, and your team's questions change with it. Build a lightweight feedback loop — a shared doc, a Slack channel, a recurring review — where users can flag outdated dashboards, missing metrics, or charts that don't reflect how the business actually works. Dashboards that nobody trusts are worse than no dashboards at all. ### 4. Monitor performance and usage Track which dashboards get opened most frequently, which queries are slowest, and which reports get ignored. If a dashboard isn't used, it may need to be improved or removed. If a query is slow on PlanetScale, optimizing the underlying SQL or adding a read replica may be the right fix. Knowing how your team interacts with reporting helps you measure ROI and prioritize improvements over time. --- ## Conclusion The PlanetScale reporting tool you choose shapes how your whole team understands the business. It determines whether your product manager can answer their own questions, whether your support team has visibility into order history, and whether your customers trust the analytics you've built inside your product. If you want a reporting layer that works naturally with PlanetScale — without data pipelines, warehouses, or heavy setup — tools like Draxlr let you ship dashboards quickly while keeping your stack simple. Whether you need internal visibility, customer-facing embedded analytics, or Slack alerts when key metrics change, the right tool should help you move fast without sacrificing clarity or data security. If you'd like to explore a PlanetScale-native approach to dashboards and reporting, you can [try Draxlr](https://app.draxlr.com/register/) and see how it fits your workflow. --- ## FAQs ### 1. What is a PlanetScale reporting tool? A PlanetScale reporting tool connects directly to your PlanetScale database and lets you build dashboards, charts, and shared reports without writing ad-hoc SQL for every question. These tools give your team controlled access to live data without requiring direct database credentials or engineering involvement for every request. ### 2. Can I connect standard BI tools to PlanetScale? Yes. PlanetScale is MySQL-compatible, so most BI tools that support MySQL can connect using standard credentials and an SSL certificate. Some tools may require minor configuration around PlanetScale's Vitess-specific query behavior, but the connection itself works with any standard MySQL driver. ### 3. Does PlanetScale have built-in reporting? PlanetScale includes built-in Insights for query performance monitoring and database observability, but it's not designed for business dashboards or team-level reporting. For product metrics, customer analytics, or internal reporting, a dedicated reporting tool is the right choice. ### 4. Is it safe to connect a reporting tool directly to PlanetScale? Yes. Best practice is to use a read-only password (PlanetScale lets you create branch-specific passwords with read-only access) and connect over SSL. Most modern reporting tools support both. This ensures your reporting layer can never accidentally modify production data. ### 5. Can non-technical team members use PlanetScale reporting tools? Yes — with the right tool. Options like Draxlr and Metabase offer visual query builders, saved dashboards, and sharing features that make data exploration accessible to product managers, support teams, and operations leads without requiring SQL knowledge. ### 6. Do I need a data warehouse to report on PlanetScale data? No. PlanetScale is a production-grade MySQL-compatible database, and most reporting tools connect directly to it. Unless you have very large analytical workloads or need to join across multiple databases, a direct connection is faster to set up and easier to maintain than introducing a warehouse.

Best MSSQL Reporting Tools

hello@draxlr.com (Draxlr) — Mon, 15 Jun 2026 00:00:00 GMT

Microsoft SQL Server (MSSQL) is still one of the most widely deployed relational databases in the world. It runs core systems at enterprises, powers SaaS products, and sits behind countless internal tools where teams depend on clean, trustworthy data. But once a business grows past a handful of users, SQL Server Management Studio and ad-hoc T-SQL queries stop being a scalable way to actually see what's happening in your data. The problem is rarely SQL Server itself — the engine can handle the workload. The problem is access. Developers and DBAs can write queries, but product managers, operations teams, finance, and founders usually cannot. Without a dedicated reporting layer, every data question turns into a Jira ticket, a Slack ping, or an email that pulls engineering off product work, and the dashboards business users asked for last quarter keep slipping. **MSSQL reporting tools** solve this by sitting between your database and the people who need answers. They connect directly to your SQL Server instance, provide visual interfaces for exploring data and building dashboards, and help teams get consistent, up-to-date reporting without writing T-SQL by hand every time. In this article, we break down the most reliable MSSQL reporting tools to consider for 2026. --- ## What are MSSQL reporting tools? **MSSQL reporting tools** connect directly to your Microsoft SQL Server database and turn live data into dashboards, charts, and shared reports without requiring SQL for every question. Rather than handing out raw database credentials to everyone on your team, these tools provide a controlled interface for querying, visualizing, and distributing data insights built on top of your SQL Server schema. Modern reporting tools connect to SQL Server over the standard TDS protocol using ODBC, JDBC, or native drivers, which means there is no need for ETL pipelines or data duplication. Queries execute against your actual tables, views, and stored procedures. Data stays live, permissions stay consistent with your existing SQL Server logins and roles, and both technical and nontechnical users can answer their own questions from a single reporting workspace instead of filing requests against your DBA team. --- ## How to choose a MSSQL reporting tool Choosing the right MSSQL reporting tool depends on what you want to achieve with your data. An enterprise team standardizing on Microsoft will prioritize very different features than a product team that wants to embed analytics for customers or a finance team replacing a legacy BI stack. Start by identifying your primary goal. ### 1. If your goal is internal reporting for your team Pick a tool with strong T-SQL support, an intuitive dashboard builder, and simple sharing. You want clarity without adding another system your DBA team has to babysit. Tools that let nontechnical teammates explore data without writing queries are especially valuable for keeping engineering and database time focused on real product work. ### 2. If your goal is customer-facing analytics Look for embedding, white-label controls, and row-level filtering. Your users should see analytics that look like part of your product, not an iframe pasted on top. This requires secure embed tokens and multi-tenant data filtering applied at the query level, not just the UI. ### 3. If your goal is replacing an existing analytics stack Prioritize tools with flexible permissions, reusable queries, scheduling, and scalable embedding. These features help you migrate and streamline an entire analytics layer rather than just stacking another tool on top of SQL Server Reporting Services (SSRS) or a dated BI deployment. ### 4. If your goal is minimal engineering involvement Pick a tool that lets product, support, finance, or operations teams explore data on their own. Features like visual query builders and AI text-to-SQL reduce the number of ad-hoc requests developers and DBAs get pulled into. The best tools make self-service analytics genuinely accessible to nontechnical teammates working on top of your SQL Server schema. Ultimately, the right MSSQL reporting tool depends on whether you prioritize speed, customer experience, or long-term scalability. --- ## Key features to look for in MSSQL reporting tools A quick search for MSSQL analytics or reporting tools will surface plenty of options, but they're not all built the same. Choosing the right one becomes much easier when you know which capabilities matter most for teams working directly on Microsoft SQL Server. Here are the core features to evaluate when comparing MSSQL reporting tools. ### 1. Native SQL Server compatibility Your MSSQL reporting tool should connect directly to SQL Server — whether on-prem, Azure SQL Database, or Azure SQL Managed Instance — without ETL, sync jobs, or schema duplication. Native support means: 1. Queries run on your actual schema, views, and stored procedures 2. No pipelines or replication required 3. No new infrastructure to manage 4. Consistent permissions tied to your existing SQL Server logins, roles, and row-level security Tools that require you to move data into a warehouse first will slow you down and add unnecessary complexity to your stack. ### 2. Ease of dashboard building Most teams using SQL Server don't have a dedicated BI department, so reporting has to be accessible. Look for features like: 1. Visual query building for nontechnical users 2. [Text to SQL AI](/blogs/ai-sql-tool/) 3. Reusable charts and saved queries 4. Filters and drilldowns 5. Clear layout controls 6. [Simple sharing via link or embed](/blogs/create-a-public-dashboard-url-for-your-sql-database/) The goal is that anyone on your team should be able to answer basic questions without a developer or DBA in the loop. ### 3. Embeddability (if you want customer-facing dashboards) If you plan to [embed analytics](/blogs/best-embedded-analytics-tools/) inside your product, choose a tool with: 1. Secure, stateless embed tokens 2. Row-level filtering for multi-tenant apps 3. White-label options Dashboards should feel native inside your product, not bolted on after the fact. If you're building on SQL Server, see [how to embed a MSSQL dashboard in your app](/blogs/how-to-embed-a-mssql-dashboard-in-your-app/) for a concrete walkthrough. ### 4. API access for automation MSSQL teams often automate workflows like report generation, alerts, scheduled queries, or embedding logic. An [API-first reporting tool](/blogs/build-sql-dashboard-with-ai/) gives you: 1. Programmatic control over dashboards and queries 2. Flexibility for future integrations 3. Easier connection to your authentication layer 4. Automation of recurring reporting tasks Developers should be able to trigger or manage analytics via API just like any other part of their stack. ### 5. Data security and access control Your MSSQL reporting tool must respect the same security expectations your application has. Important features include: 1. No data copying or external storage 2. User-scoped dashboards and query access 3. Secure embedding with filtered views 4. Strong team permission controls 5. Support for SSL/TLS and enterprise authentication (Microsoft Entra ID, Windows Authentication where relevant) 6. [Self-host option for sensitive environments](/blogs/ensuring-seamless-adoption-of-self-service-bi/) Since everything connects to your live production schema, access control is critical. ### 6. Documentation, support, and reliability Reporting is often implemented under pressure, late in the product cycle. Good documentation, responsive support, and a reliable UI save hours of frustration. Whether through docs, Slack communities, or direct support channels, pick a tool that won't leave you stuck when your team needs answers fast. --- ## The best MSSQL reporting tools to consider in 2026 Without further delay, here's a breakdown of the top MSSQL reporting tools worth exploring in 2026 and beyond. ### 1. Draxlr: Best MSSQL reporting tool for dashboards, team insights, and embedded analytics ![Draxlr — MSSQL reporting tool](/images/blogs/_tools/draxlr.png) #### Best for SaaS teams and internal ops teams using Microsoft SQL Server who want a straightforward way to build dashboards, explore data, and offer embedded analytics without introducing a complicated BI stack. Connect your Database [Draxlr](/) works directly with your SQL Server database and gives teams a clean interface for creating reports, running T-SQL, and [turning a Microsoft SQL Server database into dashboards](/blogs/microsoft-sql-server-database-to-dashboards/) without setup overhead. Built for modern product and operations teams, it helps you ship reporting fast — whether the goal is internal visibility or customer-facing analytics. For technical users, Draxlr offers full SQL control, virtual columns, filters, drill-through features, and database-aware autocomplete that understands your SQL Server schema. For nontechnical teammates, the visual query builder and AI text-to-SQL make data exploration accessible without needing to open SQL Server Management Studio. If your product includes customer analytics, Draxlr supports secure embedded dashboards with row-level filtering, white-label options, and React and Vue components that integrate naturally into your app. Teams can also automate reporting with scheduled emails, Slack alerts, and CSV, Excel, and PDF exports. Draxlr runs on your existing SQL Server infrastructure — no pipelines, warehouses, or modeling layers required. Everything connects directly to your live schema, whether SQL Server is on-prem, self-hosted in a VM, or running on Azure SQL Database. #### Key features 1. Connects directly to Microsoft SQL Server with no ETL or data prep work required 2. Visual Query Builder, full SQL editor, and AI Chat for text-to-SQL 3. Dashboard builder with filters, drill-through, and virtual columns 4. Embedded dashboards with secure tokens, row-level filters, and white-label controls 5. React and Vue SDKs for easy in-product analytics 6. Export options (CSV, Excel, PDF) and scheduled reports 7. Team-level permissions for safe access to production data 8. Supports both internal reporting and customer-facing analytics from the same workspace #### What users say about Draxlr > Draxlr offers a clean, modern interface and makes it incredibly easy to build dashboards — even for non-technical users. I especially like the visual quality and flexibility of the available graph types. It's intuitive, fast to set up, and has excellent usability from the start. The ability to securely share dashboards via link is a game-changer for our client reporting. — [Review from a SaaS Founder on G2](https://www.g2.com/products/draxlr/reviews/draxlr-review-11185807) #### Pricing Draxlr offers multiple plans depending on team size, embedding needs, and data volume. Pricing starts at **$75/month** --- ### 2. Power BI: Best for organizations already standardized on the Microsoft ecosystem ![Power BI — MSSQL reporting tool](/images/blogs/_tools/power-bi.png) #### Best for Enterprises and mid-market teams that already live inside Microsoft 365, Azure, and Teams and want BI dashboards connected to SQL Server alongside Excel, SharePoint, and other Microsoft services. [Power BI](https://www.microsoft.com/en-us/power-platform/products/power-bi) has the most natural fit with Microsoft SQL Server of any BI tool on this list. The native SQL Server connector supports both Import and DirectQuery modes, and authentication works through Microsoft Entra ID, SQL credentials, or Windows Authentication. For SQL Server Enterprise customers with Software Assurance, Power BI Report Server is included at no additional cost, which makes it a common on-prem reporting choice for regulated industries. Outside the Microsoft ecosystem, Power BI is less compelling. Its desktop-first workflow, Windows dependency for some features, and premium licensing for embedded analytics can slow down product teams that need fast, lightweight reporting directly from SQL Server. Embedding dashboards in a web product also requires Premium or Embedded capacity licensing and additional configuration that simpler tools handle out of the box. #### Key features 1. Native SQL Server connector with Import and DirectQuery support 2. Advanced DAX modeling with a large library of visualization types 3. Deep integration with Microsoft 365, Azure, Teams, and SQL Server Reporting Services #### What users say about Power BI > Power BI has transformed the way we approach data by turning complex data into clear real-time insights and actionable strategies. With its powerful visualizations and integration across multiple data sources including SQL Server, I've been able to track performance trends and uncover insights with precision. — Summary of Power BI reviews on [G2](https://www.g2.com/products/microsoft-microsoft-power-bi/reviews) #### Pricing Power BI Desktop is free. Pro starts at **$14/user/month** and Premium Per User (PPU) starts at **$24/user/month**. Premium capacity plans start at **$4,995/month** for P1. --- ### 3. Metabase: Best for teams that want quick, no-code dashboards on SQL Server ![Metabase — MSSQL reporting tool](/images/blogs/_tools/metabase.png) #### Best for Small to mid-sized teams that want fast self-service reporting on SQL Server with minimal setup and a friendly interface for nontechnical users. [Metabase](https://www.metabase.com/) is one of the most widely adopted open-source BI tools for Microsoft SQL Server. Metabase ships with a native SQL Server driver and supports the oldest supported SQL Server version through the latest stable release, with SSL enabled by default when possible. Setup usually takes a few minutes: point Metabase at the host, port, database, and credentials, and it auto-discovers your schema. Once connected, Metabase's question-and-answer interface lets users explore data, build charts, and assemble dashboards without writing SQL. It also has a full SQL editor for power users who prefer T-SQL, plus scheduling and email delivery of reports. That said, advanced features like row-level security, SSO, and white-label embedding are only available on paid Cloud plans, which can get expensive as team size grows. #### Key features 1. Native Microsoft SQL Server driver with out-of-the-box SSL support 2. No-code Question Builder and full SQL editor for mixed-skill teams 3. Scheduled reports, email delivery, and dashboard subscriptions #### What users say about Metabase > Metabase stands out for its simplicity and quick setup. As a data analyst, I appreciated how fast I could connect it to our databases without needing any complex configuration. The no-code query builder is excellent for non-technical users to explore data, create charts, and build dashboards without writing SQL. — [Review on G2](https://www.g2.com/products/metabase/reviews) #### Pricing Open-source self-hosted version is free. Cloud Starter starts at **$100/month** base plus $6/user/month. Cloud Pro with SSO, row-level security, and embedding starts at **$575/month** base plus $12/user/month. --- ### 4. Tableau: Best for analyst-heavy teams that need advanced visualizations ![Tableau — MSSQL reporting tool](/images/blogs/_tools/tableau.png) #### Best for Larger organizations with dedicated BI analysts who want deep visual exploration and polished executive dashboards on top of SQL Server. [Tableau](https://www.tableau.com/) ships with a built-in Microsoft SQL Server driver and has supported the connector by default since Tableau Desktop v9.3. The connector handles stored procedures, custom SQL, and SSL connections, and can also connect to Azure SQL Database, Azure Synapse Analytics (formerly SQL Data Warehouse), and SQL Server Parallel Data Warehouse. For teams with dedicated analysts, Tableau's visual vocabulary and depth of chart types remain hard to beat. The tradeoff is cost and complexity. Tableau is aimed at analyst-driven workflows, and licensing, Tableau Server administration, and Tableau Prep for modeling can all add up quickly. For a small SaaS or ops team that just needs clean dashboards over SQL Server, Tableau is usually more firepower — and more spend — than the problem calls for. #### Key features 1. Built-in Microsoft SQL Server driver with stored procedure and custom SQL support 2. Deep library of interactive visualizations and a polished dashboard builder 3. Tableau Prep for data shaping and Tableau Server for enterprise governance #### What users say about Tableau > Users consistently praise Tableau for its intuitive drag-and-drop interface that simplifies the creation of interactive dashboards, making data visualization accessible even for non-technical users. It integrates seamlessly with a wide range of data sources — from SQL databases to cloud platforms — which makes it easy to pull in data without heavy engineering support. — Summary of Tableau reviews on [G2](https://www.g2.com/products/tableau/reviews) #### Pricing Viewer starts at **$15/user/month**, Explorer at **$42/user/month**, and Creator at **$75/user/month**, all billed annually. Enterprise pricing is higher, with Creator around **$115/user/month**. --- ### 5. Grafana: Best for operational and time-series dashboards on SQL Server ![Grafana — MSSQL reporting tool](/images/blogs/_tools/grafana.png) #### Best for Engineering, DevOps, and platform teams that need real-time operational dashboards, monitoring, and time-series visualization directly from SQL Server, Azure SQL Database, or a SQL Server 2005+ instance. [Grafana](https://grafana.com/) ships with a built-in Microsoft SQL Server data source plugin that supports any SQL Server version from 2005 onwards, plus Azure SQL Database. No separate plugin install is required. It supports time-series queries with built-in time-grouping macros, table panels, template variables, annotations that overlay SQL Server events on your graphs, and alerting based on SQL Server query results — which makes it a natural fit for operational dashboards next to Prometheus, Loki, or other observability tooling. Where Grafana is less suited is business reporting or polished product analytics aimed at nontechnical users. The interface assumes comfort with metrics concepts and SQL, and assembling executive-ready business dashboards takes more work compared to purpose-built BI tools. But for engineering teams that already live in dashboards, Grafana's deep SQL Server integration and alerting are hard to beat. #### Key features 1. Built-in Microsoft SQL Server data source plugin with support for SQL Server 2005+ and Azure SQL Database 2. Alerting based on SQL Server query results with flexible notification routing 3. Extensive visualization library and time-series panels for operational workloads #### What users say about Grafana > Grafana is a great tool for real-time monitoring. Its intuitive dashboards make it easy to visualise trends and statuses across multiple systems. The most I like about this software is that it's free to use and it's open source — it has a broad plugin ecosystem that can connect to almost anything including SQL Server and Azure SQL. #### Pricing Open-source version is free to self-host. Grafana Cloud starts at **$19/month** plus usage-based billing for logs, metrics, and traces. --- ### 6. Apache Superset: Best self-hosted BI platform for teams with engineering capacity ![Apache Superset — MSSQL reporting tool](/images/blogs/_tools/apache-superset.png) #### Best for Companies that want a full-featured open-source BI platform, have in-house engineering to run it, and don't mind managing their own analytics infrastructure on top of SQL Server. [Apache Superset](https://superset.apache.org/) connects to Microsoft SQL Server using the `mssql+pymssql` SQLAlchemy driver and supports JOINs, subqueries, dynamic schema, catalog support, SSH tunneling, query cancellation, cost estimation, and SQL validation against SQL Server. It ships with SQL Lab for exploration, a no-code Explore view, a dashboard builder, row-level security, and a large library of visualizations. For teams comfortable operating their own stack, Superset gives you the flexibility of a modern BI tool without vendor lock-in. The tradeoff is operational overhead. Superset officially recommends running its own metadata database on Postgres rather than SQL Server, so even if you're a pure SQL Server shop on the data side, you'll likely add a small Postgres instance for Superset itself. Hosting, upgrades, auth integration, and user management are all on you, which is usually fine for engineering-heavy teams but a real commitment for smaller product teams. #### Key features 1. Connects to SQL Server via the `mssql+pymssql` SQLAlchemy driver with JOINs, subqueries, and SSH tunneling 2. SQL Lab, no-code Explore view, and a rich gallery of visualizations 3. Row-level security and role-based access controls for multi-team deployments #### What users say about Apache Superset > We can combine data from different systems for thorough analysis because the platform supports an extensive amount of data sources, including databases like SQL Server, MySQL, and Postgres. #### Pricing Apache Superset is free and open-source. Operational costs depend on hosting, maintenance, and any managed Superset offerings you choose. --- ## Implementing your MSSQL reporting tool effectively Choosing the right MSSQL reporting tool is only half the job. A tool can give you clean dashboards and powerful querying, but it won't deliver value on its own. Like any part of your product, success depends on how well you implement it and how quickly your team starts using it. Here's how to get the most out of whichever MSSQL reporting tool you choose. ### 1. Onboarding your team Invest time in proper onboarding. Developers, DBAs, product managers, support teams, and anyone responsible for reporting should understand how the tool works from day one. A short, focused onboarding session prevents future bottlenecks and avoids misconfigurations that lead to misleading dashboards or accidental data exposure — especially important on SQL Server, where many teams already have mature login and role structures you want analytics to respect. ### 2. Start with a pilot Instead of rolling dashboards out to everyone at once, start with a small group. A controlled pilot helps you validate metrics, refine permissions, and confirm that the dashboards you built actually answer the questions teams have. It also reduces the chance of misaligned metric definitions or noisy, unused reports cluttering your workspace. ### 3. Collect feedback continuously Reporting is not a "set it and forget it" feature. Once dashboards go live, actively ask your users — internal or customer-facing — what works and what needs improvement. As your SQL Server schema and product evolve, your reporting layer should evolve alongside it. ### 4. Monitor performance and usage Track how often dashboards are viewed, which queries run most frequently, and which reports are ignored. If certain dashboards never get opened, refine them. If certain queries slow down at scale, work with your DBA to optimize them, add indexes, or push aggregations into views or stored procedures. Understanding how your users interact with analytics helps you measure ROI and improve reporting over time. --- ## Conclusion The MSSQL reporting tool you choose becomes part of how your team understands the business. It shapes how founders track growth, how support teams identify issues, and how customers interpret their own data inside your product. It needs to connect reliably to your SQL Server schema, serve the right people at the right level of access, and update without manual effort. If you want a reporting layer that works naturally with Microsoft SQL Server without extra infrastructure or data pipelines, tools like Draxlr help you ship dashboards quickly while keeping your stack simple. Whether you need internal visibility, customer-facing analytics, or embedded dashboards inside your product, the right tool should help you move fast without compromising on clarity or reliability. If you'd like to explore a SQL Server-native approach to dashboards and reporting, you can [try Draxlr](https://app.draxlr.com/register/) and see how it fits your workflow. --- ## FAQs ### 1. What is a MSSQL reporting tool? A MSSQL reporting tool connects directly to your Microsoft SQL Server database and lets you build dashboards, charts, and reports without writing T-SQL for every request. These tools help teams explore live data safely without setting up ETL pipelines or extra infrastructure. ### 2. Can I build customer-facing dashboards on SQL Server? Yes. Several reporting tools support embedding dashboards inside your product. Look for options with secure embed tokens, row-level filtering for multi-tenant applications, and styling controls so the dashboards match your product's UI. ### 3. Does Microsoft SQL Server have built-in reporting? SQL Server ships with SQL Server Management Studio and SQL Server Reporting Services (SSRS), which cover paginated reports and traditional enterprise reporting well, but they are not designed for modern interactive dashboards, self-service analytics, or embedded analytics in a SaaS product. Most teams pair SQL Server with a dedicated reporting tool for those use cases. ### 4. Is it safe to connect a reporting tool directly to SQL Server? Yes, as long as the tool respects SQL Server logins and roles, uses encrypted connections (SSL/TLS), and supports user-level filtering when embedding dashboards. Most modern reporting tools offer these safeguards. Avoid tools that require exporting or duplicating your data outside your environment. ### 5. Can non-technical team members use MSSQL reporting tools? Yes. Choose a tool that offers visual query builders, saved reports, dashboard filters, and easy sharing so nontechnical users can work with SQL Server data without writing T-SQL. AI text-to-SQL features make this even more accessible for founders, finance, and operations teams. ### 6. Do I need a data warehouse for reporting on SQL Server? No. Most reporting tools connect directly to SQL Server — including on-prem, self-hosted, and Azure SQL Database — without requiring a separate warehouse. Unless you have extremely large analytical workloads or need cross-database joins across multiple systems, a direct SQL Server connection is faster to set up, easier to maintain, and sufficient for most product and business reporting needs.

Best MariaDB Reporting Tools

hello@draxlr.com (Draxlr) — Sun, 14 Jun 2026 00:00:00 GMT

MariaDB is one of the most popular open-source relational databases in production today. Forked from MySQL and maintained by its original creators, it powers everything from SaaS products and e-commerce platforms to government and enterprise workloads. But once your application has real users, the MariaDB command-line client and ad-hoc SQL queries stop being a reliable way to understand what's happening in your data. The bottleneck is rarely MariaDB itself. The engine handles the workload. The bottleneck is access. Developers can write queries, but product managers, support teams, operations staff, and founders often cannot. Without a dedicated reporting layer, every data question turns into an informal request that pulls engineering off more important work, and dashboards that should have shipped last quarter keep slipping. **MariaDB reporting tools** solve this by sitting between your database and the people who need answers. They connect directly to your MariaDB schema, provide visual interfaces for exploring data and building dashboards, and help teams get consistent, up-to-date reporting without writing SQL by hand every time. In this article, we break down the most reliable MariaDB reporting tools to consider for 2026. --- ## What are MariaDB reporting tools? **MariaDB reporting tools** connect directly to your MariaDB database and turn live data into dashboards, charts, and shared reports without requiring SQL for every question. Rather than exposing raw database access to everyone on your team, these tools provide a controlled interface for querying, visualizing, and distributing data insights. Because MariaDB is wire-compatible with MySQL, most modern reporting tools connect to it using the MySQL driver over standard credentials, which means there is no need for ETL pipelines or data duplication. Queries execute against your real schema, your data stays live, permissions stay consistent, and both technical and nontechnical users can get what they need from a single reporting workspace. --- ## How to choose a MariaDB reporting tool Choosing the right MariaDB reporting tool depends on what you want to achieve with your data. A SaaS team that needs product metrics will prioritize very different features than a company looking to embed analytics for customers or replace a legacy BI stack. Start by identifying your primary goal. ### 1. If your goal is internal reporting for your team Pick a tool with strong SQL support, an intuitive dashboard builder, and simple sharing. You want clarity without adding another system to maintain. Tools that let nontechnical teammates explore data without writing queries are especially valuable for keeping engineering time focused on product work. ### 2. If your goal is customer-facing analytics Look for embedding, white-label controls, and row-level filtering. Your users should see analytics that look like part of your product, not an iframe pasted on top. This requires secure embed tokens and multi-tenant data filtering at the query level. ### 3. If your goal is replacing an existing analytics stack Prioritize tools with flexible permissions, reusable queries, scheduling, and scalable embedding. These features help you migrate and streamline an entire analytics layer rather than just stacking another tool on top of what you already have. ### 4. If your goal is minimal engineering involvement Pick a tool that lets product, support, or operations teams explore data on their own. Features like visual query builders and AI text-to-SQL reduce the number of ad-hoc requests developers get pulled into. The best tools make self-service analytics genuinely accessible to nontechnical teammates. Ultimately, the right MariaDB reporting tool depends on whether you prioritize speed, customer experience, or long-term scalability. --- ## Key features to look for in MariaDB reporting tools A quick search for MariaDB analytics or reporting tools will surface plenty of options, but they're not all built the same. Choosing the right one becomes much easier when you know which capabilities matter most for teams working directly on MariaDB. Here are the core features to evaluate when comparing MariaDB reporting tools. ### 1. Native MariaDB compatibility Your MariaDB reporting tool should connect directly to your database without ETL, sync jobs, or schema duplication. Native support means: 1. Queries run on your actual schema 2. No pipelines or replication required 3. No new infrastructure to manage 4. Consistent permissions tied to your MariaDB users Tools that require you to move data into a warehouse first will slow you down and add unnecessary complexity to your stack. ### 2. Ease of dashboard building Most teams using MariaDB don't have a dedicated BI department, so reporting has to be accessible. Look for features like: 1. Visual query building for nontechnical users 2. [Text to SQL AI](/blogs/ai-sql-tool/) 3. Reusable charts and saved queries 4. Filters and drilldowns 5. Clear layout controls 6. [Simple sharing via link or embed](/blogs/create-a-public-dashboard-url-for-your-sql-database/) The goal is that anyone on your team should be able to answer basic questions without developer involvement. ### 3. Embeddability (if you want customer-facing dashboards) If you plan to [embed analytics](/blogs/best-embedded-analytics-tools/) inside your product, choose a tool with: 1. Secure, stateless embed tokens 2. Row-level filtering for multi-tenant apps 3. White-label options Dashboards should feel native inside your product, not bolted on after the fact. ### 4. API access for automation MariaDB teams often automate workflows like report generation, alerts, scheduled queries, or embedding logic. An [API-first reporting tool](/blogs/build-sql-dashboard-with-ai/) gives you: 1. Programmatic control over dashboards and queries 2. Flexibility for future integrations 3. Easier connection to your authentication layer 4. Automation of recurring reporting tasks Developers should be able to trigger or manage analytics via API just like any other part of their stack. ### 5. Data security and access control Your MariaDB reporting tool must respect the same security expectations your application has. Important features include: 1. No data copying or external storage 2. User-scoped dashboards and query access 3. Secure embedding with filtered views 4. Strong team permission controls 5. [Self-host option for sensitive environments](/blogs/ensuring-seamless-adoption-of-self-service-bi/) Since everything connects to your live production schema, access control is critical. ### 6. Documentation, support, and reliability Reporting is often implemented under pressure, late in the product cycle. Good documentation, responsive support, and a reliable UI save hours of frustration. Whether through docs, Slack communities, or direct support channels, pick a tool that won't leave you stuck when your team needs answers fast. --- ## The best MariaDB reporting tools to consider in 2026 Without further delay, here's a breakdown of the top MariaDB reporting tools worth exploring in 2026 and beyond. Here's a quick side-by-side comparison, followed by a detailed look at each tool.

Tool	Best for	Open source	Embedding	Starting price
Draxlr	Dashboards and embedded analytics for SaaS teams	No	Yes — white-label, row-level	$75/month
Metabase	Quick no-code self-service BI	Yes	Paid plans	Free / $100/month
Grafana	Operational and time-series monitoring	Yes	Limited	Free / $19/month
Redash	SQL-first query dashboards	Yes	Limited	Free / ~$49/month
Apache Superset	Full self-hosted BI platform	Yes	Row-level security	Free (self-host)
Power BI	Microsoft-centric organizations	No	Premium add-on	$14/user/month

### 1. Draxlr: Best MariaDB reporting tool for dashboards, team insights, and embedded analytics ![Draxlr — MariaDB reporting tool](/images/blogs/_tools/draxlr.png) #### Best for SaaS teams using MariaDB who want a straightforward way to build dashboards, explore data, and offer embedded analytics without introducing a complicated BI stack. Connect your Database [Draxlr](/) works directly with your MariaDB database and gives teams a clean interface for creating reports, running SQL, and [building dashboards from MariaDB data](/blogs/how-to-build-dashboards-from-mariadb-data/) without setup overhead. Built for modern product teams, it helps you ship reporting fast — whether the goal is internal visibility or customer-facing analytics. For technical users, Draxlr offers full SQL control, virtual columns, filters, drill-through features, and database-aware autocomplete that understands your MariaDB schema. For nontechnical teammates, the visual query builder and AI text-to-SQL make data exploration accessible without needing to open a SQL editor. If your product includes customer analytics, Draxlr supports secure embedded dashboards with row-level filtering, white-label options, and React and Vue components that integrate naturally into your app. Teams can also automate reporting with scheduled emails, Slack alerts, and CSV, Excel, and PDF exports. Draxlr runs on your existing MariaDB infrastructure — no pipelines, warehouses, or modeling layers required. Everything connects directly to your live schema. #### Key features 1. Connects directly to MariaDB with no ETL or data prep work required 2. Visual Query Builder, full SQL editor, and AI Chat for text-to-SQL 3. Dashboard builder with filters, drill-through, and virtual columns 4. Embedded dashboards with secure tokens, row-level filters, and white-label controls 5. React and Vue SDKs for easy in-product analytics 6. Export options (CSV, Excel, PDF) and scheduled reports 7. Team-level permissions for safe access to production data 8. Supports both internal reporting and customer-facing analytics from the same workspace #### What users say about Draxlr > Draxlr offers a clean, modern interface and makes it incredibly easy to build dashboards — even for non-technical users. I especially like the visual quality and flexibility of the available graph types. It's intuitive, fast to set up, and has excellent usability from the start. The ability to securely share dashboards via link is a game-changer for our client reporting. — [Review from a SaaS Founder on G2](https://www.g2.com/products/draxlr/reviews/draxlr-review-11185807) #### Pricing Draxlr offers multiple plans depending on team size, embedding needs, and data volume. Pricing starts at **$75/month** --- ### 2. Metabase: Best for teams that want quick, no-code dashboards on MariaDB ![Metabase — MariaDB reporting tool](/images/blogs/_tools/metabase.png) #### Best for Small to mid-sized teams that want fast self-service reporting on MariaDB with minimal setup and a friendly interface for nontechnical users. [Metabase](https://www.metabase.com/) is one of the most widely adopted open-source BI tools for MariaDB. Because MariaDB shares a driver with MySQL, Metabase connects using the MySQL driver out of the box, and setup takes only minutes. Once connected, Metabase provides a question-and-answer interface that allows users to explore data, build dashboards, and share reports without writing SQL. For teams that need quick wins on internal reporting, Metabase is hard to beat at the entry level. It supports SQL questions for power users while keeping the no-code query builder accessible for everyone else. The tool also supports scheduling and email delivery of reports, making it a common first choice for growing product teams. That said, advanced features like row-level security, white-label embedding, and SSO require paid plans. #### Key features 1. Native MariaDB connection via the MySQL driver with no configuration overhead 2. No-code Question Builder and full SQL editor for mixed-skill teams 3. Scheduled reports and automated email delivery #### What users say about Metabase > A major benefit is the open-source paradigm, with simple setup particularly for PostgreSQL, MySQL, and Snowflake. Fast connection to MySQL and MariaDB databases without needing any complex configuration. #### Pricing Open-source self-hosted version is free. Cloud Starter starts at **$100/month** base plus $6/user/month. Cloud Pro with SSO, row-level security, and embedding starts at **$575/month** base plus $12/user/month. --- ### 3. Grafana: Best for operational and time-series dashboards on MariaDB ![Grafana — MariaDB reporting tool](/images/blogs/_tools/grafana.png) #### Best for Engineering and DevOps teams that need real-time operational dashboards, metrics monitoring, and time-series visualization directly from a MariaDB datasource. [Grafana](https://grafana.com/) ships with a built-in MySQL data source that works with MySQL-compatible databases like MariaDB and Percona Server. It supports templating, annotations, alerting, and SQL-based panels, making it the standard choice for operational dashboards — especially for teams already using Prometheus, Loki, or other observability tooling. If you need to monitor application health, track service performance, or visualize time-series data from MariaDB, Grafana is purpose-built for that use case. Where Grafana is less suited is for business reporting or product analytics aimed at nontechnical users. The interface requires comfort with metrics concepts and SQL, and building polished business dashboards takes more effort compared to purpose-built BI tools. But for engineering teams that live in dashboards, Grafana's flexibility and deep MySQL/MariaDB integration make it a strong fit. #### Key features 1. Built-in MySQL data source that connects natively to MariaDB — no plugins required 2. Alerting based on MariaDB query results with flexible notification routing 3. Extensive visualization library and time-series panel support for operational workloads #### What users say about Grafana > Grafana is a great tool for real-time monitoring. Its intuitive dashboards make it easy to visualise trends and statuses across multiple systems. The most I like about this software is that it's free to use and it's open source — it has a broad plugin ecosystem that can connect to almost anything including MySQL and MariaDB. #### Pricing Open-source version is free to self-host. Grafana Cloud starts at **$19/month** plus usage-based billing for logs, metrics, and traces. --- ### 4. Redash: Best open-source SQL-first reporting tool for MariaDB ![Redash — MariaDB reporting tool](/images/blogs/_tools/redash.png) #### Best for Data-literate teams that want a collaborative SQL editor, query-based dashboards, and scheduled alerts directly on top of MariaDB. [Redash](https://redash.io/) is an open-source tool designed around the workflow of writing SQL, saving queries as visualizations, and combining them into dashboards. It supports MariaDB through its MySQL data source and is a solid choice for teams that prefer a SQL-first approach over visual drag-and-drop interfaces. Queries are versioned, shareable, and reusable across multiple dashboards, which makes it easy for an engineering or data team to collaborate on analytics. Redash is less friendly for nontechnical users than tools like Metabase because it assumes you're comfortable writing SQL. But for teams that already think in queries, it offers fast iteration, flexible visualizations, and strong alerting features on top of your MariaDB schema. #### Key features 1. MariaDB support via the built-in MySQL data source 2. Collaborative SQL editor with saved queries, snippets, and version history 3. Query-based alerts and scheduled refreshes for dashboards #### What users say about Redash > Redash makes it easy to write SQL queries, visualize the results, and share them with the team. The alerting and scheduled refresh features are genuinely useful for operational dashboards, and connecting to our MariaDB instance took only a couple of minutes. #### Pricing Open-source self-hosted version is free. Redash's hosted plans have historically started around **$49/month** for small teams, with business tiers available for larger deployments. --- ### 5. Apache Superset: Best self-hosted BI platform for teams with engineering capacity ![Apache Superset — MariaDB reporting tool](/images/blogs/_tools/apache-superset.png) #### Best for Companies that want a full-featured open-source BI platform, have in-house engineering to run it, and don't mind managing their own analytics infrastructure. [Apache Superset](https://superset.apache.org/) is a powerful open-source analytics platform that connects to MariaDB through the MySQL driver. It supports SQL Lab for exploration, a dashboard builder, row-level security, and a large library of visualizations. For teams comfortable operating their own stack, Superset gives you the flexibility of a modern BI tool without vendor lock-in. The tradeoff is operational overhead. Superset is officially tested primarily against MySQL and Postgres, so if you want the smoothest experience, you'll likely still point Superset's metadata database at MySQL while using MariaDB as a data source. Hosting, upgrades, and user management are all on you, which is usually fine for engineering-heavy teams but a real commitment for smaller product teams. #### Key features 1. Connects to MariaDB via the MySQL connector for live query execution 2. SQL Lab, no-code Explore view, and a rich gallery of visualizations 3. Row-level security and role-based access controls for multi-team deployments #### What users say about Apache Superset > We can combine data from different systems for thorough analysis because the platform supports an extensive amount of data sources, including databases like MySQL, MariaDB, and Postgres. #### Pricing Apache Superset is free and open-source. Operational costs depend on hosting, maintenance, and any managed Superset offerings you choose. --- ### 6. Power BI: Best for organizations already using Microsoft tools ![Power BI — MariaDB reporting tool](/images/blogs/_tools/power-bi.png) #### Best for Large organizations that already operate within the Microsoft ecosystem and want to add BI reporting connected to MariaDB alongside Excel, Teams, and Azure services. [Power BI](https://www.microsoft.com/en-us/power-platform/products/power-bi) connects to MariaDB through its MySQL connector and ODBC drivers, and offers a comprehensive feature set for enterprise reporting. It supports DAX expressions, complex data modeling, and integration with the broader Microsoft stack including Azure, Excel, and Teams. For companies already standardized on Microsoft tools, Power BI's ecosystem fit makes it a natural choice for pulling MariaDB data into business-wide reports. Outside the Microsoft ecosystem, Power BI is less compelling. Its desktop-first workflow, Windows dependency for some features, and enterprise-focused pricing can slow down product teams that need fast, lightweight reporting directly from MariaDB. Embedding analytics in a web product also requires premium licensing and additional configuration that simpler tools handle out of the box. #### Key features 1. MariaDB connectivity via the MySQL connector and ODBC drivers 2. Advanced DAX modeling with a large library of visualization types 3. Deep integration with Microsoft 365, Azure, and Teams for enterprise workflows #### What users say about Power BI > Power BI has transformed the way we approach data by turning complex data into clear real-time insights and actionable strategies. With its powerful visualizations and integration across multiple data sources including MariaDB, I've been able to track performance trends and uncover insights with precision. #### Pricing Power BI Desktop is free. Pro plan starts at **$14/user/month**. Premium Per User starts at **$24/user/month**. --- ## Implementing your MariaDB reporting tool effectively Choosing the right MariaDB reporting tool is only half the job. A tool can give you clean dashboards and powerful querying, but it won't deliver value on its own. Like any part of your product, success depends on how well you implement it and how quickly your team starts using it. Here's how to get the most out of whichever MariaDB reporting tool you choose. ### 1. Onboarding your team Invest time in proper onboarding. Developers, product managers, support teams, and anyone responsible for reporting should understand how the tool works from day one. A short, focused onboarding session prevents future bottlenecks and avoids misconfigurations that lead to misleading dashboards or accidental data exposure. ### 2. Start with a pilot Instead of rolling dashboards out to everyone at once, start with a small group. A controlled pilot helps you validate metrics, refine permissions, and confirm that the dashboards you built actually answer the questions teams have. It also reduces the chance of misaligned metric definitions or noisy, unused reports cluttering your workspace. ### 3. Collect feedback continuously Reporting is not a "set it and forget it" feature. Once dashboards go live, actively ask your users — internal or customer-facing — what works and what needs improvement. As your MariaDB schema and product evolve, your reporting layer should evolve alongside it. ### 4. Monitor performance and usage Track how often dashboards are viewed, which queries run most frequently, and which reports are ignored. If certain dashboards never get opened, refine them. If certain queries slow down at scale, optimize them or add indexes to support the reporting workload. Understanding how your users interact with analytics helps you measure ROI and improve reporting over time. --- ## Conclusion The MariaDB reporting tool you choose becomes part of how your team understands the business. It shapes how founders track growth, how support teams identify issues, and how customers interpret their own data inside your product. It needs to connect reliably to your MariaDB schema, serve the right people at the right level of access, and update without manual effort. If you want a reporting layer that works naturally with MariaDB without extra infrastructure or data pipelines, tools like Draxlr help you ship dashboards quickly while keeping your stack simple. Whether you need internal visibility, customer-facing analytics, or embedded dashboards inside your product, the right tool should help you move fast without compromising on clarity or reliability. If you'd like to explore a MariaDB-native approach to dashboards and reporting, you can [try Draxlr](https://app.draxlr.com/register/) and see how it fits your workflow. --- ## FAQs ### 1. What is a MariaDB reporting tool? A MariaDB reporting tool connects directly to your MariaDB database and lets you build dashboards, charts, and reports without writing SQL for every request. These tools help teams explore live data safely without setting up ETL pipelines or extra infrastructure. ### 2. Can I build customer-facing dashboards with MariaDB? Yes. Several reporting tools support embedding dashboards inside your product. Look for options with secure embed tokens, row-level filtering for multi-tenant applications, and styling controls so the dashboards match your product's UI. ### 3. Does MariaDB have built-in reporting? MariaDB ships with a command-line client and supports standard SQL, but it does not include dashboards, charts, or shared reporting features. A dedicated reporting tool is required to turn MariaDB data into visual insights for your team. ### 4. Is it safe to connect a reporting tool directly to MariaDB? Yes, as long as the tool respects MariaDB user permissions, uses secure connections (SSL/TLS), and supports user-level filtering when embedding dashboards. Most modern reporting tools offer these safeguards. Avoid tools that require exporting or duplicating your data outside your environment. ### 5. Can non-technical team members use MariaDB reporting tools? Yes. Choose a tool that offers visual query builders, saved reports, dashboard filters, and easy sharing so nontechnical users can work with MariaDB data without writing SQL. AI text-to-SQL features make this even more accessible for founders and operations teams. ### 6. Do I need a data warehouse for reporting on MariaDB? No. Most reporting tools connect directly to MariaDB without requiring a separate warehouse. Unless you have extremely large analytical workloads or need cross-database joins across multiple systems, a direct MariaDB connection is faster to set up, easier to maintain, and sufficient for most product and business reporting needs.

Best MySQL Reporting Tools

hello@draxlr.com (Draxlr) — Sun, 14 Jun 2026 00:00:00 GMT

MySQL is the most widely deployed open-source relational database in the world. From early-stage startups to large enterprises, teams rely on it to power their applications, store customer data, and track business operations. But once your product reaches production scale, the command-line client and ad-hoc SQL queries stop being a sustainable way to understand what's happening in your data. The bottleneck is rarely MySQL itself. The database can handle the workload. The bottleneck is access. Developers can write queries, but product managers, support teams, operations staff, and founders often can't. Without a dedicated reporting layer, every data question becomes an informal request that pulls engineering off more important work. Dashboards that should have been built last quarter get pushed back indefinitely. **MySQL reporting tools** solve this by sitting between your database and the people who need answers. They connect directly to your MySQL schema, provide visual interfaces for exploring data and building dashboards, and help teams get consistent, up-to-date reporting without writing SQL by hand every time. In this article, we break down the most reliable MySQL reporting tools to consider for 2026. --- ## What are MySQL reporting tools? **MySQL reporting tools** connect directly to your MySQL database and turn live data into dashboards, charts, and shared reports without requiring SQL for every question. Rather than exposing raw database access to everyone on your team, these tools provide a controlled interface for querying, visualizing, and distributing data insights. Because these tools connect natively to MySQL, there's no need for ETL pipelines or data duplication. Queries execute against your real schema using standard MySQL credentials and connections. Your data stays live, permissions stay consistent, and both technical and nontechnical users can get what they need from a single reporting workspace. --- ## How to choose a MySQL reporting tool Choosing the right MySQL reporting tool depends on what you want to achieve with your data. A SaaS team that needs product metrics will prioritize very different features than a company looking to embed analytics for customers or replace a legacy BI stack. Start by identifying your primary goal. ### 1. If your goal is internal reporting for your team Pick a tool with strong SQL support, an intuitive dashboard builder, and simple sharing. You want clarity without adding another system to maintain. Tools that let nontechnical teammates explore data without writing queries are especially valuable for keeping engineering time focused on product work. ### 2. If your goal is customer-facing analytics Look for embedding, white-label controls, and row-level filtering. Your users should see analytics that look like part of your product, not an iframe pasted on top. This requires secure embed tokens and multi-tenant data filtering at the query level. ### 3. If your goal is replacing an existing analytics stack Prioritize tools with flexible permissions, reusable queries, scheduling, and scalable embedding. These features help you migrate and streamline an entire analytics layer rather than just stacking another tool on top of what you already have. ### 4. If your goal is minimal engineering involvement Pick a tool that lets product, support, or operations teams explore data on their own. Features like visual query builders and AI text-to-SQL reduce the number of ad-hoc requests developers get pulled into. The best tools make self-service analytics genuinely accessible to nontechnical teammates. Ultimately, the right MySQL reporting tool depends on whether you prioritize speed, customer experience, or long-term scalability. --- ## Key features to look for in MySQL reporting tools A quick search for MySQL analytics or reporting tools will surface plenty of options, but they're not all built the same. Choosing the right one becomes much easier when you know which capabilities matter most for teams working directly on MySQL. Here are the core features to evaluate when comparing MySQL reporting tools. ### 1. Native MySQL compatibility Your MySQL reporting tool should connect directly to your database without ETL, sync jobs, or schema duplication. Native support means: 1. Queries run on your actual schema 2. No pipelines or replication required 3. No new infrastructure to manage 4. Consistent permissions tied to your MySQL users Tools that require you to move data into a warehouse first will slow you down and add unnecessary complexity to your stack. ### 2. Ease of dashboard building Most teams using MySQL don't have a dedicated BI department, so reporting has to be accessible. Look for features like: 1. Visual query building for nontechnical users 2. [Text to SQL AI](/blogs/ai-sql-tool/) 3. Reusable charts and saved queries 4. Filters and drilldowns 5. Clear layout controls 6. [Simple sharing via link or embed](/blogs/create-a-public-dashboard-url-for-your-sql-database/) The goal is that anyone on your team should be able to answer basic questions without developer involvement. ### 3. Embeddability (if you want customer-facing dashboards) If you plan to [embed analytics](/blogs/best-embedded-analytics-tools/) inside your product, choose a tool with: 1. Secure, stateless embed tokens 2. Row-level filtering for multi-tenant apps 3. White-label options Dashboards should feel native inside your product, not bolted on after the fact. ### 4. API access for automation MySQL teams often automate workflows like report generation, alerts, scheduled queries, or embedding logic. An [API-first reporting tool](/blogs/build-sql-dashboard-with-ai/) gives you: 1. Programmatic control over dashboards and queries 2. Flexibility for future integrations 3. Easier connection to your authentication layer 4. Automation of recurring reporting tasks Developers should be able to trigger or manage analytics via API just like any other part of their stack. ### 5. Data security and access control Your MySQL reporting tool must respect the same security expectations your application has. Important features include: 1. No data copying or external storage 2. User-scoped dashboards and query access 3. Secure embedding with filtered views 4. Strong team permission controls 5. [Self-host option for sensitive environments](/blogs/ensuring-seamless-adoption-of-self-service-bi/) Since everything connects to your live production schema, access control is critical. ### 6. Documentation, support, and reliability Reporting is often implemented under pressure, late in the product cycle. Good documentation, responsive support, and a reliable UI save hours of frustration. Whether through docs, Slack communities, or direct support channels, pick a tool that won't leave you stuck when your team needs answers fast. --- ## The best MySQL reporting tools to consider in 2026 Without further delay, here's a breakdown of the top MySQL reporting tools worth exploring in 2026 and beyond. Here's a quick side-by-side comparison, followed by a detailed look at each tool.

Tool	Best for	Open source	Embedding	Starting price
Draxlr	Dashboards and embedded analytics for SaaS teams	No	Yes — white-label, row-level	$75/month
Metabase	Quick no-code self-service BI	Yes	Paid plans	Free / $100/month
Looker Studio	Free lightweight dashboards	No	Limited	Free
Grafana	Operational and time-series monitoring	Yes	Limited	Free / $19/month
Redash	SQL-first query dashboards	Yes	Limited	Free / ~$49/month
Tableau	Analyst-driven, complex visualizations	No	Add-on	$75/user/month
Power BI	Microsoft-centric organizations	No	Premium add-on	$14/user/month

### 1. Draxlr: Best MySQL reporting tool for dashboards, team insights, and embedded analytics ![Draxlr — MySQL reporting tool](/images/blogs/_tools/draxlr.png) #### Best for SaaS teams using MySQL who want a straightforward way to build dashboards, explore data, and offer embedded analytics without introducing a complicated BI stack. Connect your Database [Draxlr](/) works directly with your MySQL database and gives teams a clean interface for creating reports, running SQL, and [building dashboards from MySQL data](/blogs/how-to-build-dashboards-from-mysql-data/) without setup overhead. Built for modern product teams, it helps you ship reporting fast — whether the goal is internal visibility or customer-facing analytics. For technical users, Draxlr offers full SQL control, virtual columns, filters, drill-through features, and database-aware autocomplete that understands your MySQL schema. For nontechnical teammates, the visual query builder and AI text-to-SQL make data exploration accessible without needing to open a SQL editor. If your product includes customer analytics, Draxlr supports [secure embedded dashboards](/blogs/how-to-embed-a-mysql-dashboard-in-your-app/) with row-level filtering, white-label options, and React and Vue components that integrate naturally into your app. Teams can also automate reporting with scheduled emails, Slack alerts, and CSV, Excel, and PDF exports. Draxlr runs on your existing MySQL infrastructure — no pipelines, warehouses, or modeling layers required. Everything connects directly to your live schema. #### Key features 1. Connects directly to MySQL with no ETL or data prep work required 2. Visual Query Builder, full SQL editor, and AI Chat for text-to-SQL 3. Dashboard builder with filters, drill-through, and virtual columns 4. Embedded dashboards with secure tokens, row-level filters, and white-label controls 5. React and Vue SDKs for easy in-product analytics 6. Export options (CSV, Excel, PDF) and scheduled reports 7. Team-level permissions for safe access to production data 8. Supports both internal reporting and customer-facing analytics from the same workspace #### What users say about Draxlr > Draxlr offers a clean, modern interface and makes it incredibly easy to build dashboards — even for non-technical users. I especially like the visual quality and flexibility of the available graph types. It's intuitive, fast to set up, and has excellent usability from the start. The ability to securely share dashboards via link is a game-changer for our client reporting. — [Review from a SaaS Founder on G2](https://www.g2.com/products/draxlr/reviews/draxlr-review-11185807) #### Pricing Draxlr offers multiple plans depending on team size, embedding needs, and data volume. Pricing starts at **$75/month** --- ### 2. Metabase: Best for teams that want quick, no-code dashboards on MySQL ![Metabase — MySQL reporting tool](/images/blogs/_tools/metabase.png) #### Best for Small to mid-sized teams that want fast self-service reporting on MySQL with minimal setup and a friendly interface for nontechnical users. [Metabase](https://www.metabase.com/) is one of the most widely adopted open-source BI tools for MySQL. It connects natively to MySQL databases and provides a question-and-answer interface that allows users to explore data, build dashboards, and share reports without writing SQL. Setup is straightforward, and the open-source version is free to self-host. For teams that need quick wins on internal reporting, Metabase is hard to beat at the entry level. It supports SQL questions for power users while keeping the no-code query builder accessible for everyone else. The tool also supports scheduling and email delivery of reports, making it a common first choice for growing product teams. That said, advanced features like row-level security, white-label embedding, and SSO require paid plans. #### Key features 1. Native MySQL connection with no configuration overhead 2. No-code Question Builder and full SQL editor for mixed-skill teams 3. Scheduled reports and automated email delivery #### What users say about Metabase > A major benefit is the open-source paradigm, with simple setup particularly for PostgreSQL, MySQL, and Snowflake. Fast connection to MySQL databases without needing any complex configuration. #### Pricing Open-source self-hosted version is free. Starter plan starts at **$100/month**. Pro plan with SSO, row-level security, and embedding is **$575/month**. --- ### 3. Looker Studio: Best free MySQL reporting tool for lightweight dashboards ![Looker Studio — MySQL reporting tool](/images/blogs/_tools/looker-studio.png) #### Best for Small teams, marketers, and founders who need a free, easy-to-use reporting interface for MySQL data without committing to a paid BI platform. [Looker Studio](https://lookerstudio.google.com/) (formerly Google Data Studio) provides a native MySQL connector that lets you connect directly to your database and build interactive dashboards through a drag-and-drop interface. It's completely free for individual and team use, making it one of the lowest-friction ways to start reporting on MySQL data. Looker Studio is well-suited for straightforward dashboards, marketing analytics, and operational reporting where teams don't need advanced embedding, row-level security, or programmatic API access. It integrates naturally with other Google Workspace products. For teams that need more governance, workspaces, and IAM-level controls, Looker Studio Pro is available at a modest per-user cost — though Looker Enterprise, the full-featured version for complex BI needs, runs significantly higher. #### Key features 1. Native MySQL connector with live query support and no row limit on queries 2. Drag-and-drop dashboard builder with template variables for dynamic views 3. Free unlimited viewers and scheduled email report delivery #### What users say about Looker Studio > What I like best about Looker Studio is how it helps me track my business data effectively in one single place. With Looker Studio, the data syncs automatically and stays accurate in real time. The drag-and-drop interface is a major plus — I don't need to be a data scientist to build clean, professional reports. #### Pricing Looker Studio is free. Looker Studio Pro starts at **$9/user/project/month**. Looker Enterprise starts at approximately **$60,000/year**. --- ### 4. Grafana: Best for operational and time-series dashboards on MySQL ![Grafana — MySQL reporting tool](/images/blogs/_tools/grafana.png) #### Best for Engineering and DevOps teams that need real-time operational dashboards, metrics monitoring, and time-series visualization directly from a MySQL datasource. [Grafana](https://grafana.com/) ships with a built-in MySQL data source that requires no additional installation. It supports templating, annotations, alerting, and SQL-based panels, making it the standard choice for operational dashboards — especially for teams already using Prometheus, Loki, or other observability tooling. If you need to monitor application health, track service performance, or visualize time-series data from MySQL, Grafana is purpose-built for that use case. Where Grafana is less suited is for business reporting or product analytics aimed at nontechnical users. The interface requires comfort with metrics concepts and SQL, and building polished business dashboards takes more effort compared to purpose-built BI tools. But for engineering teams that live in dashboards, Grafana's flexibility and deep MySQL integration make it a strong fit. #### Key features 1. Native MySQL data source built in — no plugins or installation required 2. Alerting based on MySQL query results with flexible notification routing 3. Extensive visualization library and time-series panel support for operational workloads #### What users say about Grafana > Grafana is a great tool for real-time monitoring. Its intuitive dashboards make it easy to visualise trends and statuses across multiple systems. The most I like about this software is that it's free to use and it's open source — it has a broad plugin ecosystem that can connect to almost anything including MySQL and MariaDB. #### Pricing Open-source version is free to self-host. Grafana Cloud starts at **$19/month** plus usage-based billing for logs, metrics, and traces. --- ### 5. Redash: Best open-source SQL-first reporting tool for MySQL ![Redash — MySQL reporting tool](/images/blogs/_tools/redash.png) #### Best for Data-literate teams that want a collaborative SQL editor, query-based dashboards, and scheduled alerts directly on top of MySQL. [Redash](https://redash.io/) is an open-source tool designed around the workflow of writing SQL, saving queries as visualizations, and combining them into dashboards. It connects natively to MySQL and is a solid choice for teams that prefer a SQL-first approach over visual drag-and-drop interfaces. Queries are versioned, shareable, and reusable across multiple dashboards, which makes it easy for an engineering or data team to collaborate on analytics. Redash is less friendly for nontechnical users than tools like Metabase because it assumes you're comfortable writing SQL. But for teams that already think in queries, it offers fast iteration, flexible visualizations, and strong alerting features on top of your MySQL schema. #### Key features 1. Native MySQL data source with a collaborative SQL editor 2. Saved queries, snippets, and version history for team collaboration 3. Query-based alerts and scheduled refreshes for dashboards #### What users say about Redash > Redash hits the sweet spot for a SQL-first team — you write a query, turn it into a chart, and drop it onto a shared dashboard in minutes. Scheduled refreshes and query alerts on our MySQL data have replaced a lot of manual checking. #### Pricing Open-source self-hosted version is free. Redash's hosted plans have historically started around **$49/month** for small teams, with business tiers available for larger deployments. --- ### 6. Tableau: Best for analyst-driven reporting and complex visualizations on MySQL ![Tableau — MySQL reporting tool](/images/blogs/_tools/tableau.png) #### Best for Organizations with dedicated BI analysts who need sophisticated visualization, complex calculated fields, and enterprise governance connected to a MySQL database. [Tableau](https://www.tableau.com/) has a native MySQL connector and is one of the most capable visualization tools in the BI market. It supports complex data modeling, advanced calculated fields, and a broad library of chart types that go well beyond standard bar and line charts. For teams with analyst resources and a need for polished, deeply customized dashboards, Tableau delivers. The tradeoff is cost and complexity. Tableau is designed for structured BI workflows with dedicated analysts, not for fast self-service by product teams. Getting up to speed requires significant learning time, and the licensing model is built for enterprise deployments. For SaaS teams or small companies looking for quick access to their MySQL data, Tableau is often more than what's needed. #### Key features 1. Native MySQL connector with live query and extract modes 2. Advanced calculated fields, LOD expressions, and complex data modeling 3. Enterprise governance with role-based access and workspace management #### What users say about Tableau > Tableau's drag-and-drop interface is intuitive enough for business users while still being powerful for analysts. It integrates seamlessly with MySQL and other data sources. I especially like how easily Tableau turns complex data into simple, interactive visuals. — G2 Review #### Pricing Creator license starts at **$75/user/month** (billed annually). Explorer and Viewer licenses available at lower price points. --- ### 7. Power BI: Best for organizations already using Microsoft tools ![Power BI — MySQL reporting tool](/images/blogs/_tools/power-bi.png) #### Best for Large organizations that already operate within the Microsoft ecosystem and want to add BI reporting connected to MySQL alongside Excel, Teams, and Azure services. [Power BI](https://www.microsoft.com/en-us/power-platform/products/power-bi) connects to MySQL databases via the built-in Power Query connector and offers a comprehensive feature set for enterprise reporting. It supports DAX expressions, complex data modeling, and integration with the broader Microsoft stack including Azure, Excel, and Teams. For companies already standardized on Microsoft tools, Power BI's ecosystem fit makes it a natural choice. Outside the Microsoft ecosystem, Power BI is less compelling. Its desktop-first workflow, Windows dependency for some features, and enterprise-focused pricing can slow down product teams that need fast, lightweight reporting directly from MySQL. Embedding analytics in a web product also requires premium licensing and additional configuration that simpler tools handle out of the box. #### Key features 1. Native MySQL connector via Power Query for live and imported data models 2. Advanced DAX modeling with a large library of visualization types 3. Deep integration with Microsoft 365, Azure, and Teams for enterprise workflows #### What users say about Power BI > Power BI has transformed the way we approach data by turning complex data into clear real-time insights and actionable strategies. With its powerful visualizations and integration across multiple data sources including MySQL, I've been able to track performance trends and uncover insights with precision. #### Pricing Power BI Desktop is free. Pro plan starts at **$14/user/month**. Premium Per User starts at **$24/user/month**. --- ## Implementing your MySQL reporting tool effectively Choosing the right MySQL reporting tool is only half the job. A tool can give you clean dashboards and powerful querying, but it won't deliver value on its own. Like any part of your product, success depends on how well you implement it and how quickly your team starts using it. Here's how to get the most out of whichever MySQL reporting tool you choose. ### 1. Onboarding your team Invest time in proper onboarding. Developers, product managers, support teams, and anyone responsible for reporting should understand how the tool works from day one. A short, focused onboarding session prevents future bottlenecks and avoids misconfigurations that lead to misleading dashboards or accidental data exposure. ### 2. Start with a pilot Instead of rolling dashboards out to everyone at once, start with a small group. A controlled pilot helps you validate metrics, refine permissions, and confirm that the dashboards you built actually answer the questions teams have. It also reduces the chance of misaligned metric definitions or noisy, unused reports cluttering your workspace. ### 3. Collect feedback continuously Reporting is not a "set it and forget it" feature. Once dashboards go live, actively ask your users — internal or customer-facing — what works and what needs improvement. As your MySQL schema and product evolve, your reporting layer should evolve alongside it. ### 4. Monitor performance and usage Track how often dashboards are viewed, which queries run most frequently, and which reports are ignored. If certain dashboards never get opened, refine them. If certain queries slow down at scale, optimize them or add indexes to support the reporting workload. Understanding how your users interact with analytics helps you measure ROI and improve reporting over time. --- ## Conclusion The MySQL reporting tool you choose becomes part of how your team understands the business. It shapes how founders track growth, how support teams identify issues, and how customers interpret their own data inside your product. It needs to connect reliably to your MySQL schema, serve the right people at the right level of access, and update without manual effort. If you want a reporting layer that works naturally with MySQL without extra infrastructure or data pipelines, tools like Draxlr help you ship dashboards quickly while keeping your stack simple. Whether you need internal visibility, customer-facing analytics, or embedded dashboards inside your product, the right tool should help you move fast without compromising on clarity or reliability. If you'd like to explore a MySQL-native approach to dashboards and reporting, you can [try Draxlr](https://app.draxlr.com/register/) and see how it fits your workflow. --- ## FAQs ### 1. What is a MySQL reporting tool? A MySQL reporting tool connects directly to your MySQL database and lets you build dashboards, charts, and reports without writing SQL for every request. These tools help teams explore live data safely without setting up ETL pipelines or extra infrastructure. ### 2. Can I build customer-facing dashboards with MySQL? Yes. Several reporting tools support embedding dashboards inside your product. Look for options with secure embed tokens, row-level filtering for multi-tenant applications, and styling controls so the dashboards match your product's UI. ### 3. Does MySQL have built-in reporting? MySQL has a command-line client and supports SQL queries, but it does not include dashboards, charts, or shared reporting features. A dedicated reporting tool is required to turn MySQL data into visual insights for your team. ### 4. Is it safe to connect a reporting tool directly to MySQL? Yes, as long as the tool respects MySQL user permissions, uses secure connections (SSL/TLS), and supports user-level filtering when embedding dashboards. Most modern reporting tools offer these safeguards. Avoid tools that require exporting or duplicating your data outside your environment. ### 5. Can non-technical team members use MySQL reporting tools? Yes. Choose a tool that offers visual query builders, saved reports, dashboard filters, and easy sharing so nontechnical users can work with MySQL data without writing SQL. AI text-to-SQL features make this even more accessible for founders and operations teams. ### 6. Do I need a data warehouse for reporting on MySQL? No. Most reporting tools connect directly to MySQL without requiring a separate warehouse. Unless you have extremely large analytical workloads or need cross-database joins across multiple systems, a direct MySQL connection is faster to set up, easier to maintain, and sufficient for most product and business reporting needs.

What Is Embedded Analytics? A Guide for SaaS Teams

hello@draxlr.com (Draxlr) — Wed, 10 Jun 2026 00:00:00 GMT

There are two kinds of SaaS products. Those that show customers their data, and those that make customers go find it somewhere else. The first kind retains better, charges more, and wins more deals. Embedded analytics is what separates them. This guide covers what embedded analytics actually is, why it matters for SaaS specifically, how it works technically, what to look for in an [embedded analytics tool](/embedded-analytics-tool/), and how Draxlr makes it easy to ship without building from scratch. --- ## What Is Embedded Analytics Embedded analytics is the practice of integrating data visualizations, [dashboards](/features/dashboard/), reports, and analytics features directly into a software application. Instead of pointing users toward a separate BI tool or a standalone reporting platform, the analytics experience lives inside your product. When a customer logs into your SaaS product and sees a dashboard showing their usage, revenue trends, or performance metrics without ever leaving the app, that is embedded analytics working correctly. The word "embedded" is doing real work here. It does not just mean an element dropped onto a page. It means the analytics experience is integrated into the user journey, uses the product's authentication and access model, respects the user's data permissions, and matches the look and feel of the rest of the product. For SaaS teams specifically, embedded analytics solves a problem that general BI tools were never designed to address. A SaaS product serves dozens, hundreds, or thousands of customers, each of whom needs to see their own data. General BI tools are built for internal teams analyzing a single dataset. [Embedded analytics tools for SaaS](/blogs/embedded-analytics-for-saas/) are built for multi-tenant products serving many customers simultaneously. --- ## Embedded Analytics vs Traditional Business Intelligence Traditional BI platforms like Tableau, Looker, and Power BI were designed for internal data teams. A company's analysts use them to explore data, build reports, and share insights with executives and department heads. The users are employees. The data is the company's own. The tool can require training, a dedicated login, and a data modeling layer because the people using it are professionals whose job is data analysis. Embedded analytics inverts this model entirely. The users are customers, not employees. The data belongs to each individual customer, not to the company running the product. The tool must feel like part of the product, not a separate application. And the experience must be accessible to someone whose job is not data analysis and who has no patience for a learning curve. This creates four requirements that traditional BI tools were not designed around and that most adapted inadequately to support. 1. **Multi-tenant data isolation.**
In a SaaS product with 500 customers, each customer must only ever see their own data. A logistics company's shipment data cannot be visible to another logistics company. A fintech platform's transaction records cannot bleed across customer accounts. Traditional BI tools handle data security for internal roles. [Embedded analytics tools](/embedded-analytics-tool/) must handle it for external customer boundaries, at scale, reliably. 2. **White labeling.**
When a customer uses an embedded dashboard, they are using the SaaS product. They are not using Draxlr, Tableau, or any other analytics tool. The dashboard should look, feel, and behave as though the product team built it from scratch. That means no third-party logos, no external color schemes, no vendor branding of any kind. Draxlr's [white-label BI solution](/white-label-bi-solution/) is built specifically for this requirement. 3. **Unlimited customer viewers.**
A SaaS product with 500 customers does not know in advance which of those customers will view their embedded dashboard on a given day. Per-viewer pricing models where each customer who views a dashboard is billed as a separate user create costs that compound unpredictably as the product grows. [Embedded analytics pricing](/embedded-analytics-pricing/) built for SaaS uses flat, capacity-based models that decouple the analytics cost from the customer count. 4. **Fast implementation.**
A SaaS team shipping embedded analytics is shipping a product feature, not building a data warehouse. The tool needs to connect to the existing database, generate ready-to-use embed code, and get a dashboard live in the product within days or weeks, not months. If you are evaluating whether to replace an existing tool, the [best embedded analytics tools](/blogs/best-embedded-analytics-tools/) guide compares the leading options purpose-built for SaaS. > **Traditional BI was built for internal teams analyzing company data. Embedded analytics is built for SaaS products serving external customers who each need their own view of their own data.** --- ## How Embedded Analytics Works At a technical level, embedded analytics involves three stages and those are data connection, dashboard creation, and embed delivery. ### 1. Data connection The embedded analytics tool connects to the SaaS product's database. This is typically a direct connection to a SQL database like [PostgreSQL](/databases/postgres/), [MySQL](/databases/mysql/), [Redshift](/databases/redshift/), [BigQuery](/databases/bigquery/), [Snowflake](/databases/snowflake/), or similar. The connection allows the tool to run queries against live data and return results in real time. ### 2. Dashboard creation The product team builds dashboards using the analytics tool's interface. This might involve a drag-and-drop builder for non-technical team members, a SQL editor for developers, or an AI-powered query tool that generates SQL from plain English. The result is a set of charts, tables, and metrics arranged into a dashboard. ### 3. Embed delivery The embedded analytics tool generates an embed token or code snippet. This token authenticates the request, enforces the data filters for the specific customer viewing the dashboard, and returns the rendered dashboard to the host application. The host application renders the dashboard inside the product. The customer opens the product, sees a dashboard that looks like part of the application, and interacts with their data. They apply filters, drill into specific records, export a report. None of this requires them to know that an embedded analytics tool is doing the work underneath. ![Embedded analytics flow: from database through Draxlr to embed token to SaaS product to end customer](/images/blogs/what-is-embedded-analytics/embedded-analytics-flow.svg) The embed token is where multi-tenant security is enforced. When the SaaS product generates an embed request for a specific customer, it includes that customer's identifier. The embedded analytics tool uses this to filter the data before returning anything. Customer A's token returns only Customer A's data. Customer B's token returns only Customer B's data. This filtering happens at the query level, not the display level, which means the isolation is enforced even if a customer attempts to manipulate the URL or interface. --- ## What Embedded Analytics Requires for SaaS Products Understanding the mechanics is different from understanding what a SaaS team actually needs to ship embedded analytics successfully. The requirements go beyond the technical. 1. **A direct database connection with no middleware.** The [best embedded analytics tools](/blogs/best-embedded-analytics-tools/) connect directly to the existing production database or data warehouse. Tools that require a separate ETL pipeline, a data modeling layer, or a custom API as an intermediary add weeks to the implementation and ongoing engineering overhead. 2. **Tenant-level data filtering that requires no custom code per customer.** Manually writing filter logic for each new customer is not scalable. The right tool lets the product team define a parameterized filter once, for example a customer ID field, and applies it automatically to every embed request for that customer. New customers are added without any additional engineering. 3. **White labeling from the first paid plan.** For many SaaS teams, white labeling is not a nice-to-have. It is a requirement. A dashboard that displays third-party branding breaks the product experience, confuses customers, and undermines trust in the product. Tools that gate white labeling behind an enterprise tier force teams to either pay a premium or ship a sub-standard experience. 4. **Unlimited or flat-rate customer viewers.** Per-viewer pricing is the most common cause of embedded analytics budget problems. A tool that seems affordable at 50 customers becomes expensive at 500 and unsustainable at 5,000. SaaS teams planning for growth need a pricing model where the analytics cost does not scale linearly with the customer base. 5. **Embed options that match the product's architecture.** A product built in React needs a React SDK. A product that renders server-side needs an API. The embedded analytics tool must support the host product's frontend architecture, not require the product to change its architecture to accommodate the tool. --- ## Benefits of Embedded Analytics for SaaS Teams Shipping embedded analytics affects the product in ways that extend beyond the feature itself. ### 1. Reduced churn Customers who regularly engage with data inside a product have a much stronger attachment to that product. The data becomes an asset they rely on. Moving to a competitor means losing that history, that context, and that visibility. Analytics creates switching costs that benefit retention. ### 2. Increased perceived value A product that surfaces actionable insights alongside the core workflow is a more valuable product than one that requires customers to export data and analyze it elsewhere. Embedded analytics raises the price ceiling for the product and justifies higher subscription tiers. ### 3. Fewer support requests A significant proportion of customer support queries are data questions like how many of X happened last month, what changed between these two dates, why is this number different from last week. When customers can answer these questions themselves through an embedded dashboard, the support load decreases. ### 4. Competitive differentiation Most SaaS products in a given category offer roughly equivalent core functionality. Embedded analytics gives customers a reason to choose one product over another that goes beyond features and price. It changes the product from a tool that processes data to a platform that generates insight. --- ## Who Uses Embedded Analytics Embedded analytics appears across nearly every SaaS category. The specific implementation varies but the core pattern is consistent. Customer-specific data surfaced inside the product without requiring the customer to leave. - **Operations and logistics SaaS.** Shipping and delivery platforms embed dashboards showing order volume, delivery times, carrier performance, and exception rates. Each customer sees their own operational data with filters for date range, geography, carrier, and status. - **Fintech and payment platforms.** Payment processing and financial management tools embed revenue dashboards, transaction breakdowns, dispute analysis, and reconciliation reports. Customers use these to manage cash flow and report to their own stakeholders without exporting to spreadsheets. - **HR and workforce management.** HR platforms embed headcount dashboards, turnover analysis, hiring funnel metrics, and compensation benchmarks. These are particularly valuable for customers who need to present workforce data to leadership without involving the HR team for every report. - **E-commerce and retail SaaS.** E-commerce platforms embed product performance, customer cohort analysis, revenue attribution, and inventory dashboards. Merchants use these to make buying, merchandising, and marketing decisions within the platform. - **Developer tools and infrastructure SaaS.** API platforms, monitoring tools, and infrastructure products embed usage dashboards showing request volume, error rates, latency percentiles, and cost attribution. These are often the primary way customers understand their own usage patterns. - **Healthcare and compliance SaaS.** Patient management, compliance tracking, and clinical workflow tools embed reporting dashboards for regulatory submissions, outcome tracking, and operational metrics. The multi-tenant isolation requirement is particularly critical in this category given data privacy regulations. ![Draxlr embedded analytics dashboard inside a SaaS product showing response trends, team standup data, and follow-up breakdowns](/images/blogs/what-is-embedded-analytics/sup-standup-bot.png) _A Draxlr embedded analytics dashboard inside Sup, asynchronous standup tool, showing response trends, happiest day, total responses, and a breakdown by follow-up type and team member. The Draxlr dashboard renders natively inside the Sup product with no separate login required for Sup users._ --- ## How Much Does Embedded Analytics Cost [Embedded analytics pricing](/embedded-analytics-pricing/) varies more than almost any other SaaS tool category. The range runs from $75/month for purpose-built tools to $250,000 per year for enterprise platforms. The variation is explained by pricing model, not by features. Most of the capabilities that matter for a SaaS embedded analytics implementation are available at the affordable end of the market. What enterprise pricing buys is primarily organizational support, compliance certifications, and the brand name on the contract. The five pricing models in the category work as follows. 1. **Flat pricing** charges a fixed monthly fee regardless of how many customers view dashboards, how many queries are run, or how much data is processed. This is the most predictable model for SaaS teams because the analytics cost does not change as the product scales. [Draxlr](/) uses flat pricing starting at $75/month. 2. **Per-seat pricing** charges for each user who accesses embedded content. For internal analytics where the user count is fixed and known, this model is predictable. For customer-facing embedded analytics where user counts grow with the product, it compounds quickly. Metabase Pro starts at $575/month and charges $12/month per user for interactive embedding. A product with 500 customers accessing interactive embedded dashboards pays $6,000/month in viewer fees alone before any infrastructure costs. 3. **MAU-based pricing** charges based on the number of Monthly Active Users who interact with embedded dashboards. Costs scale with customer engagement, which creates a perverse incentive where a successful product that drives more customer analytics usage generates a higher bill. Luzmo uses MAU-based pricing starting at €495/month billed annually. 4. **Capacity-based pricing** charges for compute capacity rather than users or queries. Power BI Embedded uses Azure A-SKU capacity pricing starting at approximately $735/month for the A1 SKU. Costs increase when more concurrent users require higher capacity. This model is more predictable than per-seat but still requires capacity planning and can spike under unexpected load. 5. **Consumption-based pricing** charges for actual usage like queries run, data processed, dashboard sessions initiated. Domo uses a credit-based consumption model with no published pricing. Costs are highly variable and have surprised customers with significant increases at renewal. For a full comparison of embedded analytics pricing across multiple tools, see the [embedded analytics pricing comparison](https://www.draxlr.com/embedded-analytics-pricing/). > **The most common pricing mistake SaaS teams make is choosing an embedded analytics tool based on its advertised entry price without modeling what it costs at 500 customers. A $10/month entry price with per-viewer fees can cost more than a $75/month flat-rate tool within three months of launch.** --- ## What to Look for When Choosing an Embedded Analytics Tool Not all embedded analytics tools are built for the same use case. Some are designed for enterprise data teams that also need an embedding option. Others are built specifically for SaaS products that need customer-facing analytics. Knowing what to look for narrows the field quickly. ### Multi-tenant support built in, not bolted on Ask specifically how tenant data isolation is enforced. Some tools handle this natively with parameterized filters that apply at the query level. Others require custom implementation per customer, which adds engineering time for every new account. ### White labeling included at the tier you are actually buying. Do not assume white labeling is available because the pricing page lists it as a feature. Check which tier includes it. Several tools advertise white labeling but gate it behind a tier that costs five to ten times the entry price. ### Pricing model that does not punish growth. Model the cost at 100 customers, 500 customers, and 1,000 customers using the tool's pricing model. If the cost at 500 customers is unacceptable, the tool is not the right fit regardless of how it looks at 10 customers. ### Direct database connectivity. If the tool requires a custom API, a data transformation layer, or a specific cloud data warehouse to function, add the implementation cost and ongoing maintenance of that infrastructure to the total cost of ownership. ### Embed options that match your stack. Confirm the tool supports your frontend framework before evaluating anything else. A React SDK is not interchangeable with an iframe. A backend API requires different development effort than a pre-built component. ### A free trial with no credit card required. Any embedded analytics tool worth evaluating should let the product team connect a real database, build a real dashboard, and test the embed in a staging environment before committing to a subscription. If a sales call is required before pricing or trial access is available, that is a signal about how the relationship will continue. --- ## Build vs Buy Every SaaS team evaluating embedded analytics eventually asks whether to build a custom charting layer instead of using a third-party tool. The build option is appealing because it eliminates vendor dependency, gives full control over the UI, and avoids ongoing subscription costs. The honest case for building is narrow. It makes sense when the analytics requirements are simple and unlikely to expand, when the product team has dedicated frontend and data engineering capacity, and when the long-term roadmap does not require features like drill-down, multi-tenant filtering, or white-labeled PDF exports. In practice, the scope of a custom-built analytics layer almost always expands beyond its original definition. Customers ask for new chart types, new filters, new export formats, new date range comparisons. Each request requires engineering time. The backlog of analytics feature requests competes with the core product roadmap. The opportunity cost is usually not recognized until the team is six months into maintaining a charting system they never intended to build. The cost comparison is rarely close. A custom embedded analytics implementation requires initial development time of four to twelve weeks depending on complexity, plus ongoing maintenance as requirements grow. At a conservative $150/hour fully loaded engineering cost, a twelve-week build is $60,000 before the first dashboard is live. An embedded analytics tool at $75/month is $900/year. The build option is economically rational only at very high scale or with very specific requirements that no existing tool can meet. For most SaaS teams the question is not build vs buy. It is which tool to buy and how quickly the team can ship the feature. > **The real cost of building custom embedded analytics is not the initial development. It is the ongoing engineering time maintaining and extending a feature that is adjacent to but not part of the core product.** --- ## AI and the Future of Embedded Analytics The most significant shift in embedded analytics over the past two years has been the introduction of natural language interfaces. Instead of customers navigating pre-built dashboards, they can ask questions in plain English and receive chart responses generated from their data in real time. This changes the relationship between the customer and their data. A pre-built dashboard answers questions the product team anticipated. A natural language interface answers questions the product team did not anticipate, which is the vast majority of the questions customers actually have. For SaaS teams evaluating embedded analytics tools today, AI capability is worth considering but should not be the primary evaluation criterion. The foundational requirements, covering multi-tenancy, white labeling, flat pricing, and fast implementation, matter more than AI features for most teams at the stage where they are first shipping embedded analytics. AI features are additive once the core embedded analytics layer is working. What is more immediately relevant is AI-assisted dashboard creation. Several embedded analytics tools now include [AI SQL tools](https://www.draxlr.com/blogs/best-ai-sql-tools/) that generate queries from plain English, which reduces the engineering dependency for building and updating dashboards. This matters for product teams where non-technical members need to own dashboard creation and iteration. --- ## How to Get Started Getting embedded analytics into a SaaS product involves five practical steps regardless of which tool is chosen. - **Step 1 — Define the customer data questions.** Before evaluating tools, write down the five to ten questions your customers most frequently ask about their own data. These become the first dashboards. Starting with real customer questions rather than generic metrics produces dashboards that get used. - **Step 2 — Map the data.** Identify which tables and columns in the existing database answer those questions. Note which columns contain customer identifiers that will be used for multi-tenant filtering. If the data lives in multiple sources, decide whether to consolidate it before starting or start with a single source and expand later. - **Step 3 — Choose the embed method.** Decide whether the product will use iframe, HTML, React SDK, Vue SDK, or backend API embedding based on the product's existing frontend architecture. This decision affects which tools are viable and how long implementation will take. - **Step 4 — Start a free trial with real data.** Connect the actual production database or a staging copy to the tool. Build one of the dashboards from Step 1. Configure the multi-tenant filter. Generate the embed code and paste it into a staging environment. Evaluate the output against the real customer experience, not against a demo dataset. - **Step 5 — Define the customer experience.** Before launching, define what the customer will see, how they will interact with it, and what they will be able to do. The technical implementation should serve a defined customer experience, not the other way around. For a practical breakdown of how to choose between tools based on your specific stack, budget, and customer requirements, see the [embedded analytics tool comparison](https://www.draxlr.com/embedded-analytics-tool/). For teams specifically evaluating cost, the [affordable embedded analytics tool guide](https://www.draxlr.com/affordable-embedded-analytics-tool/) covers the pricing options in detail. --- ## Next Steps: Start Embedding Analytics in Your Product Start a 7-day free trial with no credit card required and have your first embedded dashboard live with Draxlr. Connect your Database ### Watch: How Embedded Analytics Works in Draxlr --- ## FAQs ### 1. What is embedded analytics? Embedded analytics means putting data dashboards inside a software product so users can see and interact with their data without leaving the application. Instead of logging into a separate reporting tool or downloading a spreadsheet, users see live charts and tables built into the product they already use. ### 2. What is the difference between embedded analytics and business intelligence? Business intelligence tools are designed for internal teams analyzing company-wide data. Embedded analytics is designed for customer-facing products where each customer needs to see their own data inside the application. The key differences are multi-tenant data isolation, white labeling, and the requirement that the experience feels native to the host product rather than like a third-party tool. ### 3. What does an embedded analytics tool do? An embedded analytics tool connects to a database, lets teams build dashboards, and generates embed code that renders those dashboards inside another application. It handles data security, query execution, visualization rendering, and white labeling so that the product team does not need to build these capabilities from scratch. ### 4. How much does embedded analytics cost? Embedded analytics pricing ranges from $75/month for purpose-built SaaS tools to $250,000 per year for enterprise platforms. The most significant cost variable is the pricing model. Flat-rate tools like Draxlr charge a fixed monthly fee regardless of customer count. Per-seat tools like Metabase charge $12/month per user for interactive embedding. Capacity-based tools like Power BI Embedded start at approximately $735/month on Azure. See the full [embedded analytics pricing comparison](https://www.draxlr.com/embedded-analytics-pricing/) for verified figures across 12 tools. ### 5. What is multi-tenant embedded analytics? Multi-tenant embedded analytics means each customer in a SaaS product sees only their own data when they view an embedded dashboard. The tool enforces data boundaries at the query level using customer-specific filters. Customer A never sees Customer B's data. This is a fundamental requirement for any customer-facing SaaS product and should be verified as a built-in capability, not a custom implementation, before choosing a tool. ### 6. What is white label embedded analytics? White label embedded analytics means the embedded dashboards show no vendor branding. The dashboards appear to be built by the SaaS team. No third-party logos, no external color schemes, no "powered by" badges. White labeling is available from the entry pricing tier on some tools and gated behind enterprise pricing on others. Draxlr includes white labeling in the Premium plan at $75/month. ### 7. Can I embed analytics without a data engineering team? Yes, with the right tool. Purpose-built embedded analytics tools like Draxlr connect directly to SQL databases without requiring a data modeling layer or ETL pipeline. Non-technical team members can build dashboards using a drag-and-drop builder or an AI SQL tool. Enterprise tools require more data engineering investment before the first dashboard can be embedded. ### 8. What is the difference between embedded analytics and data visualization? Data visualization refers to the charts and graphs used to represent data. Embedded analytics refers to the full system of connecting to data, building visualizations, enforcing data security, and delivering the output inside a host application. Data visualization is one component of embedded analytics. Embedded analytics also includes query execution, multi-tenant security, embed delivery, and white labeling. ### 9. What should I look for in an embedded analytics tool for a SaaS product? The four most important criteria for SaaS teams are multi-tenant data isolation built in without custom code per customer, white labeling available at the plan you are actually buying, a pricing model that does not scale with customer count, and a free trial that lets you test with real data before committing. Secondary criteria include embed method support for your frontend framework, direct database connectivity without middleware, and implementation time to first live dashboard. See the [best embedded analytics tools comparison](https://www.draxlr.com/blogs/best-embedded-analytics-tools/) for a detailed breakdown across eight tools.

Best BlazeSQL Alternatives in 2026

hello@draxlr.com (Draxlr) — Mon, 08 Jun 2026 00:00:00 GMT

BlazeSQL is a natural language SQL tool that lets you query your database by typing a plain English question. It is clean, focused, and easy to get started with. But depending on your team size, the complexity of your data model, or the features you need beyond just generating SQL, it may not be the right long-term fit. If you are evaluating an [AI SQL tool](/blogs/best-ai-sql-tools/) in 2026, this guide covers the best BlazeSQL alternatives with enough detail to help you make the right call. Each AI SQL tool below is evaluated on text-to-SQL accuracy, database connectivity, visualization, collaboration, and pricing. --- ## What makes a good AI SQL tool ### 1. Schema-aware query generation The AI should read your actual database schema before generating any SQL. Tools that pass your question to a generic language model with no context about your tables and columns produce queries that fail or return wrong results. The best tools index your table names, column types, and relationships so the SQL is grounded in your real data. Our guide on [natural language to SQL](/blogs/natural-language-to-sql/) covers how this works in practice. ### 2. End-to-end workflow from question to dashboard A strong AI SQL tool should take you from a plain English question all the way to a chart pinned on a shared [dashboard](/features/dashboard/). Tools that stop at SQL generation put the remaining visualization and sharing work back on you. ### 3. Direct database connectivity Your tool should connect straight to your existing databases without requiring a separate data layer or ETL pipeline. [Direct connections](/blogs/sql-data-visualization/) to PostgreSQL, MySQL, Snowflake, BigQuery, and others keep your stack simple and your queries fast. ### 4. Sharing and scheduled delivery From [public dashboard URLs](/blogs/create-a-public-dashboard-url-for-your-sql-database/) to [email and Slack alerts](/blogs/alerts-for-sql-data/), the ability to get data to people without them logging in every time separates a personal query tool from a team analytics platform. ### 5. Transparent pricing AI SQL tools vary widely in cost. Tools that publish what you pay before a sales conversation make it much easier to shortlist options without committing time to demo cycles. ### 6. Data security and access control Any AI SQL tool that connects to production databases needs to handle credentials securely, support role-based access controls, and ideally offer options like SSO, audit logs, and on-premise or VPC deployment for regulated industries. If your data cannot leave your infrastructure, confirm whether the tool supports self-hosting before evaluating further. ### 7. Embedded analytics support If you are building a product and need to give customers access to their own data, check whether the tool supports [embedding](/features/embedding/) with row-level security and multi-tenant filtering. Not every AI SQL tool is built for this, but for product teams it is often the deciding factor. --- ## Best BlazeSQL alternatives in 2026 at a glance

Tool	Best for	Ease of use	AI capabilities	Starting price
Draxlr	AI SQL querying with dashboards and embedded analytics	High	Strong	$25/month
ThoughtSpot	Enterprise AI search analytics	High	Strong	From $50/user/month (minimum 25 users, billed annually)
Metabase	Open-source BI with AI query assistance for teams	High	Good	Open-source, Cloud from $500/month
Chat2DB	AI database client for developers	High	Good	$8/user/month
Vanna AI	Self-improving text-to-SQL for data teams	Medium	Strong	$50/month

--- ## 1. Draxlr ### Best BlazeSQL Alternative for Teams That Want AI SQL Querying with Full Dashboard and Embedding Support [Draxlr](/) is an AI SQL tool that lets you ask a question in plain English and get back a real answer from your database, not just a SQL query to run yourself. You type what you want to know, and Draxlr figures out the tables, writes the SQL, runs it, and shows you the result as a chart or table you can share or pin to a dashboard. The [AI](/features/AI/) reads your actual schema before generating any query. It indexes your table names, column types, and relationships so the SQL it produces is grounded in your real data model. You can also use the [visual query builder](/features/query-builder/) to filter and aggregate without writing anything, or drop into the full SQL editor if you want complete control. Both technical and non-technical users can get answers on their own. Draxlr connects directly to SQL databases including [PostgreSQL](/databases/postgres/), [MySQL](/databases/mysql/), [Snowflake](/databases/snowflake/), [BigQuery](/databases/bigquery/), [Redshift](/databases/redshift/), [ClickHouse](/databases/clickhouse/), [Databricks](/databases/databricks/), and more. You connect, query, and visualize from your existing setup with no ETL pipeline or data migration needed. For teams building customer analytics into their product, Draxlr supports [embedded dashboards](/blogs/best-embedded-analytics-tools/) via iframe, React SDK, Vue SDK, and backend APIs. You can filter data per customer and apply row-level security for multi-tenant setups. Connect your Database ### Key Features - Text-to-SQL AI that reads your real schema and generates accurate, executable queries - AI SQL assistant that writes, explains, and fixes queries in the editor - Visual query builder for filtering, sorting, joining, and aggregating data across tables - Full SQL editor for analysts and engineers who need complete query control - Multiple chart types with flexible visualization options - Dashboard sharing via public URLs, team workspaces, and scheduled email or Slack delivery - [Automated data alerts](/features/alerts-and-automations/) triggered by threshold crossings or value changes - [Embedded analytics](/embedded-analytics-tool/) via iframe, React SDK, and Vue SDK with per-customer filtering and row-level security - [White-label](/white-label-bi-solution/) and [self-hosted](/self-hosted-bi-tool/) options for full branding and infrastructure control - Transparent pricing from $25/month with no credit card required to get started ### Pricing Draxlr offers a free trial with no credit card required. [Paid plans](/pricing/) start at $25/month and include AI querying. [Embedding](/embedded-analytics-pricing/) starts at $75/month with unlimited customer users included. Enterprise and self-hosted plans are available for teams that need SSO, dedicated support, or full infrastructure control. --- ## 2. ThoughtSpot ### Best BlazeSQL Alternative for Enterprise-Scale Natural Language Analytics ![BlazeSQL Alternative - ThoughtSpot](/images/blogs/yellowfin-bi-alternatives-2026/thoughtspot.png) ThoughtSpot is built around the idea that anyone in an organization should be able to get answers from data by typing a question, the same way you would run a search. Its natural language search sits on top of your cloud data warehouse and returns charts and analysis without the user needing to write SQL or build a dashboard manually. For large organizations that have standardized on Snowflake, Databricks, or BigQuery and need to democratize data access at scale, ThoughtSpot is one of the strongest options available. If you have already evaluated ThoughtSpot and want to compare it further, our [ThoughtSpot alternatives guide](/blogs/thoughtspot-alternatives-2026/) covers how it stacks up against other AI analytics platforms. ### Key Features - Natural language search that returns charts and answers from a plain English question - AI agents for automated analysis, trend spotting, and follow-up exploration - Live query to Snowflake, Databricks, BigQuery, and other cloud warehouses - SpotIQ automated insight detection across large datasets - Embedded analytics for product teams with multi-tenant row-level security - Enterprise governance and access control at scale ### Pricing ThoughtSpot publishes its pricing. The Pro plan starts at $50/user/month with a minimum of 25 users and is billed annually. The embedding plan is contact-sales only. For small teams or teams without a minimum of 25 users, the entry cost is high. --- ## 3. Metabase ### Best BlazeSQL Alternative for Teams That Want Open-Source BI with AI Query Assistance ![BlazeSQL Alternative - Metabase](/images/blogs/yellowfin-bi-alternatives-2026/metabase.png) Metabase is one of the most widely used open-source BI tools available. It lets anyone on your team ask questions about data in plain English through its natural language interface, or use the visual question builder to explore tables without writing SQL. For teams that already know SQL, there is a full editor with autocomplete and query history. The result is a tool that works for both technical and non-technical users from the same interface. Metabase connects to most common databases including PostgreSQL, MySQL, Snowflake, BigQuery, Redshift, and others. You can publish dashboards, set up scheduled email or Slack delivery, and embed charts in other tools. The open-source version is free to self-host, which makes it one of the most cost-effective options for teams that want full control over their data and infrastructure. ### Key Features - Natural language question interface that maps plain English to your actual schema - Automated data summaries that describe what changed and why - Visual query builder for filtering, grouping, and summarizing data - Full SQL editor with autocomplete and saved question history - Connects to PostgreSQL, MySQL, Snowflake, BigQuery, Redshift, and more - Dashboard sharing, scheduled reports, and email or Slack delivery - Embedding support for surfacing data inside other applications ### Pricing Metabase is free to self-host as an open-source tool. The managed Cloud plan starts at $500/month. Enterprise pricing is available for larger teams with advanced governance needs. --- ## 4. Chat2DB ### Best BlazeSQL Alternative for Developers Who Want an AI-Powered Database Client ![BlazeSQL Alternative - Chat2DB](/images/blogs/best-ai-sql-tools/chat2db-screenshot.png) Chat2DB is an AI-powered database client that combines the familiar interface of a tool like DBeaver or TablePlus with a built-in natural language to SQL layer. You connect to your database, browse schemas, and ask questions in plain English or write SQL directly. The AI is context-aware and reads your schema before generating queries, which keeps accuracy reasonably high for most standard query patterns. It is primarily built for developers and data engineers who live in a database client all day and want AI assistance woven into that workflow rather than accessed through a separate chat tool. ### Key Features - AI text-to-SQL grounded in your connected schema - Full database client with schema browsing, table inspection, and query history - Support for MySQL, PostgreSQL, Oracle, SQL Server, SQLite, ClickHouse, and more - SQL editor with autocomplete and AI-assisted query generation - Query explanation and optimization suggestions - Cross-platform desktop client and web version ### Pricing The paid AI plan starts at $8/user/month with 3,000 AI requests per month. Team plans are available at higher tiers. --- ## 5. Vanna AI ### Best BlazeSQL Alternative for Data Teams That Want a Self-Improving Text-to-SQL Layer ![BlazeSQL Alternative - Vanna AI](/images/blogs/best-ai-sql-tools/vanna-screenshot.png) Vanna AI takes a different approach to text-to-SQL than most tools. Instead of relying purely on the language model to understand your schema, it builds a retrieval-augmented system on top of your actual query history. You train Vanna on your past SQL queries, DDL statements, and data documentation, and over time the model gets better at your specific data model, terminology, and business logic. The more you use it and the more you train it, the more accurate it gets. This makes Vanna particularly well-suited for data teams that have complex, heavily customized schemas with specific naming conventions and business definitions that a generic language model would not know. It is open-source at its core, so technical teams can self-host and customize the retrieval layer. For a comparison of how different AI querying approaches work, see our [AI SQL tool guide](/blogs/ai-sql-tool/). ### Key Features - Retrieval-augmented text-to-SQL that improves with training on your query history - Open-source Python library for self-hosting and customization - Support for PostgreSQL, MySQL, BigQuery, Snowflake, DuckDB, and more - Connects to Jupyter notebooks, Slack bots, or any Python environment - Business-logic-aware SQL generation trained on your domain - Cloud-hosted option for teams that do not want to manage infrastructure ### Pricing Vanna AI is open-source and free to self-host. The managed cloud plan is available at a starting price of $50 per month for 2-3 people. --- ## How to choose the right BlazeSQL alternative If you need a complete AI analytics platform that goes from text-to-SQL to shareable [dashboards](/features/dashboard/), [embedded analytics](/embedded-analytics-tool/), and alerting, [Draxlr](/) is the strongest fit. It covers everything BlazeSQL leaves open: visualization, team sharing, scheduled delivery, and embedding, all starting at $25/month with transparent pricing. If your organization has already standardized on a cloud data warehouse and needs AI analytics at enterprise scale, ThoughtSpot is built for that deployment model. The minimum user commitment makes it less accessible for smaller teams. If you want open-source BI with a plain English question interface and the option to self-host for free, Metabase is one of the most widely deployed tools in this space. The managed cloud plan is expensive, but the self-hosted version costs nothing. If you are a developer who lives in a database client and wants AI assistance woven into that workflow, Chat2DB is worth trying. Its pricing is low and it handles a wide range of database types. If your data model is highly customized and you want an AI layer that improves as it learns your specific schema and query patterns, Vanna AI is worth exploring, especially if your team is comfortable with Python and self-hosting. If data security and self-hosting are a hard requirement, Metabase, Draxlr, and Vanna AI all support running within your own infrastructure. --- ## Conclusion BlazeSQL is a well-designed, focused AI SQL tool for generating SQL from plain English. For many individuals and small teams, it gets the job done. The reasons teams look elsewhere are practical like they need dashboards, alerts, team sharing, embedded analytics, or a deeper integration with a specific data stack that BlazeSQL does not cover. All of the alternatives above connect directly to SQL databases, so the switching cost is lower than it looks. The main effort is deciding which capabilities matter most for your specific workflow. If you want a broader comparison of the category, our [best AI SQL tools](/blogs/best-ai-sql-tools/) guide and [AI-powered BI tools](/blogs/ai-powered-bi-tools/) guide cover the full landscape. --- ## FAQs ### 1. What are the best BlazeSQL alternatives in 2026? The best BlazeSQL alternatives in 2026 are Draxlr, ThoughtSpot, Metabase, Chat2DB, and Vanna AI. The right choice depends on whether you need end-to-end dashboards, embedded analytics, open-source self-hosting, a developer-focused database client, or a self-improving text-to-SQL layer. ### 2. Why do teams switch from BlazeSQL? The most common reasons are the lack of built-in dashboard and visualization support, no alerting or scheduled reports, limited collaboration features for teams, and no embedded analytics for product teams that need to surface data inside their own applications. ### 3. Which BlazeSQL alternative is best for non-technical users? Draxlr and ThoughtSpot are the best for non-technical users. Both let business users ask questions in plain English and get chart results without writing SQL. Draxlr is more accessible for smaller teams given its pricing. ### 4. Which BlazeSQL alternative has the best AI features? Draxlr, ThoughtSpot, and Vanna AI lead on AI quality. Draxlr grounds its SQL generation in your real schema and handles the full workflow to dashboards. ThoughtSpot offers the most polished natural language search experience for large organizations. Vanna AI improves over time by learning your specific query history. ### 5. Which BlazeSQL alternative is best for product teams that need embedded analytics? Draxlr is built for this. It supports embedding via iframe, React SDK, and Vue SDK with per-customer data filtering and row-level security. Embedding starts at $75/month with flat pricing and no per-viewer fees. ThoughtSpot also supports embedding but at a significantly higher cost. ### 6. Is it hard to migrate from BlazeSQL? Not particularly. Most alternatives connect directly to your existing SQL databases with no data migration required. The main effort is reconnecting your databases and rebuilding any saved queries or dashboards you rely on in your new tool.

Best Bold BI Alternatives in 2026

hello@draxlr.com (Draxlr) — Fri, 05 Jun 2026 00:00:00 GMT

Finding the right Bold BI alternative can change how your team builds dashboards, runs analytics, and gets data into the hands of people who need it. Bold BI is a capable platform, but it is not the right fit for every team. Pricing transparency, setup complexity, SQL flexibility, and ease of use for non-technical users all come up regularly when teams start looking elsewhere. If you are evaluating your options in 2026, this guide covers the best alternatives with enough context to help you make the right call for your situation. --- ## Why consider a Bold BI alternative? Bold BI has a solid feature set and a high overall satisfaction rating across review platforms. That said, a consistent set of frustrations shows up in G2, Capterra, and Gartner Peer Insights reviews. If any of these sound familiar, it may be worth looking at what else is out there. ### 1. Data source schema changes are clunky Once you create a data source in Bold BI, adding new fields later means going back into the configuration manually. Users flag this regularly, noting that you need to think through your data model upfront. If your schema changes down the line, the update process feels cumbersome rather than straightforward. Teams working with fast-moving datasets tend to feel this friction most. ### 2. Performance degrades with large datasets This is the most common technical complaint across review platforms. Dashboards that load quickly at first often become noticeably slow as data volumes grow. Getting performance back requires deliberate optimization work on your end. Teams that have scaled into Bold BI over time tend to notice this more than those who started with large data from the beginning. ### 3. Specific visualization gaps Bold BI has 40+ chart types, but users point to specific missing capabilities. Bar charts cannot show a different color per category in a single-series view, and map visualizations are described as limited for many real-world use cases. These are not edge cases. When a chart type cannot do what you need it to do, you end up working around it rather than with it. ### 4. No published pricing Bold BI does not list pricing publicly. Plans are custom-quoted based on deployment type and user count, with a 30-day free trial available. If you want to know whether it fits your budget before getting on a sales call, you cannot. That adds friction when you are comparing multiple tools at the same time. ### 5. Mobile experience lags behind desktop Bold BI has a mobile app, but reviewers note that it does not match the desktop experience in terms of polish and interaction quality. For teams where stakeholders check dashboards on their phones, this gap is noticeable. --- ## What to look for in a Bold BI alternative ### 1. AI-powered querying The tools worth considering in 2026 let non-technical users ask questions in plain English and get answers without writing SQL. A solid [text-to-SQL layer](/blogs/natural-language-to-sql/) removes the analyst bottleneck that most BI teams run into as the organization grows. ### 2. Direct database connectivity Your alternative should connect straight to your existing databases whether that is PostgreSQL, MySQL, BigQuery, Snowflake, or Redshift, without needing a separate data layer or ETL pipeline in between. ### 3. Transparent pricing If you cannot see the price on the website, budgeting becomes guesswork. Look for a tool with published tiers so you know what you are getting into before the sales process starts. ### 4. Sharing and alerts From [public dashboard URLs](/blogs/create-a-public-dashboard-url-for-your-sql-database/) to team sharing and scheduled [email and Slack alerts](/blogs/alerts-for-sql-data/), the ability to get data to people without them logging in every time matters more than most teams expect. ### 5. Visualization flexibility A good [SQL data visualization](/blogs/sql-data-visualization/) tool should support the chart types you actually need and let you customize them without jumping through hoops. ### 6. Performance at scale Check how each option handles large datasets and concurrent users before committing. Slow dashboards frustrate users and erode confidence in the data. ### 7. Support quality and product pace A team that responds quickly and ships regularly matters. Smaller, focused vendors often move faster and give you better access than large enterprise platforms do. --- ## Best Bold BI alternatives in 2026 at a glance

Tool	Best for	Ease of use	AI capabilities	Starting price
Bold BI	Enterprise BI and dashboards	Medium	Moderate	Custom (quote-based)
Draxlr	AI-powered self-service BI for SQL teams	High	Good	From $25/month
Tableau	Enterprise visualization	Medium	Moderate	From $15/user/month (billed annually)
Microsoft Power BI	Microsoft ecosystem analytics	Medium	Moderate	$14/user/month (billed annually)
ThoughtSpot	AI-powered search and natural language analytics	High	Strong	From $50/user/month (atleast 25 users, billed annually)
Metabase	Simple internal dashboards	High	Good	Free or $500/month
Qlik Sense	Self-service analytics for large enterprises	Medium	Moderate	From $300/month

--- ## 1. Draxlr ### Best Bold BI Alternative for Teams That Want AI-Powered Self-Service Analytics ![Bold BI Alternative - Draxlr](/images/blogs/gooddata-alternatives-embedded-analytics/images/draxlr.png) [Draxlr](/) is a SQL-native, AI-powered tool built for teams that want to move from database connection to working dashboard without a heavy setup process or a steep learning curve. Both data engineers and non-technical business users can get answers on their own without routing every question through a developer. You get three ways to query your data. A visual query builder for point-and-click filtering and aggregation, a full SQL editor for complex joins and custom logic, and an [AI text-to-SQL layer](/blogs/how-to-generate-sql-query-with-AI/) that converts plain English into executable queries. Analysts get full SQL control while operations and product teams can self-serve without writing a single line of code. Draxlr connects directly to 14+ SQL databases including PostgreSQL, MySQL, Snowflake, BigQuery, Redshift, ClickHouse, Databricks, and more. No data warehouse migration or ETL pipeline required. You connect and query from your existing setup. For teams building customer-facing analytics, Draxlr supports [embedded dashboards](/blogs/best-embedded-analytics-tools/) via iframe, React SDK, Vue SDK, and Backend APIs. You can filter data per customer, apply row-level security for multi-tenant setups, and white-label the experience to match your product. Embedding starts at $75/month with flat pricing and no per-viewer fees. Connect your Database ### Key Features - Text-to-SQL AI that converts plain English into executable queries with no SQL skills required - Visual query builder for filtering, sorting, joining, and summarizing data across tables - Full SQL editor for advanced users who need complete query control - 14+ direct database connections including PostgreSQL, MySQL, Snowflake, BigQuery, and Redshift - Multiple chart types with flexible visualization customization - Embedded analytics via iframe, React SDK, and Vue SDK with per-customer data filtering and multi-tenant row-level security - White-label and self-hosted options for full branding and infrastructure control - Dashboard sharing via public URLs, team access, and scheduled email or Slack delivery - Automated alerts triggered by data changes or threshold crossings - Transparent pricing from $25/month ### Pricing Draxlr offers a free plan with no credit card required. Paid plans start at $25/month and cover team dashboards, AI querying, and direct database connections. Embedding starts at $75/month with unlimited customer users. Enterprise and self-hosted plans are available for teams that need SSO, dedicated support, or full infrastructure control. --- ## 2. Tableau ### Best Bold BI Alternative for Enterprise Visualization ![Bold BI Alternative - Tableau](/images/blogs/5-redash-alternatives/tableau.png) Tableau is one of the most widely used enterprise analytics platforms. Large analytics teams reach for it when they need deep visual storytelling, complex data blending, and executive-level reporting. If sophisticated visualization is the primary requirement and budget is not a constraint, Tableau is hard to beat. ### Key Features - Drag-and-drop visualization builder with a wide chart library - Data blending across multiple sources - Forecasting, trend analysis, and predictive modeling - Tableau Prep for data transformation - Enterprise-grade security and governance - Large ecosystem of extensions and connectors ### Pricing Tableau has four plans billed annually. Standard starts at $15/user/month and covers authoring, Tableau Desktop, Prep Builder, and Tableau Pulse. Enterprise is $35/user/month and adds advanced data and access management. Tableau Cloud+ and the Tableau+ Bundle are both contact-sales only. For most teams, Standard or Enterprise is where you start. --- ## 3. Microsoft Power BI ### Best Bold BI Alternative for Microsoft Ecosystem Teams ![Bold BI Alternative - Power BI](/images/blogs/5-redash-alternatives/powerbi.png) Microsoft Power BI is the natural choice for organizations already running on Azure, Excel, Microsoft 365, or Dynamics. If Microsoft infrastructure is already the standard at your company, Power BI slots in without much friction. If you are evaluating a broader set of options, our [Power BI alternatives guide](/blogs/power-bi-alternatives-2026/) is worth reading alongside this one. ### Key Features - Deep integration with Azure, Excel, and Microsoft 365 - Drag-and-drop report builder - AI-powered visuals and natural language Q&A - Power BI Embedded for application integration - DirectQuery and Import modes for data connectivity - Row-level security and governance controls ### Pricing Power BI also has plans billed annually. Pro starts at $14/user/month. Advanced features like AI Copilot and higher refresh rates require Power BI Premium, which adds significant cost. Embedded use cases layer on further licensing complexity. --- ## 4. ThoughtSpot ### Best Bold BI Alternative for Natural Language Analytics ![Bold BI Alternative - ThoughtSpot](/images/blogs/yellowfin-bi-alternatives-2026/thoughtspot.png) ThoughtSpot is built around the idea that anyone in the organization should be able to get answers from data by typing a question, the same way you would search on Google. Its natural language search sits on top of your cloud data warehouse and returns charts and insights without the user needing to know SQL or build a dashboard first. If you have already looked at ThoughtSpot and want to compare it further, our [ThoughtSpot alternatives guide](/blogs/thoughtspot-alternatives-2026/) covers how it stacks up against other options. ### Key Features - Natural language search that returns charts and answers instantly - AI agents for automated analysis and follow-up questions - Live query to Snowflake, Databricks, BigQuery, and other warehouses - SpotIQ automated insight detection - Embedded analytics for product teams - Row-level security and enterprise governance ### Pricing ThoughtSpot publishes its pricing. The Pro plan starts at $50/user/month with a minimum of 25 users and it is billed annually. The embedding plan is custom. --- ## 5. Metabase ### Best Open-Source Bold BI Alternative for Developer Teams ![Bold BI Alternative - Metabase](/images/blogs/yellowfin-bi-alternatives-2026/metabase.png) Metabase is one of the most widely adopted open-source BI tools, mainly used for internal reporting and self-service dashboards. The question-based interface is approachable for non-technical users, and the free open-source version keeps costs low for teams that do not need advanced features. If Metabase is already on your shortlist, our [Metabase alternatives comparison](/blogs/metabase-alternatives-2026/) covers where teams tend to outgrow it. ### Key Features - Question-based query builder and SQL editor - Automatic chart recommendations based on data shape - Dashboard filters, drill-through, and interactive controls - Scheduled reports and email delivery - Open-source with self-hosting support - Embedding available on Pro and Enterprise plans ### Pricing Metabase open-source is free and self-hosted. Metabase Pro, which includes embedding and whitelabeling, starts at around $500/month. Enterprise pricing is custom. --- ## 6. Qlik Sense ### Best Bold BI Alternative for Large Enterprise Self-Service Analytics ![Bold BI Alternative - Qlik Sense](/images/blogs/yellowfin-bi-alternatives-2026/qlik-sense.png) Qlik Sense is an enterprise BI platform known for its associative data engine, which lets users explore relationships across data without being locked into predefined query paths. It is used mainly by larger organizations that need flexible, self-directed data exploration across complex datasets. If you have already evaluated Qlik and are looking at lighter options, our [Qlik alternatives guide](/blogs/qlik-alternatives-2026/) is worth a read. ### Key Features - Associative data engine for exploring data relationships freely - Drag-and-drop dashboard and report builder - AI-assisted insights and natural language querying - Multi-cloud and on-premise deployment options - Row-level security and enterprise governance controls - Active community and a broad library of extensions ### Pricing Qlik Sense Business starts at $300/month, with Enterprise plans available for larger deployments. Pricing is published on their website so you can evaluate cost fit without a sales call. --- ## How to choose the right Bold BI alternative If you need AI-powered self-service analytics or [affordable embedded analytics](/affordable-embedded-analytics-tool/) with transparent pricing, [Draxlr](/) is the strongest starting point. Plain English querying, a visual builder, and full SQL access in one platform from $25/month. If your team is already on Microsoft infrastructure, Power BI is the natural fit with deep Azure and Excel integration. If enterprise-grade visualization is the priority and budget is not a constraint, Tableau delivers on that better than anything else on this list. If your team wants to search data in plain English without building dashboards upfront, ThoughtSpot is built around that workflow. If you want open-source internal dashboards at low cost, Metabase covers most basic BI needs without much setup. If you are in a large enterprise and need flexible data exploration across complex, interconnected datasets, Qlik Sense is worth evaluating. --- ## Conclusion Bold BI is a solid tool with a real feature set. The reasons teams look elsewhere are specific and usually practical. Data source management that requires planning ahead, performance that degrades at scale, a few chart type gaps, deployment instability on Linux environments, and pricing that requires a sales conversation before you can assess fit. Most alternatives on this list connect directly to your existing SQL databases, so switching is less disruptive than it sounds. The main work is rebuilding your dashboards, not moving your data. --- ## FAQs ### 1. What are the best Bold BI alternatives in 2026? The best Bold BI alternatives in 2026 are Draxlr, Tableau, Microsoft Power BI, ThoughtSpot, Metabase, and Qlik Sense. The right choice depends on your priorities: AI-powered self-service analytics, embedded analytics, pricing transparency, SQL flexibility, ease of use for non-technical users, or enterprise visualization depth. ### 2. Why do teams switch from Bold BI? The most common reasons are schema management friction when data sources evolve, dashboard performance issues as data volumes grow, specific chart type limitations, instability on Docker and Kubernetes, and the lack of published pricing. ### 3. Which Bold BI alternative is best for non-technical users? Draxlr and Metabase are the easiest to pick up for non-technical users. They both have text-to-SQL AI that lets business users ask questions in plain English and get chart results without writing SQL. ### 4. Which Bold BI alternative has the best AI features? Draxlr and ThoughtSpot lead here, with text-to-SQL, AI-assisted query building, and automated chart generation built into the core product rather than added on top. Power BI and Tableau have AI features too, but they are layered into broader platforms rather than being central to the query experience. ### 5. What is the cheapest Bold BI alternative? Metabase is free as an open-source, self-hosted tool. Draxlr starts at $25/month with a free tier that requires no credit card. Both publish their pricing clearly, which makes budgeting straightforward before you commit. ### 6. Which Bold BI alternative is best for small businesses? Draxlr is the strongest option for small businesses. Draxlr is quick to set up and scales without pricing surprises. ### 7. Is it hard to migrate from Bold BI? Not particularly. Most alternatives connect directly to your existing SQL databases, so your data stays where it is. The main effort is rebuilding your dashboards in the new tool, which usually takes days rather than weeks.

Embedded Analytics for SaaS: Why It Matters and How to Ship It

hello@draxlr.com (Draxlr) — Mon, 25 May 2026 00:00:00 GMT

Your users are already generating data inside your product. Every action they take, every workflow they complete, every outcome they track is producing signals that could tell them something valuable. The question is whether your product surfaces that value for them, or makes them export a CSV and figure it out themselves. That gap is exactly what [embedded analytics tools](/embedded-analytics-tool/) close. In 2026, embedded analytics has moved from a "nice to have" feature to a baseline expectation in competitive SaaS markets. This guide covers why it matters for SaaS specifically, how it works technically, what to look for in a tool, and the mistakes to avoid when shipping it. --- ## What is embedded analytics? [Embedded analytics](/blogs/what-is-embedded-analytics/) means putting dashboards and reports directly inside your product so users never have to leave to find their data. For a full breakdown of how it works technically, see the [complete guide to embedded analytics](/blogs/what-is-embedded-analytics/). For SaaS companies, the question is not what it is. The question is whether you can afford not to have it. --- ## Standalone Analytics vs Embedded Analytics: What is the difference? Standalone analytics tools are accessed separately from the products your customers actually use day to day. A user who wants to see their data has to log into a different application, navigate an unfamiliar interface, and often connect or configure their data source manually. The insight exists, but the friction to get there is entirely on the user. Embedded analytics removes that friction entirely. The analytics live inside your product, at the point in the workflow where they are most relevant. Users do not need to go anywhere else. They do not need to configure anything. The product team has already defined what data matters and built the experience around it. The distinction is not about analytical power. It is about where the analytics live and who has to do the work to access them.

Dimension	Standalone Analytics	Embedded Analytics
Where it lives	Separate application	Inside your SaaS product
Who configures it	The end user	Your product team, once
Context awareness	General purpose	Specific to your product's workflows
Branding	The analytics vendor's	Your product's
User effort to access	Log in, navigate, configure	Open the page it already lives on
Viewer access model	Separate accounts in the analytics tool	Access through your product

--- ## Why SaaS companies are investing in embedded analytics There are five business reasons SaaS companies build embedded analytics, and each one has a measurable impact on growth. ### 1. It increases product stickiness When your product is the place where users get insight into their own data, they have a reason to come back that goes beyond task completion. Analytics creates a habit loop. Users check their dashboards. They monitor trends. They share reports with colleagues. All of that happens inside your product. Higher engagement means lower churn. For subscription SaaS, churn reduction is one of the highest-leverage improvements you can make to revenue. ### 2. It justifies premium pricing Analytics is a natural upsell. Offering deeper reporting, custom dashboards, or advanced data exports as part of a higher-tier plan gives you a concrete, tangible reason for a price difference. Users understand the value of better visibility into their data. Companies that embed analytics into premium tiers consistently see higher conversion from lower plans and lower sensitivity to price increases at renewal. ### 3. It reduces support load A significant portion of support tickets in most SaaS products are questions that analytics could answer. "How many users completed this workflow last month?" "What is our average processing time?" "Where are we spending the most?" If your product surfaces those answers automatically, users stop filing tickets asking for them. ### 4. It accelerates product-led growth When users can see the impact of their work inside your product, they have something concrete to share with their manager or team. Analytics creates shareable, compelling proof of value. That proof of value is often what gets a product expanded from one team to a whole department. ### 5. It is now a buying criterion In most B2B SaaS categories, buyers have become sophisticated. Reporting and analytics features appear on procurement checklists alongside security certifications and API access. Products that cannot show buyers a robust analytics story are losing deals to products that can. --- ## How embedded analytics works technically Understanding the technical architecture helps you make better decisions when evaluating embedded analytics tools or planning your implementation. ### 1. Data connection Embedded analytics tools connect to your data source, usually a database like PostgreSQL, MySQL, or a data warehouse like BigQuery or Redshift. Some tools also support live connections to spreadsheet sources like Google Sheets, which is useful for smaller SaaS products or teams that manage data outside a traditional database. The tool queries your data either on demand (when a user loads a dashboard) or on a schedule (pre-aggregated snapshots updated periodically). ### 2. The embed layer This is what makes it "embedded." The analytics tool generates a dashboard or chart that your application loads inside an iframe, a JavaScript component, or a framework-specific component for React or Vue. From the user's perspective, the chart just appears inside your product. There is no visible seam between your application and the analytics layer. Most embedded analytics tools offer: - **Iframe embeds** — the simplest approach, drop a URL into your frontend and the dashboard renders - **HTML embeds** — slightly more customizable, useful for injecting into existing layouts - **React components** — native integration for React-based frontends - **Vue components** — the same for Vue-based applications ### 3. Access control and row-level security One of the more complex parts of embedded analytics is making sure users only see their own data. If your SaaS serves multiple customers, you cannot show Company A's data to Company B. Embedded analytics tools handle this through row-level security or token-based access control. When a user loads a dashboard, your application passes context (usually a user ID or tenant ID) to the analytics tool, which then filters the data accordingly. The user sees only what is relevant to them. ### 4. White labeling Most enterprise-grade embedded analytics tools support white labeling, meaning the analytics interface shows your branding, not the tool's branding. Your logo, your colors, your typography. Users have no reason to know a third-party tool is powering the analytics layer. This matters for perceived product quality. Users trust analytics that feel native to the product they are using. --- ## What to look for in an embedded analytics tool Not all embedded analytics platforms are built for the same use case. Here are the criteria that matter most for SaaS teams. ### 1. Ease of embedding The embed process should not require weeks of engineering work. Look for tools that offer multiple embed types and clear documentation. If the embed requires complex backend configuration to get a basic chart on screen, that is a signal the tool was built for enterprise IT teams, not product teams moving fast. ### 2. Row-level security and multi-tenancy If your SaaS serves more than one customer, multi-tenancy support is non-negotiable. You need a tool that can filter data at the query level based on the user's identity. Ask vendors directly how this works and whether it requires custom code on your side. ### 3. White Label Support Check whether white labeling is included in the plan you are considering, or whether it is an add-on. Some vendors charge significantly more for this capability. For most SaaS products, white labeling is a baseline requirement, not a premium feature. ### 4. Pricing Model This is where embedded analytics vendors diverge sharply. Some charge per external viewer, meaning your pricing scales with your customer count. At 500 customers, that cost becomes substantial. Others charge flat monthly fees regardless of how many end users access the dashboards. For SaaS companies at growth stage, flat pricing is almost always preferable. It gives you a predictable cost and lets you expand usage without watching your analytics bill grow in parallel. ### 5. Speed of Implementation Enterprise platforms often have implementation timelines measured in months. For a SaaS team trying to ship an analytics feature, that timeline is not acceptable. Look for tools where you can have a first working dashboard embedded in your staging environment within one to two weeks. ### 6. SQL, AI and No-Code Query Building Your team may include both engineers who prefer raw SQL and product managers or data analysts who prefer AI or visual query builder. The best embedded analytics tools support both without forcing everyone into one mode. --- ## Common mistakes SaaS teams make with embedded analytics ### 1. Starting with too much The most common mistake is trying to build a complete analytics suite on day one. You end up with a sprawling dashboard that no one uses because it is not clear what users should look at. Start with three to five metrics that directly relate to the core outcome your product delivers. Build those well. Add more based on what users ask for. ### 2. Ignoring the viewer experience Analytics that are built for internal stakeholders often end up in customer-facing products unchanged. What works in an internal quarterly review does not work for a user checking their dashboard during a busy workday. Keep customer-facing dashboards simple, fast to read, and focused on actionable metrics. ### 3. Choosing the wrong pricing model Some teams pick a tool based on the base plan cost without thinking through what the cost looks like once they have 200 or 500 customers accessing dashboards. A tool that is affordable at 10 customers can become prohibitively expensive at scale if it charges per viewer. Model out your cost at 100, 500, and 1,000 customers before committing to a platform. ### 4. Underestimating the branding gap Shipping a white-labeled analytics experience where the branding is 80% right looks worse than not branding it at all. If your product is dark mode and your embedded charts are white with a different font, users notice. Treat the branding of your analytics layer with the same rigor you apply to the rest of your product. --- ## What good embedded analytics looks like in practice Here is what a well-executed embedded analytics implementation typically includes across different SaaS categories: **Project management SaaS:** Sprint velocity, task completion by assignee, time to completion trends, blocked items over time. **HR and workforce SaaS:** Headcount changes over time, turnover rate by department, time-to-hire by role, offer acceptance rates. **E-commerce or marketplace SaaS:** Revenue by period, order volume, average order value, return rates, top products by revenue. **Logistics or operations SaaS:** Delivery time distributions, error rates by carrier or route, cost per shipment over time, SLA compliance rates. The common thread is that every metric connects directly to an outcome the user cares about. The analytics exist to help them do their job better, not to demonstrate that your product is collecting data. --- ## How Draxlr Fits Into This Picture Draxlr is an [affordable embedded analytics](/affordable-embedded-analytics-tool/) platform built specifically for SaaS products that need to ship analytics features without the cost and complexity of enterprise tools. It connects to your existing database or data warehouse, lets your team build dashboards using AI, a visual query builder, or raw SQL, and generates embed-ready components for React, Vue, and iframe integrations. White labeling is included in the embedding plan, not sold as a separate add-on. Pricing starts at $75 per month for full embedding capability, with unlimited external viewers. There are no per-user charges that scale with your customer count. For most SaaS teams, the implementation path looks like this: connect your database, build your first dashboard, embed it in your staging environment. That first working dashboard typically takes one to two weeks. Full production deployment across multiple customer-facing views runs two to four weeks. You can start with a [7-day free trial, no credit card required](https://draxlr.com). Connect your Database --- ## Conclusion Embedded analytics is not a reporting feature. It is a structural investment in the value your product delivers to customers. The SaaS companies getting the most from embedded analytics share a few characteristics. They start with a small number of high-signal metrics, they treat the analytics experience with the same product discipline they apply to their core workflows, and they choose pricing models that do not penalize them for growing their customer base. If you are evaluating where embedded analytics fits in your product roadmap, the right question is not "should we build this?" The right question is "how fast can we ship something useful?" The answer, with the right tool, is faster than most teams expect. **[Explore Draxlr for your SaaS product →](/)** --- ## FAQs ### 1. Do I need a data warehouse to use embedded analytics? Not necessarily. Most embedded analytics tools connect directly to transactional databases like PostgreSQL or MySQL. Data warehouses like BigQuery or Redshift are useful if you are working with large volumes of data or need to query across multiple sources, but many SaaS products at early and mid-stage get significant value from connecting their primary database directly. ### 2. How do I make sure users only see their own data? This is handled through row-level security. You pass user context (such as a customer ID or tenant ID) to the analytics tool when loading a dashboard, and the tool filters results accordingly. Every embedded analytics platform handles this differently, so ask vendors specifically how multi-tenancy works before committing. ### 3. Is embedded analytics only for large SaaS companies? No. The cost and complexity of embedded analytics has dropped significantly. Tools now exist at price points that make sense for SaaS companies at Series A or earlier, and implementation timelines have compressed to weeks rather than months. The decision is less about company size and more about whether analytics is a meaningful part of your product value proposition. ### 4. What embed options should I look for? At minimum, look for iframe and HTML embed support. If your frontend is built on React or Vue, native component support is a significant advantage as it gives you more control over styling and behavior. White label support (custom branding) should be included, not priced as an add-on. ### 5. How long does it take to implement embedded analytics? For modern tools designed for SaaS use cases, a working first dashboard embedded in your application can be ready in one to two weeks. Full production deployment with multiple dashboards, row-level security, and white labeling typically takes two to four weeks.

Best GoodData Alternatives for Embedded Analytics in 2026

hello@draxlr.com (Draxlr) — Wed, 13 May 2026 00:00:00 GMT

GoodData is one of the more established platforms in the embedded analytics space. Its multi-tenant workspace architecture, semantic layer, and support for iframe, React SDK, and web component embedding make it a credible option for SaaS companies that want to deliver analytics to their customers. But for many product teams, GoodData creates more friction than it removes, particularly around pricing, deployment complexity, and the learning curve of its proprietary query language. This guide covers seven GoodData alternatives worth evaluating if you are building [embedded analytics](/embedded-analytics-tool) into a software product and GoodData is not the right fit. Each tool is assessed on what it offers for embedding specifically, who it suits, and what it costs. --- ## What Is GoodData and What Is It Used For? ![GoodData main page cover](/images/blogs/gooddata-alternatives-embedded-analytics/images/gooddata.png) GoodData is a cloud-based business intelligence and embedded analytics platform. It lets organizations connect to data sources, build dashboards and reports, and embed those dashboards into their own software products. Its core architecture is built around a semantic layer that centralizes business metrics and data relationships. This means you define a metric once and it stays consistent across every dashboard that references it. GoodData also supports multi-tenancy, meaning a single deployment can serve many separate customer environments, each seeing only their own data. The platform offers three main embedding methods: iframes for basic integration, a React SDK for deeper frontend customization, and web components for modern application architectures. It also supports analytics-as-code workflows, letting data teams manage analytics configuration through Git and CI/CD pipelines rather than only through a UI. GoodData's target users are primarily product teams at SaaS companies and enterprise data teams that need governed, scalable analytics at the customer level. --- ## Why Teams Look for GoodData Alternatives GoodData suits certain use cases well, but consistent patterns emerge across user reviews on G2, Capterra, TrustRadius, and SelectHub that explain why teams start evaluating other options for embedding. - **Pricing that requires a sales conversation.** GoodData does not publish prices for either of its plans. The Professional plan is structured as a platform fee plus a per-workspace charge, with each workspace representing a separate customer environment. The Enterprise plan is fully custom. Because neither plan shows a number without contacting sales, it is difficult to estimate costs or compare them against other tools without going through a discovery call first. For teams that prefer to evaluate pricing independently before speaking to a vendor, this is a meaningful friction point. - **A proprietary query language with a steep learning curve.** GoodData uses MAQL (Multi-Dimensional Analytical Query Language) for metric definitions and data modeling. Product teams that want to move fast and already know SQL have to invest time in a new language before they can build anything. This slows down time to first embedded dashboard. - **Limited visual customization.** While GoodData offers multiple embedding methods, deep visual customization, going beyond color palette changes and basic layout adjustments, requires significant developer time. The chart library is narrower than competitors, and users report that it can feel dated compared to more modern embedded analytics tools. - **Support that depends on your plan tier.** Dedicated customer success managers and priority support are only available on GoodData's Enterprise plan. Teams on lower tiers submit tickets and rely on community forums. For companies embedding analytics into customer-facing products where downtime has a direct business impact, this creates meaningful risk. - **Limited customization of dashboard layout.** Several reviewers note that modifying existing dashboard layouts as business requirements change is more time-consuming than expected. The platform can be rigid when you need to restructure how content is organized. - **Implementation takes longer than expected.** Beyond the query language, configuring workspace hierarchies, setting up row-level security for multi-tenancy, and working through GoodData's deployment process typically takes several weeks before the first dashboard is live in a product. These are not edge-case complaints. They appear across enough independent reviews to represent genuine structural limitations of the platform rather than individual user errors. If any of these match your situation, the GoodData alternative tools below are worth investigating. --- ## What to Look for in a GoodData Alternative Before comparing GoodData alternatives, it helps to know which capabilities matter most for embedded analytics use cases. These are the criteria that separate tools that work for embedding from general-purpose BI platforms that technically support it as a feature. - **Embedding method flexibility.** Iframes are the most common embedding method and the easiest to implement, but they have real drawbacks like slower loading, limited responsiveness, and styling constraints. The best tools also offer React components, Vue components, or web components that integrate directly into your application's DOM. This makes dashboards feel native rather than bolted on. - **Multi-tenancy and row-level security.** If you are embedding analytics for multiple customers, you need to guarantee that each customer sees only their own data. Row-level security and multi-tenant architecture are not optional features. They are requirements. Confirm how each tool implements this before committing. - **White labeling.** Your customers should see your product's branding throughout. Confirm which plan tier includes white labeling and whether there are any limits on what can be customized, such as colors, fonts, logo, and domain. - **Pricing model transparency.** Per-customer or per-workspace pricing can escalate quickly as you scale. Look for flat-rate models or per-seat structures that are easy to forecast. Published pricing is a meaningful advantage over contact-sales-only models. - **Time to first embedded dashboard.** Developer hours are expensive. A tool that takes three months and a professional services engagement to go live has a much higher true cost than its monthly fee suggests. Look for realistic deployment timelines and self-service onboarding. - **Self-service analytics for end users.** The best embedded analytics tools let your customers explore their own data through filter controls, drill-downs, and natural language querying without filing requests to your team. This requires row-level security as a foundation, since customers can only safely self-serve on data that is properly isolated to their environment. - **Data source compatibility.** Confirm the tool connects to the databases and warehouses you already use. Most tools support the major options (PostgreSQL, MySQL, Snowflake, BigQuery, Redshift), but if your stack includes less common databases, verify before shortlisting. - **Support quality.** For customer-facing products, analytics downtime has a business cost. Understand what level of support is included in each pricing tier and what the expected resolution time is for critical issues. --- ## GoodData Alternatives Comparison Table

Tool	Starting Pricing	Best For	Free Trial
GoodData	Contact sales	Enterprise SaaS needing governed multi-tenant embedding	No
Draxlr	$75/month	SaaS teams wanting fast, affordable embedding with transparent pricing	Yes, 7 days
Looker	Contact sales	Google Cloud teams needing analytics-as-code with embedding	No
Power BI Embedded	From ~$500/month (capacity)	Teams already standardized on Microsoft 365 and Azure	Yes
Sisense	$399/month	Organizations with large datasets that need fast query performance	Yes
Luzmo	$495/month, Billed annually	Product teams that want a modern, developer-friendly analytics platform	Yes
Omni(Explo)	Contact sales	Teams wanting governed BI and embedded analytics in one platform	Yes
Metabase	$575/month + $12/user(interactive)	Teams that want dev-focused tool	Yes

--- ## 1. Draxlr **Best GoodData Alternative For: SaaS teams that want a fast, affordable embedded analytics with transparent pricing and no proprietary query language.** ![Draxlr](/images/blogs/_tools/draxlr.png) [Draxlr](/) is an analytics platform that connects directly to your SQL database and gets dashboards into your product quickly. Unlike GoodData, which requires learning MAQL before you can build anything useful, Draxlr works with what your team already knows. Technical users write SQL. Non-technical users can use the visual query builder or AI. Anyone can ask questions in plain English using AI-powered natural language querying. All three modes work from the same platform without switching tools or licenses. Embedding is available via iframe, React JS SDK, Vue JS SDK, and Backend APIs across all embedded plans. Most teams have their first dashboard live in one to two weeks. Full production deployment typically lands in two to four weeks, considerably faster than the multi-month implementations common with enterprise platforms. Pricing is published on the website with no surprises i.e., the Premium plan at $75 per month includes all embedding methods, [white labeling](/white-label-bi-solution/), and unlimited external viewers. As your customer base grows, your analytics cost does not. Connect your Database ### Key features for embedding - **Embedding methods:** iFrame, React JS SDK, Vue JS SDK, and Backend APIs available across all embedded plans, giving engineering teams flexibility over how dashboards integrate into the host application without being forced into a single approach - **White labeling:** Remove Draxlr branding entirely and apply your own colors, logo, and custom domain. Your customers see your product throughout, with no third-party branding in sight. Available on Premium plan and above - **Interactive dashboards:** Embedded dashboards are not static reports. End users can apply filters, drill down into specific data points, and explore their data independently without filing requests to your team, reducing the analytics support burden on your side - **Multi-tenant support:** Each customer environment is kept fully isolated from others. One deployment serves your entire customer base, with data separation enforced at the platform level - **Row-level security (coming soon):** Per-tenant data isolation enforced at the query level, ensuring each customer sees only the rows they are authorized to access. Currently in development and expected to be available by end of May 2026 - **Responsive dashboards:** Embedded dashboards adapt automatically to different screen sizes, so your customers get a consistent experience whether they are viewing on desktop, tablet, or mobile within your application - **Export and sharing options:** End users can export dashboard data to common formats and share views directly from within the embedded experience, without needing to leave your product or contact your team - **Unlimited external viewers:** No per-seat charges for customers viewing embedded dashboards. Whether you have ten customers or ten thousand, your analytics cost stays predictable and flat - **Three query modes:** Visual query builder for non-technical users who need answers without writing code, raw SQL for analysts who want full control, and AI-powered natural language querying for anyone who wants to ask a question in plain English and get a chart back. All three available from the same interface - **Alerts:** Configure threshold-based alerts so users are notified automatically when data crosses conditions that matter to them, without having to check dashboards manually - **Fast implementation:** Most teams have their first embedded dashboard live in one to two weeks. Full production deployment with multi-dashboard setups and access controls typically lands in two to four weeks ### Pros - Fully transparent pricing published on the website, no sales call required to understand what deployment will cost - Unlimited external viewers at a flat rate means pricing stays predictable as your customer base grows, unlike per-seat or per-workspace models - Short deployment timeline, most teams are live in one to two weeks - No proprietary query language, SQL and visual query builder work out of the box - White labeling, all embedding methods, and unlimited viewers available from $75 per month without needing an enterprise contract ### Cons - No formal semantic layer. - Supports SQL Databases only. ### Pricing Premium $75/month, Power $125/month, Enterprise custom. 7-day free trial, no credit card required. ### What G2 users say about Draxlr > Exactly what we needed for embedded analytics at a fair price. Draxlr gives us everything we need for embedded analytics at a fraction of what other tools charge. The pricing is transparent and we didn't have to compromise on features to stay within budget. — [All Draxlr reviews](https://www.g2.com/products/draxlr/reviews/) --- ## 2. Looker **Best GoodData Alternative For: Google Cloud organizations that need analytics-as-code with deep BigQuery integration and strong governance.** ![Looker](/images/blogs/metabase-alternatives-2026/looker.png) Looker takes a code-first approach to analytics through LookML, a modeling language that treats metric definitions and data relationships as versioned, testable code. For teams that want Git workflows, code review, and CI/CD pipelines applied to their analytics layer, this is a meaningful differentiator. For embedding, Looker offers iframe and SDK-based integration with row-level security handled at the semantic layer level. The Gemini AI integration adds natural language querying for end users. Pricing is custom and typically starts around $3,000 per month, and full deployment takes 6 to 12 weeks. ### Key features for embedding - **Embedding methods:** Signed iframe embedding and JavaScript SDK for authenticated, secure customer-facing dashboards with per-session URL generation - **Row-level security:** Data isolation for individual customers is enforced through LookML rather than application-level code, making it more consistent across dashboards - **Multi-tenancy:** Single Looker deployment serves multiple isolated customer environments through LookML-defined access rules - **White labeling:** Custom themes, branding, and domain available on the Embed edition - **Gemini AI integration:** Natural language querying for end users, allowing customers to ask questions without navigating dashboard controls - **LookML governance:** Business metric definitions are version-controlled, testable code, ensuring consistency across every embedded dashboard and query ### Pros - Consistent metric definitions enforced at the semantic layer prevent dashboard drift across teams - Deep BigQuery integration with native querying and no data duplication - Strong access controls and role-based permissions suited to enterprise compliance requirements - Broad connector support across 50+ SQL dialects and cloud data warehouses ### Cons - LookML has a steep learning curve, replacing one proprietary language (MAQL) with another - Pricing is custom and typically starts around $3,000 per month with no published tiers - Implementation takes 6 to 12 weeks, one of the longest timelines on this list ### Pricing Custom, typically $3,000+ per month. No free trial. ### What G2 users say about Looker > Looker provides flexible, extensible data visualization. There is not full feature parity in embed. — [Review from on G2](https://www.g2.com/products/looker/reviews/looker-review-5016431) --- ## 3. Power BI Embedded **Best GoodData Alternative For: Organizations already on Microsoft 365 and Azure that want to reuse existing Power BI reports in their product.** ![Power BI Embedded](/images/blogs/metabase-alternatives-2026/power-bi.png) Power BI Embedded lets you take Power BI reports and dashboards and embed them into your own application using Azure capacity. For organizations already using Power BI internally, report investments carry over rather than being rebuilt in a new tool. Embedding is handled through the Power BI JavaScript API, with row-level security and multi-tenancy supported through careful configuration. The Copilot integration adds AI-powered natural language querying for embedded end users. Pricing is based on Azure capacity SKUs and can range from roughly $500 to several thousand dollars per month depending on usage patterns. ### Key features for embedding - **Embedding methods:** JavaScript API for direct DOM integration and iframe embedding, allowing reports to be surfaced inside external applications - **Row-level security:** Enforced per user or group, restricting what data each embedded viewer can access. Requires careful configuration for multi-tenant SaaS environments - **Multi-tenancy:** Separate customer environments supported through service principal authentication and workspace isolation - **White labeling:** Power BI branding can be removed from embedded reports. Available on Power BI Embedded capacity plans - **Copilot AI:** Natural language querying for embedded end users, powered by Microsoft's AI stack. Lets customers ask questions and receive visual answers without building custom queries - **Azure integration:** Capacity-based pricing through Azure SKUs scales with usage, and integrates with other Microsoft infrastructure your team may already use ### Pros - Familiar interface for business users already using Excel or Power BI internally - Extensive chart library with both native and community-built visualization types - Copilot AI integration for natural language querying without custom development - Strong Microsoft ecosystem integration across Azure, Teams, and SharePoint ### Cons - Capacity-based Azure pricing is harder to forecast than flat-rate alternatives, costs can escalate quickly - DAX (the calculation language) has a steep learning curve, consistently the top complaint in G2 reviews - Performance degrades with large datasets, cited in roughly 95% of reviews mentioning speed - Multi-tenancy and row-level security configuration requires careful architectural planning ### Pricing Capacity-based, approximately $500 to $5,000+ per month depending on Azure SKU and usage. ### What G2 users say about Power BI Embedded > Easy to learn BI tool. Not as powerful nor as customizable as some of its competitors. It uses DAX for many things, which can be difficult for some to use. The tool is very rigid sometimes, not giving you the flex quite to do what you want to do, and sometimes you have to settle for what IT wants something to look like. The ability to embed analytics is not as flexible as competitors. — [Review from on G2](https://www.g2.com/products/microsoft-microsoft-power-bi/reviews/microsoft-power-bi-review-7990127) --- ## 4. Sisense **Best GoodData Alternative For: Mid-market and enterprise SaaS teams with large datasets that need fast query performance and published pricing.** ![Sisense](/images/blogs/metabase-alternatives-2026/sisense.png) Sisense is a business intelligence and analytics platform with an in-chip data engine that processes queries in memory, which generally means faster dashboard loading on large datasets. For embedding, it supports SDK and API-based integration alongside iframes, with white labeling and multi-tenancy available on higher plans. AI-powered insight surfacing can highlight anomalies and trends automatically in embedded dashboards. Unlike GoodData, Sisense publishes pricing. The Launch plan starts at $399 per month and the Grow plan at $1,299 per month includes white labeling, both with a 7-day free trial. ### Key features for embedding - **Embedding methods:** Compose SDK for React-based native embedding, Sisense.JS for widget-level embedding, and iframe for standard integration - **In-chip analytics engine:** Processes queries in memory, which generally means faster dashboard loading on large datasets compared to traditional query engines - **White labeling:** Full brand customization including colors, fonts, and logo. Available on Grow plan ($1,299/month) and above - **Multi-tenancy:** Separate customer environments supported through role-based permissions and data model configuration - **Row-level security:** Column and row level security available, though reviewers note tenant isolation requires intentional design and ongoing configuration - **AI-powered insights:** Automatically surfaces anomalies, trends, and narrative summaries within embedded dashboards without end users needing to explore manually - **Published pricing:** Unlike GoodData and Looker, Sisense publishes plan tiers with a 7-day free trial, allowing cost evaluation without a sales call ### Pros - Published pricing tiers with a 7-day free trial, no sales call required to evaluate - In-chip engine handles large datasets faster than traditional query-based tools - Strong data connector support ### Cons - Deep visual customization is harder than it appears, matching your product's branding requires significant engineering effort - Learning curve is steeper than marketed, multiple G2 reviewers note it was described as low-code but required more technical skill in practice - Documentation quality is frequently criticized, with outdated links and gaps in guidance - Bugs in the database cache system are flagged repeatedly across G2 and Reddit reviews ### Pricing Launch $399/month (50 viewer seats, 2 designer seats), Grow $1,299/month (100 viewer seats, 5 designer seats, white labeling), Scale custom. 7-day free trial on Launch and Grow. ### What G2 users say about Sisense > Lots of potential but not delivering on all of its promise. The conceptual approach of the product is exactly what we were looking for, a straightforward embedded analytics tool. The tool makes creating visualizations pretty easy and has a lot of flexibility in data sources. — [Review from on G2](https://www.g2.com/products/sisense/reviews/sisense-review-4314806) --- ## 5. Luzmo **Best GoodData Alternative For: Product teams that want a purpose-built embedded analytics platform with a clean developer experience.** ![Luzmo](/images/blogs/gooddata-alternatives-embedded-analytics/images/luzmo.png) Luzmo (formerly Cumul.io) focuses on embedding analytics into SaaS products rather than adapting an internal BI tool for external use. It offers separate tracks for non-technical users (Studio, a no-code builder) and developers (Flex SDK, for programmatic control). Natural language querying for end users is available through Luzmo IQ. White labeling requires the Premium plan. Pricing is billed annually, and scales with Monthly Active Users, which means costs increase as your customer base becomes more active. ### Key features for embedding - **Embedding methods:** Studio offers a no-code drag-and-drop interface for building dashboards without engineering involvement. Flex SDK gives developers programmatic control over layout, data binding, interactivity, and styling - **Multi-tenancy:** Customer data isolation handled through account overrides at the platform level - **White labeling:** Brand customization of embedded dashboards available on Premium plan (€1,995/month) and above - **Natural language querying:** Luzmo IQ allows end users to ask questions about their data in plain English within embedded dashboards - **Chart library:** Multiple chart types including standard charts, maps, and custom options - **MAU-based pricing:** Costs scale with Monthly Active Users rather than per seat or per workspace ### Pros - Purpose-built for embedding, documentation and APIs are designed for SaaS product teams - Dual no-code and SDK tracks serve both technical and non-technical dashboard builders - Fast to get first dashboard live, reviewers consistently praise ease of initial setup ### Cons - MAU-based pricing means costs grow as your customer base becomes more active, harder to forecast at scale - Limited querying capabilities for complex SQL use cases, reviewers note it can feel restrictive - Performance issues with large datasets and certain connector configurations, particularly Databricks - Some missing features compared to more mature platforms, components have occasional bugs ### Pricing Starter $495/month, Premium $1,995/month (both billed annually), Enterprise custom. ### What G2 users say about > Fast and user-friendly tool, sometimes a bit restrictive. Very fast to create dashboards, user friendly so easy to build those. Nice embedding in pages and also very nice support to help us solve our issues ! They also give our company some visibility in their roadmap so that we can see what is coming and if it can solve our issues. — [Review from on G2](https://www.g2.com/products/luzmo/reviews/luzmo-review-9706780) --- ## 6. Omni (Explo) **Best GoodData Alternative For: Teams that want governed analytics powering both internal BI and customer-facing embedded dashboards from a single semantic platform.** ![Omni](/images/blogs/gooddata-alternatives-embedded-analytics/images/omni.png) Omni is a BI and analytics platform built around a semantic layer that centralizes metric definitions and ensures consistency across dashboards, queries, and AI-generated answers. It supports SQL, spreadsheet-style formulas with Excel-compatible syntax, and point-and-click exploration from the same interface. Omni acquired Explo in 2024, integrating its customer-facing embedded analytics capabilities into the broader platform. For teams that need both governed internal reporting and embedded product analytics, Omni offers a single semantic foundation for both use cases. Pricing is not publicly listed. ### Key features for embedding - **SDK embedding:** Embedded dashboards integrate into your product via SDK, inheriting the same semantic layer definitions used for internal analytics - **Semantic layer:** Centralizes metric definitions so embedded customer dashboards and internal dashboards reference the same calculations, preventing metric drift between products - **SQL and spreadsheet-style exploration:** Technical users can write SQL, non-technical users can use Excel-compatible formula syntax, both working from the same governed layer - **AI-powered queries:** Natural language querying allows customers and internal users to ask data questions without writing SQL or building custom queries - **Row-level security:** Data isolation enforced at the semantic layer level, ensuring customers see only their own data in embedded dashboards - **Single platform for internal and embedded analytics:** Omni acquired Explo in 2024, consolidating customer-facing embedded analytics and internal BI under one semantic foundation ### Pros - Balances governance and flexibility, described by reviewers as the middle ground between Looker's rigidity and Metabase's looseness - Accessible to non-technical users, workbook model lets analysts build on a shared governed layer without writing LookML - Single semantic foundation for internal and customer-facing analytics reduces metric drift between products ### Cons - Pricing is not publicly listed, requires a sales conversation before evaluating cost - Semantic layer is still maturing, reviewers note it is less developed than Looker's equivalent - Fewer reviews than more established tools, harder to assess at scale - Integration can be challenging, data accuracy during setup requires validation ### Pricing Not publicly listed, requires contact. Free trial available. ### What G2 users say about > A modern plug and play BI tool. A little difficult for non-SQL folks and prices seem a little high compared to other alternatives available. — [Review from on G2](https://www.g2.com/products/omni-analytics-inc-omni-analytics/reviews/omni-analytics-review-11323948) --- ## 7. Metabase **Best GoodData Alternative For: Teams that want to validate an embedding use case at low upfront cost before committing to a dedicated platform.** ![Metabase](/images/blogs/yellowfin-bi-alternatives-2026/metabase.png) Metabase is an open-source business intelligence tool primarily designed for internal analytics that also supports embedding. The self-hosted open-source version is free, making it the lowest-barrier entry point on this list. Metabase offers static embedding (view-only, no sign-in required) and interactive embedding (authenticated, full interactivity). Static embedding is available from the Starter plan at $100 per month but carries a "Powered by Metabase" watermark. Interactive embedding with white labeling requires the Pro plan at $575 per month. Critically, every authenticated viewer counts as a paid user at $12 per month, meaning a product with 500 customers can reach roughly $74,000 per year in user fees alone. ### Key features for embedding - **Static embedding:** View-only dashboards embedded via signed URLs, available from the Starter plan ($100/month). Carries a "Powered by Metabase" watermark and cannot be white labeled - **Interactive embedding:** Authenticated, fully interactive dashboards for customer-facing use. Requires Pro plan ($575/month). Every authenticated viewer counts as a paid user at $12/month - **React SDK:** Component-based embedding for deeper frontend integration. Available on Pro and above, allowing more native-feeling dashboard integration than iframes alone - **Row-level security:** Data sandboxing to isolate each customer's data within embedded dashboards. Available on Pro plan and above - **White labeling:** Remove Metabase branding and apply custom colors and fonts. Available on Pro plan and above - **Visual query builder:** Non-technical end users can explore data with a point-and-click interface without writing SQL, available across all plans ### Pros - Open-source self-hosted version - Fast to set up, most teams have basic dashboards running in days - No proprietary query language, SQL and visual query builder are accessible without training - Large community and active development ### Cons - Per-authenticated-viewer pricing makes costs unpredictable and potentially very high for SaaS products with large customer bases - Interactive embedding with white labeling requires Pro at $575/month, the free version only offers static embeds with a watermark - Limited customization, reviewers note the interface cannot be significantly restyled to match a host product's design - Struggles with advanced multi-tenant SaaS embedding requirements compared to purpose-built tools ### Pricing Starter $100/month (static embed, no white labeling), Pro $575/month + $12/user/month (interactive embedding, white labeling, RLS), Enterprise custom. ### What G2 users say about > Great but only if you're good at understanding analytics. if you understand analytics then it's a really great tool. the filters you use to help navigate you to the right sets of data makes life really easy. the dashboards you can create too are great and the way you can place them, change the sizes of them to tailor to exactly what you want is great. it's definitely not designed for those who just want to pull data, you need to understand how to work data and analytics. when you're joining pieces of data together it can get confusing as well with the different types of joining. — [Review from on G2](https://www.g2.com/products/metabase/reviews/metabase-review-11461970) --- ## How to Choose the Right GoodData Alternative for Embedding - **If pricing transparency is the priority**, Draxlr, Sisense, Luzmo, Power BI Embedded, and Metabase all publish pricing. GoodData, Looker, and Omni require a sales conversation before you can estimate cost. - **If deployment speed matters**, Draxlr and Metabase have the shortest timelines. Draxlr gets most teams to a live embedded dashboard in one to two weeks. Metabase can be running in days for simpler use cases. Looker, Power BI Embedded, and Sisense typically take four to eight weeks or more. - **If performance on large datasets is the main concern**, Sisense's in-chip engine is the most direct solution. Looker running natively on BigQuery is also strong for large-scale cloud data warehouses. - **If you want a platform purpose-built for embedding**, Luzmo is designed from the ground up for SaaS product analytics. Draxlr is also a strong fit here and adds the advantage of transparent pricing and a shorter deployment timeline. - **If you need both internal and customer-facing analytics from one semantic layer**, Omni is the most complete solution following its Explo acquisition. - **If budget is the primary constraint**, Draxlr at $75 per month is the most affordable option with full embedding support and published pricing. --- ## Conclusion No single tool on this list is the right choice for every team. GoodData has real strengths in governance and multi-tenancy at enterprise scale, but the combination of unpublished pricing, a proprietary query language, and a longer implementation timeline makes it a poor fit for teams that need to move quickly or evaluate cost independently. The GoodData alternatives here cover a wide range of requirements. Teams with tight budgets and straightforward needs have viable options at the lower end of the pricing spectrum. Teams with large datasets, complex governance requirements, or existing cloud infrastructure investments have purpose-built options at the higher end. The right starting point is understanding which of GoodData's limitations is actually driving your evaluation, then matching that to the tool that addresses it most directly. A free trial or a short proof-of-concept deployment is worth doing before committing. Most tools on this list offer one or the other, and the difference between how a tool looks in a demo and how it performs in your actual stack is always worth finding out before signing a contract. --- ## FAQs ### 1. What is embedded analytics? Embedded analytics is the integration of dashboards, reports, and data visualizations directly into a software product for end users, typically customers. Rather than directing users to a separate analytics tool, the analytics experience lives inside your product. This requires multi-tenancy to isolate each customer's data, white labeling so the experience matches your branding, and embedding methods that make dashboards feel native rather than bolted on. ### 2. Which GoodData alternative is cheapest for embedded analytics? For a fully hosted solution with complete embedding support including white labeling and unlimited external viewers at a flat rate, Draxlr's Premium plan at $75 per month is the most affordable published price on this list. Metabase's open-source version is free to self-host, but it only supports static embedding with a "Powered by Metabase" watermark. Interactive embedding with white labeling requires the Pro plan at $575 per month, and every authenticated viewer counts as a paid user at $12 per month, which can make it significantly more expensive than it appears for customer-facing use cases. ### 3. How important is white labeling for embedded analytics? For most SaaS products, white labeling is essential. Your customers are using your product and expect your branding throughout. Seeing a third-party vendor's logo or branding in an embedded dashboard undermines the experience. Confirm which plan tier includes white labeling for any tool you are evaluating, as several tools restrict it to higher-priced plans. ### 4. What is multi-tenancy in embedded analytics? Multi-tenancy means a single platform deployment serves multiple distinct customer environments, each fully isolated from the others. Customer A cannot access or see Customer B's data. This isolation is enforced through row-level security at the data layer, ensuring filters are applied based on the authenticated user before any query result is returned. Multi-tenancy is a non-negotiable requirement for any SaaS product embedding analytics for multiple customers. ### 5. What embedding methods should I look for? At minimum, look for iframe support and a JavaScript SDK. Better tools also offer React and Vue components that integrate natively into your frontend without iframe performance overhead. Native components load faster, respond better on mobile, and integrate more cleanly with your application's navigation and state management. Draxlr supports iframe, HTML, React JS, and Vue JS. Luzmo offers a full developer SDK alongside iframe. Looker, Sisense, and Power BI Embedded all offer SDK-based integration. ### 6. What are the best GoodData alternatives in 2026? The best alternatives depend on your specific requirements. Draxlr is the strongest option for SaaS teams that want transparent pricing, fast deployment, and straightforward embedded analytics without enterprise overhead. Sisense is well regarded for performance on large datasets and publishes its pricing. Luzmo is built specifically for SaaS product embedding. Omni covers both internal BI and embedded analytics from a single semantic layer. Power BI Embedded is the natural fit for Microsoft-first organizations. ### 7. Which GoodData alternative is best for embedded analytics? Draxlr and Luzmo are the most purpose-aligned tools for embedded analytics use cases. Draxlr offers transparent flat-rate pricing, unlimited external viewers, and a short deployment timeline. Luzmo is built from the ground up for SaaS product embedding with separate no-code and SDK tracks. ### 8. Which GoodData alternative is best for non-technical users? Metabase and Draxlr are the most accessible for non-technical users. They both have a visual query builder and AI-powered natural language querying, letting internal users ask questions in plain English.

Best Yellowfin BI Alternatives in 2026

hello@draxlr.com (Draxlr) — Fri, 08 May 2026 00:00:00 GMT

Finding the right Yellowfin BI alternative can have a significant impact on how your team builds dashboards, explores data, and delivers analytics to internal stakeholders or external customers. While Yellowfin is a well-regarded business intelligence platform with solid collaboration features and its proprietary Signals engine for automated anomaly detection, it is not the right fit for every organization. Teams running complex embedded analytics workloads, organizations with budget constraints, and companies that need deeper AI capabilities regularly evaluate Yellowfin BI alternatives. Whether you are frustrated by Yellowfin's performance at scale, its steep learning curve for non-technical users, its cost structure, or simply want to explore what the current BI landscape offers, this guide covers the strongest alternatives available today. Each option has been evaluated against the dimensions that matter most: ease of use, pricing transparency, embedded analytics capability, AI features, database connectivity, and enterprise readiness. --- ## What is Yellowfin BI? Yellowfin BI is a business intelligence and analytics platform founded in 2003 and acquired by Idera Inc. in January 2022. It is used by organizations in healthcare, retail, financial services, manufacturing, higher education, hospitality, and utilities to build dashboards, automate reporting, and surface data-driven insights across technical and non-technical audiences alike. The platform is built around four core products that work together as a suite. **Yellowfin Dashboards** provides interactive data visualization and KPI tracking. **Yellowfin Signals** is the platform's most distinctive capability — an automated anomaly detection and monitoring engine that continuously scans live data, identifies statistically significant changes, and proactively alerts users to outliers, trends, and unusual patterns without requiring manual threshold configuration. **Yellowfin Stories** enables teams to build narrative reports that combine charts with written context, turning raw data into guided presentations for non-technical stakeholders. **Yellowfin Present** takes this further by assembling those stories into polished, board-ready slide presentations driven by live data. Yellowfin supports both cloud-hosted and on-premises deployment, which makes it attractive to regulated industries where data residency matters. Its embedded analytics capability allows software vendors to white-label and embed Yellowfin dashboards inside their own products through a JavaScript API. Where Yellowfin earns genuine praise is in the maturity of its automated monitoring and its collaborative storytelling features — areas where many competing BI tools require third-party integrations or custom development to match. Where it falls short is in performance at high data volumes, per-user licensing costs at scale, and the engineering effort required to achieve production-grade embedded analytics. These gaps explain why so many organizations actively search for a Yellowfin BI alternative. --- ## Why consider a Yellowfin BI alternative? Before comparing alternatives, it is worth understanding the specific limitations that drive users away from Yellowfin. These pain points are consistent across recent reviews and are not edge cases — they reflect real constraints of the platform architecture and pricing model. ### 1. Performance degradation at high data volumes Approximately 95% of recent reviews that mentioned performance cited slowdowns when querying large datasets. Yellowfin's query engine can struggle with high-concurrency workloads and complex joins across large tables. Teams that started on Yellowfin with modest data volumes often find themselves looking for a Yellowfin BI alternative once their data warehouse grows. ### 2. High total cost of ownership Around 71% of users who discussed pricing described Yellowfin as expensive. Its named-user licensing model means per-seat costs accumulate quickly for larger teams. When you factor in server-based licensing for enterprise deployments and the cost of add-on modules, Yellowfin's TCO over three years for a 50-user deployment can approach $150,000 — a significant commitment that not every organization can justify. ### 3. Steep learning curve for non-technical users Approximately 81.5% of reviewers who commented on adoption challenges cited a steep learning curve. Yellowfin offers substantial functionality, but that depth comes with complexity. Business users without SQL knowledge or data training often struggle to self-serve effectively, which increases reliance on centralized data teams and slows analytics delivery. ### 4. Limited visualization customization Users frequently note that while Yellowfin's out-of-the-box charts are functional, making detailed adjustments to visual design, layout, or chart behavior is more restrictive than competing platforms. Teams that need pixel-level control over dashboard presentation — particularly for customer-facing embedded analytics — find Yellowfin's customization ceiling frustrating. ### 5. Embedded analytics complexity Yellowfin does support embedded analytics through its JavaScript API, but achieving production-grade multi-tenancy, row-level security, white labeling, and SSO integration requires substantial engineering effort. SaaS companies embedding analytics into their products often discover that Yellowfin's embedding architecture demands more custom development than purpose-built embedded analytics platforms. ### 6. Smaller ecosystem and community Compared to Tableau, Power BI, or Metabase, Yellowfin has a smaller user base, fewer community resources, and a narrower third-party integration ecosystem. Finding pre-built connectors, community-contributed templates, or developers with Yellowfin expertise is measurably harder. ### 7. AI capabilities lag newer entrants Yellowfin Signals offers automated anomaly detection, but the platform's broader AI story — particularly around natural language querying and text-to-SQL — lags behind newer BI tools that have built AI assistance into the core authoring experience. --- ## What to look for in a Yellowfin BI alternative Choosing a replacement BI platform means evaluating multiple dimensions simultaneously. Here is what to prioritize when comparing your options. ### 1. Ease of use for all skill levels The best BI tools serve both SQL-fluent analysts and business users who prefer point-and-click interfaces. Look for platforms that offer multiple authoring modes — visual query builders, AI-driven natural language querying, and raw SQL access — so your entire organization can self-serve without a training program. ### 2. Embedded analytics and white-label readiness If your use case involves shipping dashboards inside a SaaS product or customer portal, your Yellowfin BI alternative must support iframe, React, or Vue embedding with proper multi-tenant row-level security and white-label branding. Some platforms treat embedding as an afterthought — evaluate it as a first-class capability. ### 3. Transparent, predictable pricing Yellowfin's licensing complexity is a known pain point. Prioritize alternatives with clear pricing pages, flat pricing that does not penalize viewer scale, and honest documentation of what is included versus what costs extra. Per-viewer pricing can make embedded analytics prohibitively expensive at scale. ### 4. AI and text-to-SQL capabilities Modern BI tools increasingly offer AI assistants that convert plain English questions into SQL queries, surface automated insights, and recommend visualizations. These features materially reduce the time from question to answer for non-technical stakeholders. ### 5. Database connectivity breadth Your Yellowfin BI alternative should connect natively to your existing data stack — whether that is PostgreSQL, MySQL, Snowflake, BigQuery, Redshift, ClickHouse, Databricks, or another warehouse — without requiring extra ETL pipelines or data duplication. ### 6. Performance at scale Test your candidate platforms against your actual data volumes and query patterns. Some tools that shine in demos degrade under concurrent usage or with large table scans. Real-world performance testing before commitment is non-negotiable. ### 7. Alerting and monitoring Yellowfin Signals is one of the platform's strongest capabilities — automated monitoring that flags anomalies and outliers without manual threshold configuration. Look for alternatives that match or exceed this with scheduled alerts, threshold-based notifications, and integrations with Slack and email. ### 8. Governance and security Enterprise deployments require row-level security, role-based access control, audit logging, SSO, and compliance certifications. Verify that your chosen alternative handles these at the level your organization requires — particularly SOC 2 Type II, GDPR, and HIPAA certifications for regulated industries like healthcare and financial services. ### 9. Augmented analytics and headless BI readiness The BI landscape in 2026 is moving beyond static dashboards. Augmented analytics — the use of AI and machine learning to automate data preparation, anomaly detection, insight generation, and narrative summarization — is rapidly becoming a baseline expectation rather than a premium feature. Separately, headless BI is emerging as an architectural pattern where metrics are defined once in a governed semantic layer and delivered anywhere: dashboards, Slack, APIs, spreadsheets, or embedded product experiences. When evaluating a Yellowfin BI alternative, assess whether the platform is building toward augmented analytics and composable delivery, or whether it is locked into a traditional report-and-dashboard model that is increasingly constraining. --- ## Best Yellowfin BI alternatives in 2026 at a glance

Tool	Best use case	Ease of use	AI capabilities	Embedded analytics	Starting price
Yellowfin BI	Collaborative enterprise BI with anomaly detection	Medium	Moderate (Signals)	Available but complex	$50/user/month
Draxlr	AI powered analytics and embedded dashboards for SaaS	High	Strong (Text-to-SQL)	First-class, flat pricing	$75/month flat
Tableau	Enterprise data visualization and exploration	Medium	Moderate	Available (Embedded Analytics)	$70/user/month
Microsoft Power BI	Microsoft ecosystem analytics	Medium	Moderate (Copilot)	Available (Premium required)	$10/user/month
Looker	Governed semantic layer analytics	Low	Moderate	Available (SDK)	Custom pricing
Metabase	Fast self-service BI for small teams	High	Limited	Basic (Pro and Enterprise)	Free (open source)
Sisense	Embedded analytics for product teams	Medium	Moderate	Strong (Compose SDK)	Custom pricing
Domo	All-in-one cloud BI with ETL and real-time data	Medium	Moderate	Available (Domo Everywhere)	Custom pricing
Qlik Sense	Enterprise analytics with associative data engine	Medium	Moderate (AutoML)	Available (Qlik Embedded)	$70/user/month
ThoughtSpot	AI-native search-driven analytics	High	Strong (SpotIQ, NLQ)	Available (Embedded Analytics)	Free developer tier

--- ## 1. Draxlr ### AI powered analytics and embedded dashboards built for teams that need speed ![Draxlr](/images/blogs/_tools/draxlr.png) [Draxlr](https://www.draxlr.com/) is the strongest Yellowfin BI alternative for teams that prioritize ease of use, AI-driven querying, and embedded analytics without complexity. Where Yellowfin requires navigating a feature-rich but occasionally opaque interface, Draxlr offers a clean, immediate experience: connect your database, ask a question in plain English, and get a dashboard in minutes. Its Text-to-SQL AI converts natural language questions into optimized queries — eliminating the SQL barrier that blocks business users from self-serving on Yellowfin. For SaaS companies specifically, Draxlr's embedded analytics story is compelling. Flat pricing at $75 per month with unlimited external viewers means you can expose dashboards to thousands of customers without per-viewer licensing costs spiraling. Draxlr supports iframe, React SDK, and Vue SDK embedding out of the box, with configurable row-level security, white-label branding, domain restrictions, and drill-down behavior — the features that Yellowfin requires custom engineering to achieve. Teams migrating from Yellowfin consistently highlight three improvements: faster onboarding for non-technical users, lower maintenance overhead since Draxlr manages infrastructure, and dramatically simpler embedded analytics integration. Connect your Database ### Key Features - AI powered Text-to-SQL that converts plain English into optimized SQL queries — no analyst required - Three authoring modes: AI chat, Visual Query Builder, and raw SQL editor for power users - Native connections to PostgreSQL, MySQL, SQL Server, BigQuery, Snowflake, ClickHouse, Redshift, Databricks, MariaDB, CockroachDB, and more - Embedded dashboards via iframe, React SDK, or Vue SDK with row-level security per tenant - White-label customization including colors, logos, domain restrictions, and export controls - Scheduled email and Slack alerts triggered by threshold conditions or data changes - Interactive filters, drill-downs, and cross-chart filtering on live warehouse data - Role-based access controls and workspace isolation for multi-team environments - Zero infrastructure overhead — no servers to provision, scale, or maintain - Unlimited external viewers on every paid plan with flat, transparent pricing ### Pricing Draxlr's embedding features start at **$75 per month** (Premium plan) with unlimited external viewers — a pricing structure that directly solves the per-seat cost problem that plagues Yellowfin deployments at scale. Higher tiers unlock dedicated support, SSO, and custom integrations. There are no hidden fees for connectors, API calls, or viewer counts. A free trial is available to test the platform before committing. --- ## 2. Tableau ### Industry-standard visualization power for data-intensive enterprises ![Tableau](/images/blogs/yellowfin-bi-alternatives-2026/tableau.png) [Tableau](https://www.tableau.com/) is the most recognized name in enterprise business intelligence and consistently appears at the top of Yellowfin BI competitor comparisons. While Yellowfin scored 8.7 on overall quality ratings, Tableau achieved 9.2 — driven by its unmatched visualization depth, the VizQL engine that translates drag-and-drop interactions into database queries automatically, and a mature ecosystem of training resources, community forums, and third-party integrations. Where Yellowfin's charting capabilities can feel limiting when you need precise visual customization, Tableau gives analysts pixel-level control over every visual element. Its desktop authoring experience enables exploratory analysis workflows that are difficult to replicate in browser-based BI tools. For organizations where visualization quality directly influences executive decision-making or client presentations, Tableau's output is consistently the benchmark. The tradeoff is cost. Tableau Creator licenses start at $70 per user per month, making it more expensive than Yellowfin for comparable user counts. Large viewer pools are expensive, and embedded analytics requires Tableau Embedded Analytics licensing that adds further cost. Teams with tight budgets or high viewer-to-author ratios may find Tableau's licensing model unsustainable. ### Key Features - VizQL engine that translates visual interactions into optimized SQL queries against live connections - Desktop authoring with granular chart design and layout control across dozens of visualization types - Native connectors to virtually every major database, warehouse, cloud platform, and SaaS app - Tableau Prep for visual, no-code data cleaning and transformation pipelines - Built-in statistical functions, trend lines, clustering, forecasting, and R/Python integration - Tableau Pulse (AI powered) for automated metric summaries and natural language insights - Tableau Server and Tableau Cloud for centralized governance, scheduling, and version control - Row-level security, content access controls, and Salesforce identity integration for enterprise governance ### Pricing Tableau pricing starts at **$70 per user per month** for Creator licenses and scales down to Explorer and Viewer tiers. Embedded Analytics requires separate pricing discussions with Salesforce. Three-year TCO for a 50-user deployment is estimated around $175,000 — higher than Yellowfin but justified for organizations where visualization sophistication directly drives outcomes. A 14-day free trial is available for Tableau Cloud. --- ## 3. Microsoft Power BI ### The natural Yellowfin alternative for Microsoft-centered organizations ![Power BI](/images/blogs/yellowfin-bi-alternatives-2026/power-bi.png) [Microsoft Power BI](https://powerbi.microsoft.com/) is the dominant choice for organizations already running on Azure, Microsoft 365, SQL Server, or Dynamics 365. Where Yellowfin requires configuring database connections and managing authentication independently, Power BI connects to Excel spreadsheets, SharePoint lists, Azure Synapse Analytics, and Dataverse with near-zero configuration. Its deep integration with Microsoft Teams means dashboards surface directly where collaboration happens, reducing friction in sharing and discussing insights. Power BI's DAX modeling language is a meaningful capability advantage over Yellowfin for teams needing complex calculated measures, time intelligence functions, and hierarchical data models. Its natural language Q&A feature enables business users to type questions like "show revenue by region last quarter" and receive auto-generated charts — a self-service capability that partially compensates for the steeper learning curve of the full authoring interface. The three-year TCO for a 50-user Power BI deployment is approximately $120,000 — meaningfully lower than Yellowfin's estimated $150,000 for the same deployment size. For embedded analytics at scale, Power BI Premium capacities are required, which significantly increases cost, but for internal analytics use cases, Power BI Pro is among the most cost-efficient options available. ### Key Features - One-click connectivity to Azure, SQL Server, Excel, SharePoint, Dataverse, Dynamics 365, and hundreds of other sources - DAX formula engine with calculated columns, measures, KPIs, and time intelligence for complex data modeling - Power BI Copilot for AI-assisted report creation, natural language querying, and automated narrative summaries - Power BI Embedded SDK for integrating reports into custom web and mobile applications - Row-level security, sensitivity labels, and Microsoft Entra ID integration for enterprise governance - Paginated reports for pixel-perfect financial statements, invoices, and printable operational reports - Incremental refresh and composite models for handling large datasets without performance degradation - Direct Lake mode for real-time access to OneLake data without import or DirectQuery latency trade-offs ### Pricing Power BI Pro costs **$10 per user per month**, making it the most affordable commercial Yellowfin alternative for internal analytics. Premium Per User (PPU) at $20/user/month unlocks advanced features including paginated reports, AI capabilities, and larger refresh frequencies. Embedded Analytics at scale requires Premium Per Capacity pricing, which starts at several thousand dollars per month and scales by workload. Power BI Desktop is free to download, and Power BI Pro includes a 60-day free trial. --- ## 4. Looker ### Governed, semantic-layer analytics for engineering-led data organizations ![Looker](/images/blogs/yellowfin-bi-alternatives-2026/looker.png) [Looker](https://cloud.google.com/looker), now part of Google Cloud, takes a philosophically different approach to BI than Yellowfin. Instead of allowing each analyst to write ad-hoc SQL for individual reports — which leads to metric drift where different teams calculate the same KPI differently — Looker uses LookML, a version-controlled modeling language that defines business logic once and reuses it everywhere. This eliminates the inconsistency that grows organically in Yellowfin deployments where dozens of analysts maintain their own report definitions. For organizations where data consistency and governance are non-negotiable — financial services, healthcare, large-scale e-commerce — Looker's semantic layer approach provides structural assurances that Yellowfin cannot match. Metrics defined in LookML flow through every dashboard, every API query, and every embedded report without divergence. The practical limitation is the learning curve and engineering investment. LookML requires data engineering skills that business users do not have. Deploying Looker effectively requires dedicated data engineering resources, a meaningful implementation timeline, and budget for LookML model development. Per-viewer pricing also makes Looker economically challenging for SaaS companies embedding analytics into their products at scale. ### Key Features - LookML semantic layer that centralizes business logic, metric definitions, and access controls - Git-integrated development workflow for version control, code review, and CI/CD on analytics models - Looker Studio (formerly Google Data Studio) integration for lightweight, shareable reporting - Looker API and Action Hub for triggering workflows, pushing data to Slack, or writing query results to external systems - Native BigQuery optimization with awareness of partitioning, BI Engine, and clustering - Embedded analytics via iframes and the Looker Embed SDK with SSO, content filtering, and user attributes - Granular field-level security tied to user attributes for row-level and column-level access control ### Pricing Looker pricing is contract-based and negotiated per deployment. It is positioned firmly in the mid-market to enterprise segment with per-user costs that typically exceed Yellowfin. Smaller teams and startups generally find Looker cost-prohibitive. Organizations with mature data engineering capabilities and strict governance requirements typically see strong ROI. Google Cloud offers a 30-day free trial for Looker Studio Pro; the full Looker platform requires a sales engagement. --- ## 5. Metabase ### The fastest path to self-service BI for small teams and startups ![Metabase](/images/blogs/yellowfin-bi-alternatives-2026/metabase.png) [Metabase](https://www.metabase.com/) is the most popular open-source Yellowfin BI alternative and the easiest to get started with. Where Yellowfin requires licensing negotiation, onboarding, and configuration before anyone can build their first dashboard, Metabase can be running in under an hour — either self-hosted via Docker or through Metabase Cloud. Its visual question builder requires no SQL knowledge, and its question-and-answer interface guides business users through building charts without training. Metabase's strength is accessibility and speed of initial deployment. Its free, open-source tier is genuinely capable for internal analytics up to moderate complexity. For teams that found Yellowfin's interface overwhelming or whose non-technical stakeholders struggled to self-serve, Metabase's simpler interface is immediately impactful. The limitations emerge at scale. Metabase's multi-tenancy support requires significant custom configuration, white-label branding is locked behind paid tiers, and row-level security is less sophisticated than Yellowfin's enterprise access controls. Teams embedding analytics into SaaS products frequently hit Metabase's ceiling around multi-tenancy and governance and look for alternatives that handle these requirements natively. ### Key Features - Visual question builder that enables non-SQL users to explore data through guided interface flows - Native SQL editor for analysts who prefer direct query access with auto-complete and syntax highlighting - Dashboards with click-through filters, cross-filtering, and scheduled email subscriptions - Metabase Embedding SDK for React-based embedding with SSO and permission-based content filtering - Automated X-rays that generate instant exploratory charts for any table or segment - Over 50 native database drivers covering SQL and NoSQL sources - Open-source deployment via Docker, Kubernetes, or Metabase Cloud with optional managed hosting - LDAP and SAML SSO support on Pro and Enterprise plans ### Pricing Metabase is **free to self-host** on the open-source tier, making it the most accessible Yellowfin alternative by initial cost. Metabase Cloud starts at $500/month. Pro and Enterprise tiers add SSO, advanced permissions, white labeling, and dedicated support. The open-source option works well for internal analytics but requires DevOps capacity to maintain and scale. No free trial is needed for the open-source edition — it is free to use indefinitely. --- ## 6. Sisense ### Purpose-built embedded analytics with mature multi-tenancy for product teams ![Sisense](/images/blogs/yellowfin-bi-alternatives-2026/sisense.png) [Sisense](https://www.sisense.com/) is a strong Yellowfin BI alternative for SaaS companies and product teams whose primary analytics use case is embedding insights into customer-facing applications. While Yellowfin offers embedded analytics through its JavaScript API, Sisense treats embedded analytics as its core product — the Compose SDK is built specifically for developers integrating analytics into products, with mature multi-tenant architecture, SSO, white labeling, and granular permission management available without custom engineering. Sisense's in-chip technology accelerates complex queries by caching columnar data in memory, which addresses one of Yellowfin's known weaknesses around performance degradation at high data volumes. For product teams dealing with many concurrent customers querying the same underlying datasets, Sisense's query architecture handles concurrency better than most alternatives. Sisense also retains strong internal BI capabilities alongside its embedded story — analytics teams can build governed dashboards in the same platform where developers embed customer-facing reports, eliminating the need to maintain two separate BI tools. ### Key Features - Sisense Compose SDK for React-based embedded analytics with component-level customization - Multi-tenant data security with row-level security and tenant isolation out of the box - White-label customization including full theme control, custom CSS, and domain restrictions - In-chip query acceleration that caches columnar data in memory for high-concurrency performance - Flexible deployment — cloud-hosted, on-premises, or hybrid to meet compliance requirements - AI-assisted analytics with natural language querying and automated insight surfacing - Pre-built data connectors for databases, cloud warehouses, SaaS applications, and APIs - Enterprise governance including audit logs, SAML SSO, and role-based access controls ### Pricing Sisense uses custom contract pricing, typically negotiated based on deployment size, user count, and embedding requirements. It is positioned in the mid-market to enterprise segment. While more expensive than Metabase or Power BI for pure internal analytics, Sisense's TCO is competitive for SaaS companies that would otherwise need to build custom embedded analytics infrastructure. A free trial and demo are available through the Sisense sales team. --- ## 7. Domo ### All-in-one cloud platform that eliminates your fragmented BI stack ![Domo](/images/blogs/yellowfin-bi-alternatives-2026/domo.png) [Domo](https://www.domo.com/) goes beyond replacing Yellowfin as a visualization tool — it aims to consolidate your entire data pipeline. Where a typical Yellowfin deployment requires separate tools for ETL, data warehousing, and dashboarding, Domo bundles data ingestion, transformation, storage, and visualization into a single cloud platform. This integrated approach is particularly appealing to operations teams and executives who want real-time KPI dashboards without depending on multiple vendor relationships. Users who switched from Yellowfin to Domo consistently describe the transition as "a breath of fresh air" in terms of ease of use and data source management. Domo's 1,000+ pre-built connectors reduce the integration work that Yellowfin leaves to customers, and its Magic ETL pipeline builder enables non-technical users to transform data without writing code. Domo Everywhere provides embedded analytics for external-facing deployments, with features comparable to Yellowfin's embedded offerings but integrated into the same platform as your internal BI rather than managed separately. ### Key Features - Over 1,000 pre-built data connectors covering SaaS apps, databases, cloud storage, and REST APIs - Magic ETL — a no-code, drag-and-drop data transformation pipeline builder - Domo AI with natural language querying, automated insights, and AI-generated summaries - Domo Everywhere for embedding dashboards and KPIs in external applications and partner portals - Buzz — a built-in messaging layer for discussing data directly within dashboard cards - DataFusions and DataFlows for blending, transforming, and enriching data without a separate warehouse - Mobile-first design with a fully featured iOS and Android app for real-time decision-making - Domo Appstore with pre-built dashboard templates for finance, sales, operations, and marketing ### Pricing Domo uses custom contract pricing that scales by user count, data volume, and connector requirements. It is generally positioned toward mid-market and enterprise buyers. The all-in-one approach can reduce total spend by consolidating tools that would otherwise be maintained separately, but individual licensing costs are higher than point solutions like Power BI or Metabase. A free trial is available directly through Domo's website. --- ## 8. Qlik Sense ### Associative analytics engine for enterprises that need unrestricted data exploration ![Qlik Sense](/images/blogs/yellowfin-bi-alternatives-2026/qlik-sense.png) [Qlik Sense](https://www.qlik.com/us/products/qlik-sense) is one of the most established Yellowfin BI alternatives for large enterprises that need unrestricted, multi-directional data exploration. Yellowfin follows a conventional query model where analysis flows from pre-defined relationships between tables. Qlik Sense uses an associative engine that keeps all data relationships in memory simultaneously — meaning analysts can click any data point, in any chart, in any sequence, and the entire dashboard responds without being constrained by defined query paths. This approach consistently surfaces unexpected connections in data that directed-query tools miss entirely. Where Yellowfin's AI story centers on automated anomaly detection via Signals, Qlik has invested heavily in AutoML capabilities that allow data teams to build predictive models directly inside the BI environment — without exporting data to a separate machine learning platform. For enterprises where predictive analytics and exploration depth matter more than ease of initial setup, Qlik Sense represents a meaningful upgrade over Yellowfin. The tradeoffs are real: Qlik Sense has a steeper learning curve than most tools in this list, a three-year TCO that approaches $200,000 for a 50-user deployment (higher than Yellowfin), and an interface that rewards dedicated training. Teams expecting rapid self-service adoption without structured onboarding will struggle. ### Key Features - Associative engine that holds all data relationships in memory for click-anywhere, unrestricted exploration - AutoML for in-platform predictive model building without exporting to a separate data science tool - Qlik Sense SaaS for fully managed cloud deployment with automated updates and elastic scaling - Qlik NPrinting for pixel-perfect scheduled reports distributed to thousands of users via email or portal - Native connectors to databases, cloud warehouses, SAP, Salesforce, and hundreds of SaaS applications - Active Intelligence with real-time streaming analytics for time-sensitive operational dashboards - Qlik Embedded Analytics for embedding visualizations into custom applications with row-level security - Governance, lineage tracking, and impact analysis for enterprise data management at scale ### Pricing Qlik Sense starts at **$70 per user per month** for the cloud edition. Enterprise and embedded deployments are contract-based. The three-year TCO for a 50-user deployment is estimated at approximately $200,000 — the highest in this comparison — but organizations that need the associative engine's exploration depth consistently find the investment justified. A 30-day free trial is available for Qlik Cloud Analytics. --- ## 9. ThoughtSpot ### AI-native search-driven analytics for organizations that want every employee to self-serve ![ThoughtSpot](/images/blogs/yellowfin-bi-alternatives-2026/thoughtspot.png) [ThoughtSpot](https://www.thoughtspot.com/) takes the most radical departure from Yellowfin's architecture of any tool in this list. Where Yellowfin is a traditional BI platform where analysts build reports and dashboards that others consume, ThoughtSpot is built around the premise that every employee should be able to search their data the same way they search the internet — typing plain English questions and receiving instant, AI-generated answers. There are no dashboard authoring workflows to learn, no chart-type selection menus to navigate, and no reliance on a central analytics team to pre-build reports. ThoughtSpot's SpotIQ engine runs continuously in the background, automatically analyzing data for statistically significant patterns, anomalies, and trends — a capability directly comparable to Yellowfin Signals, but with more sophisticated statistical underpinning and natural language output that explains what it found and why it matters. For Yellowfin users who are frustrated that non-technical stakeholders still cannot self-serve despite the platform's visual interface, ThoughtSpot eliminates that problem structurally. The limitation is that ThoughtSpot's Liveboards, while interactive and AI-powered, offer less white-label customization than Yellowfin for embedded analytics use cases. Teams that need full brand control over embedded dashboards should weigh this carefully. ### Key Features - Natural language search that converts plain English questions into instant charts and data answers - SpotIQ AI engine that continuously analyzes data for anomalies, trends, and statistically significant changes - Liveboards — interactive, real-time dashboards that update as users ask follow-up questions - Analyst Studio for deeper work with SQL, R, or Python when natural language is insufficient - ThoughtSpot Embedded Analytics for integrating search-driven analytics into SaaS applications - Developer plan with free access for building and testing embedding workflows - Connections to Snowflake, BigQuery, Redshift, Databricks, SQL Server, and other major warehouses - Worksheets for curating governed data models that shape what employees can search and how results are presented ### Pricing ThoughtSpot offers a **free Developer plan** for testing embedding workflows and exploring the platform. The Team plan starts at $95 per user per month. Enterprise pricing is custom-negotiated based on user count and deployment requirements. For organizations where the primary goal is eliminating the analytics bottleneck by enabling every employee to self-serve, ThoughtSpot's per-user cost is justified by the reduction in analyst time spent fielding ad-hoc report requests. --- ## How to choose the right Yellowfin BI alternative With nine strong options evaluated, narrowing to the right choice depends on which of these profiles best describes your situation. **Choose Draxlr** if your primary need is embedded analytics for a SaaS product, you want AI-driven self-service querying that works for non-technical users, and you want flat pricing without per-viewer fees. Draxlr is also ideal if you connect directly to SQL databases and want to eliminate the ETL and semantic layer complexity. **Choose Tableau** if visualization sophistication is your top priority, your data team needs advanced exploratory analytics capabilities, and your organization can justify premium per-user licensing. Tableau is best when the quality of visual output directly influences executive or client decisions. **Choose Power BI** if your organization runs on the Microsoft stack, you need the lowest per-user cost for internal analytics, and you want tight integration with Azure, Teams, and Office 365. Power BI is the pragmatic choice for Microsoft-aligned organizations that want commercial support without Yellowfin's cost. **Choose Looker** if your organization has dedicated data engineering resources, metric consistency across a large, distributed team is critical, and you are running on BigQuery or another major cloud warehouse where LookML's semantic layer provides a structural advantage. **Choose Metabase** if you need the fastest path to working dashboards, your team is small, and budget is the primary constraint. Metabase is the right starting point for organizations that are not yet ready to invest in a commercial platform. **Choose Sisense** if embedded analytics inside your product is the core use case, you need mature multi-tenant data security without custom engineering, and you want a single vendor for both product-embedded and internal analytics. **Choose Domo** if you want to consolidate your ETL, warehousing, and visualization stack into a single platform, and you have budget for an all-in-one solution that reduces the complexity of managing multiple vendor relationships. **Choose Qlik Sense** if unrestricted, associative data exploration is a hard requirement, your analysts regularly discover insights by clicking across dimensions in unexpected sequences, and your organization has the budget and onboarding capacity for a premium enterprise platform. **Choose ThoughtSpot** if your primary bottleneck is that non-technical employees still cannot self-serve despite having a BI tool, you want AI-native search to replace the traditional dashboard authoring workflow entirely, and your analysts spend too much time fielding ad-hoc report requests that a search interface would eliminate. --- ## Which Yellowfin BI alternative is best by industry? The right Yellowfin BI alternative often depends as much on the industry context as on feature specifications. Here is how the leading options map to common sector-specific requirements. **Healthcare and life sciences** organizations must handle PHI data under HIPAA constraints, require strict access controls, and often need audit trails for regulatory reporting. Power BI's integration with Azure Health Data Services and Microsoft's HIPAA-eligible cloud infrastructure make it the natural choice for Microsoft-aligned healthcare systems. Looker is favored in larger health networks for enforcing metric consistency across departments — ensuring that "readmission rate" means the same thing in every report system-wide. **Financial services and banking** teams need real-time fraud detection dashboards, complex calculated risk metrics, and support for regulatory reporting formats. Qlik Sense's associative engine is particularly well-suited here — analysts can click from an anomalous transaction amount across every related dimension simultaneously without pre-defining the analysis path. Power BI's paginated reports handle the pixel-perfect regulatory output formats that banks require. **SaaS and technology companies** embedding analytics in their products should evaluate Draxlr and Sisense first. Both platforms are built with multi-tenant, customer-facing analytics as a primary use case — not an afterthought. Draxlr's flat pricing makes it cost-effective for startups and growth-stage companies; Sisense serves more complex enterprise-grade product requirements. **Retail and e-commerce** organizations need real-time inventory tracking, campaign performance attribution, and granular customer segmentation. Domo's 1,000+ connectors — including direct integrations with Shopify, Google Ads, Meta Ads, and major ERPs — reduce integration engineering significantly. ThoughtSpot's natural language search enables merchandising and marketing teams to self-serve on sales data without routing requests through a central analytics team. **Manufacturing and supply chain** operations benefit from Yellowfin's Signals-style automated monitoring, but for teams that need more — real-time streaming dashboards, predictive maintenance analytics, or integration with IoT data pipelines — Qlik Sense's Active Intelligence and Domo's real-time connector ecosystem are the strongest alternatives. **Higher education and public sector** organizations often have limited BI budgets and heterogeneous data environments. Metabase is a natural fit for universities and public agencies that need cost-effective, accessible analytics without long procurement cycles. Power BI is also widely used in education thanks to free licensing available for qualified institutions through Microsoft's academic programs. --- ## Conclusion Yellowfin BI is a capable platform with genuine strengths in automated anomaly detection via Signals, collaborative data storytelling features, and flexible deployment options. But for organizations running into performance ceilings, wrestling with per-user licensing costs, struggling to onboard non-technical users, or trying to deliver embedded analytics to external customers, the limitations are real and worth addressing. The Yellowfin BI alternatives covered in this guide span a wide range of use cases, price points, and technical profiles. There is no universally correct replacement — the right choice depends on the specific pain points driving your evaluation. Before committing to any platform, run a structured proof of concept against your actual data, your realistic user population, and your specific use cases. Most vendors offer trial access or implementation support to facilitate this. The BI landscape in 2026 rewards teams that match their analytics platform to their actual needs rather than selecting by brand recognition or analyst ranking alone. With clear requirements and hands-on evaluation, finding the right Yellowfin BI alternative is a solvable problem. --- ## FAQs ### 1. What are the best Yellowfin BI alternatives in 2026? The best Yellowfin BI alternatives in 2026 include Draxlr, Tableau, Microsoft Power BI, Looker, Metabase, Sisense, Domo, Qlik Sense, and ThoughtSpot. The right choice depends on your specific needs — AI powered analytics, embedded analytics for SaaS products, Microsoft ecosystem integration, open-source flexibility, associative data exploration, or AI-native search analytics. ### 2. Why do teams switch from Yellowfin BI? Teams typically switch from Yellowfin because of performance degradation at high data volumes, high per-user licensing costs, steep learning curves for non-technical users, limited visualization customization, and the engineering complexity required for embedded analytics. Newer platforms have closed many of these gaps while offering stronger AI capabilities. ### 3. Which Yellowfin BI alternative is best for embedded analytics? For embedded analytics, Draxlr and Sisense are the strongest choices. Draxlr offers flat pricing with unlimited external viewers and React/Vue SDK embedding, making it cost-effective for SaaS products at scale. Sisense provides a mature Compose SDK with enterprise multi-tenancy and white-labeling for complex product analytics requirements. ### 4. Which Yellowfin BI alternative is cheapest? Metabase is free to self-host on the open-source tier, making it the lowest-cost entry point. Microsoft Power BI Pro starts at $10 per user per month for commercial use. Draxlr starts at $75 per month flat with unlimited viewers, which is highly cost-effective for embedded analytics use cases. ### 5. Which Yellowfin BI alternative is best for non-technical users? Draxlr and Metabase are the most accessible for non-technical users. Draxlr's AI chat lets business users ask questions in plain English and receive charts without any SQL knowledge. Metabase's visual question builder guides users through data exploration without requiring technical skills. ### 6. Can I migrate from Yellowfin to another BI tool without rebuilding everything? Most Yellowfin alternatives connect to the same underlying databases, which means you point the new tool at your existing data sources and rebuild dashboards in the new platform. The rebuilding effort depends on the complexity of your existing reports. Most vendors offer implementation support, and simple dashboards can typically be recreated in days rather than weeks. ### 7. Is Tableau better than Yellowfin BI? Tableau generally scores higher for visualization capabilities and has a larger ecosystem of community resources and third-party integrations. However, Tableau is more expensive, and its per-user pricing model is challenging for large viewer audiences. Yellowfin's automated anomaly detection (Signals) and data storytelling features have no direct Tableau equivalent. The better choice depends on whether visualization depth or automated monitoring is your primary requirement. ### 8. Is there a free version of Yellowfin BI? Yellowfin BI does not offer a permanently free tier. It provides a free trial and free demo access for evaluation, but all production deployments require a paid license. This is a meaningful contrast to alternatives like Metabase, which is genuinely free to self-host indefinitely on the open-source edition, Power BI Desktop which is free to download, or ThoughtSpot's free Developer plan. If a free or low-cost starting point is a requirement, those three options are the strongest alternatives to evaluate. ### 9. What industries use Yellowfin BI? Yellowfin BI has its largest footprint in financial services, healthcare, higher education, hospitality, insurance, manufacturing, retail, and utilities. Its data storytelling and automated anomaly detection capabilities are particularly valued in healthcare (where surfacing unexpected patient outcome patterns quickly matters) and financial services (where regulatory reporting and fraud detection benefit from Signals-style automated monitoring). Its flexible on-premises and cloud deployment options make it attractive to regulated industries with data residency requirements. ### 10. Who acquired Yellowfin BI? Yellowfin was acquired by Idera Inc. in January 2022. Idera is a B2B software holding company that owns more than 30 developer and data tools brands. The acquisition placed Yellowfin alongside other Idera portfolio products including FusionCharts, Embarcadero, and Sencha. Some organizations evaluating Yellowfin factor the acquisition into their decision, as consolidation under a holding company can shift product roadmap priorities and support quality over time. ### 11. When should you stay on Yellowfin BI rather than switching? Yellowfin remains a strong choice when automated data monitoring is central to your workflow — its Signals engine for anomaly detection and proactive alerting is more mature than most alternatives at this price point. It also suits organizations that need its built-in data storytelling features (Yellowfin Stories and Yellowfin Present) for presenting insights to non-technical audiences, and teams that require flexible on-premises or hybrid deployment without the infrastructure management overhead of open-source platforms. If these specific capabilities are core to your analytics workflow and the platform's performance and cost are acceptable for your scale, switching may not be worth the migration effort.

Best AI SQL Tools in 2026

hello@draxlr.com (Draxlr) — Thu, 07 May 2026 00:00:00 GMT

**AI SQL tools** have moved from novelty to necessity in 2026. What used to take a data analyst an afternoon — joining tables, writing CASE statements, debugging a missing GROUP BY — now happens in a sentence. You type _"Show me weekly active users by plan for the last 8 weeks"_ and get a working SQL query, a chart, and a dashboard tile, all within seconds. But the market has exploded. There are now dozens of tools claiming to be the "best AI SQL tool," and most of them aren't. Some are wrappers around ChatGPT with no schema awareness. Others are enterprise BI suites that bolt on AI as a $100/month add-on. A few are genuinely useful, accurate, and priced for real teams. This guide breaks down the **best AI SQL tools** worth using in 2026 — what they do well, where they fall short, and which one fits your team's stack and budget. The list focuses on tools that are actively maintained, have transparent pricing, and ground their AI in your real database schema rather than guessing column names from a generic LLM. If you're new to the category, [our guide on generating SQL queries with AI](/blogs/how-to-generate-sql-query-with-ai) covers the basics first. If you want answers from your database without writing SQL, this is the shortlist. --- ## What is an AI SQL tool? An **AI SQL tool** is software that converts plain-English questions into working SQL queries by reading your database schema and using a large language model to generate, validate, and execute the query against your data. The good ones do four things well: 1. **Read your schema** — table names, column types, foreign keys, and relationships — so the AI knows what data exists before it tries to query it. 2. **Generate accurate SQL** — picking the right joins, filters, and aggregations based on your question's intent. 3. **Run the query and visualize the result** — so you see a chart, not just a code block. 4. **Let you refine and reuse** — saving queries, building dashboards, and editing the generated SQL when needed. The difference between a useful AI SQL tool and a frustrating one comes down to schema grounding. Tools that pass your question to a generic LLM with no context will hallucinate column names and produce queries that fail. Tools that index your schema, cache metadata, and validate output before showing it to you produce SQL that actually runs. For a deeper technical breakdown of how text-to-SQL works under the hood, see [Natural Language to SQL: How It Works Under the Hood](/blogs/natural-language-to-sql). --- ## Why AI SQL tools matter in 2026 Three things changed in the past two years that made AI SQL tools genuinely useful instead of just impressive demos: 1. **LLMs got reliable at SQL.** Models like GPT-5, Claude 4, and open-source SQLCoder now handle complex joins, window functions, and CTEs with high accuracy when grounded in a real schema. 2. **Schema-aware retrieval became standard.** The best tools now index your schema, sample data, and past queries — so the AI has context, not just guesses. 3. **Embedded analytics moved into the workflow.** Instead of copying SQL from a chat tool into your database client, modern AI SQL tools run the query, render the chart, and pin it to a dashboard in one motion. The result: teams that used to wait days for an analyst to write a query now get answers in the same meeting they were raised in. Marketing managers stop pinging engineers for signup numbers. Product managers stop building Notion docs out of CSV exports. Founders stop guessing. --- ## Key features to look for in an AI SQL tool Not every AI SQL tool is built the same. Before picking one, make sure it covers these fundamentals. ### 1. Schema-aware text-to-SQL The tool should read your real table names, column types, and relationships before generating a query. Without this, the AI hallucinates column names and produces SQL that fails on the first run. Look for tools that index your schema automatically when you connect a database. ### 2. Direct database connectivity The best AI SQL tools connect directly to your database — [PostgreSQL](/databases/postgres), [MySQL](/databases/mysql), [BigQuery](/databases/bigquery), [Snowflake](/databases/snowflake), [Redshift](/databases/redshift), and so on — rather than asking you to upload CSVs. This keeps your data live and avoids ETL overhead. ### 3. Query transparency and editability You should be able to see the generated SQL, modify it, and rerun it. Black-box tools that hide the SQL feel magical until they get something wrong, and then you have no way to debug it. ### 4. Visualization and dashboards Generating SQL is half the job. The tool should run the query and pick a sensible chart type (bar, line, pie, table) based on the result. Bonus: the ability to save charts to a dashboard, schedule them, and share with the team. See our guide on [SQL data visualization](/blogs/sql-data-visualization) for what good chart suggestions look like in practice. ### 5. Read-only safety A good AI SQL tool supports read-only database connections, encrypts credentials at rest, and never modifies data. This is non-negotiable for production databases. ### 6. Pricing that matches your team size Enterprise BI tools often charge $50–$70 per user per month plus an AI add-on. Standalone AI SQL tools tend to be flat-priced or freemium. For most teams under 50 people, the standalone option is significantly cheaper. ### 7. Custom context for business logic The best tools let you teach the AI about your business — what "active user" means, which columns are deprecated, how status codes map to labels — so queries reflect your real world, not just your raw schema. --- ## How we evaluated these tools This shortlist isn't a market-share leaderboard. Tools were selected and ranked against six criteria that matter when you're actually using one day-to-day: 1. **Schema grounding** — does the AI read your real database schema, or pass the question to a generic LLM? Tools that hallucinate column names on a fresh connection didn't make the list. 2. **Pricing transparency** — products with public pricing rank above sales-led tools where you need a demo just to see a number. 3. **Active maintenance** — we excluded products that have been acquired and shut down (Outerbase) or gone quiet for 12+ months (Ottertune). 4. **Database coverage** — native support for PostgreSQL, MySQL, BigQuery, Snowflake, and Redshift at minimum; bonus for NoSQL stores and self-hosted setups. 5. **Workflow completeness** — tools that take you from question to chart to dashboard rank above tools that only return a SQL string. 6. **Team fit** — who each tool is genuinely useful for, whether that's solo developers, non-technical founders, or compliance-heavy enterprises. We didn't run formal text-to-SQL accuracy benchmarks across tools. Public benchmark scores tend to depend heavily on schema complexity and prompt phrasing, and they rarely match what teams see on their own data. Instead, the focus was on what each tool's architecture, schema-grounding approach, and product surface area make possible — which is closer to what you'll experience after week one. --- ## Quick comparison: best AI SQL tools in 2026

Tool	Best for	Pricing (starts at)	Open source
Draxlr	Teams that want AI SQL plus dashboards on a flat price	$25/month	No (self-host available)
AI2sql	Individual analysts who want a fast SQL generator	$9/month	No
Chat2DB	Developers who want an AI-enhanced database client	Free / $15/user/month	Yes (community)
SQLAI.ai	Broad database coverage including NoSQL	Free / $6/month	No
Vanna AI	Developers embedding text-to-SQL in their own apps	Free (OSS)	Yes (MIT)
Text2SQL.AI	Lightweight NL-to-SQL with API access	Free / paid tiers	No
AskYourDatabase	Non-technical users chatting with large databases	$39/month	No
Defog AI (SQLCoder)	Enterprises with strict data residency needs	Custom (OSS models free)	Yes (models)
Hex (Magic AI)	Data teams in a notebook-style workspace	$28/user/month	No
Metabase (Metabot AI)	Existing Metabase users adding AI on top	$85/month + $100 AI add-on	Yes (community)
DataChat	Niche enterprises trialing no-code chat analytics	Sales-led (no public pricing)	No

--- ## 1. Draxlr **Best AI SQL tool for:** Teams that want plain-English querying, dashboards, and embedded analytics on a flat price — without per-seat pricing or AI add-ons. [Draxlr](https://www.draxlr.com/) is built around a simple idea: a person on your team should be able to ask a question in English and get a chart back in under a minute, without bothering an engineer. You connect a SQL database, type a question, and Draxlr's [AI SQL engine](/blogs/ai-sql-tool/) reads your schema, generates the query, runs it, and renders the result as a chart you can pin to a dashboard. What sets Draxlr apart is that the AI is one of three ways to query your data — alongside a visual query builder and a full SQL editor — so technical and non-technical teammates work in the same tool. The generated SQL is always visible and editable, so analysts can verify and tweak queries before saving them. You can also teach Draxlr custom context (business definitions, column meanings, deprecated tables) so its queries reflect how your business actually works. For teams building customer-facing analytics, Draxlr offers white-label [embedded dashboards](/blogs/best-embedded-analytics-tools) with React and Vue SDKs, secure embed tokens, and row-level filtering for multi-tenant apps. ![Draxlr AI Chat generating SQL and a chart from a plain-English question](/images/blogs/best-ai-sql-tools/draxlr-ai-chat.png) In the example above, the prompt _"List of orders grouped by employee's name"_ produces a working `SELECT … CONCAT … COUNT … LEFT JOIN` query, runs it, renders a bar chart of orders per employee, and adds an AI Summary highlighting that Margaret Peacock leads with 156 orders while Steven Buchanan has the fewest at 42. From question to insight: about 5 seconds. Connect your Database ### Key strengths - AI Chat that turns plain-English questions into runnable SQL and ready-made charts - Schema-aware: reads table and column names directly from your database — no ETL or sync - Always-visible, editable SQL — no black-box queries - Custom app-wide and table-level context so the AI understands your business logic - Connects to [PostgreSQL](/databases/postgres), [MySQL](/databases/mysql), [MariaDB](/databases/mariadb), MS SQL, [BigQuery](/databases/bigquery), [Snowflake](/databases/snowflake), [Redshift](/databases/redshift), [Supabase](/databases/supabase), [ClickHouse](/databases/clickhouse), [CockroachDB](/databases/cockroachdb), [YugabyteDB](/databases/yugabytedb), [PlanetScale](/databases/planetscale), [Databricks](/databases/databricks), and [Neon](/databases/neon) - Dashboards, scheduled email/Slack reports, threshold-based alerts, and CSV/Excel exports - Embedded analytics with white-labeling for SaaS products - Self-hosted deployment available for regulated industries - Flat team pricing — no per-seat surprises and AI is included, not an add-on ### Limitations - Optimized for SQL databases (NoSQL is not the primary focus) ### Pricing Starts at **$25/month** (Starter), $75/month (Growth), $125/month (Scale). All plans include AI SQL generation, dashboards, alerts, and embeds — no feature gating or per-seat charges. ### What users say about Draxlr > Draxlr offers a clean, modern interface and makes it incredibly easy to build dashboards — even for non-technical users. I especially like the visual quality and flexibility of the available graph types. It's intuitive, fast to set up, and has excellent usability from the start. The ability to securely share dashboards via link is a game-changer for our client reporting. — [Review from a SaaS Founder on G2](https://www.g2.com/products/draxlr/reviews/draxlr-review-11185807) ⭐ 4.9/5 on G2 --- ## 2. AI2sql **Best AI SQL tool for:** Individual analysts and developers who want a fast, accurate query generator without setting up a full BI tool. [AI2sql](https://ai2sql.io/) is a focused web-based AI SQL generator. You connect a database (or paste a schema), type a question in plain English, and get back a SQL query in seconds. It also includes a SQL toolkit — formatter, validator, optimizer, and error fixer — that's useful even when you're writing SQL yourself. It supports prompts in nine languages and claims around 90% accuracy on common queries. There's no dashboarding layer, but for solo developers, freelance analysts, and quick ad-hoc work, it's one of the cheapest tools in this list. ![AI2sql web interface generating SQL from a natural-language prompt](/images/blogs/best-ai-sql-tools/ai2sql-screenshot.png) ### Key strengths - Direct schema detection from connected databases - Multi-language prompts (9 languages) - Built-in SQL toolkit: format, validate, optimize, fix - Broad SQL dialect coverage - Very low entry price ### Limitations - No dashboarding or persistent reports - Less suited for team collaboration ### Pricing Freemium. Pro plans start at **$9/month**. --- ## 3. Chat2DB **Best AI SQL tool for:** Developers and DBAs who want a Datagrip-style database client with built-in AI for query generation and error fixing. [Chat2DB](https://chat2db.ai/) is an AI-driven database GUI that combines a desktop SQL client with a paid AI layer. It feels like a traditional database client — table browser, query editor, schema explorer — except every panel has AI hooks. Type a prompt and it generates SQL. Hit a syntax error and it offers a one-click fix. Run a query and it can auto-build a chart from the result. It supports more than ten LLMs (GPT-4o, Claude 3.5/3.7, DeepSeek, Qwen) and works with SQL and NoSQL databases alike, including MongoDB and Redis. ![Chat2DB AI-enhanced database client with text-to-SQL editor](/images/blogs/best-ai-sql-tools/chat2db-screenshot.png) ### Key strengths - Open-source community edition with a large GitHub following - Wide database support including NoSQL (MongoDB, Redis) and analytical engines (ClickHouse, Presto, Hive) - Multi-LLM support — bring your own model - One-click SQL error fixing and query optimization ### Limitations - AI features and dashboards locked behind paid Pro tier - UI density can feel heavy for non-technical business users ### Pricing Free Community edition. Pro starts at **$15/user/month** with a 30-day trial. --- ## 4. SQLAI.ai **Best AI SQL tool for:** Analysts who need broad database coverage including NoSQL and want a free-forever tier. [SQLAI.ai](https://www.sqlai.ai/) is an AI SQL assistant that connects to 30+ database types — including NoSQL stores like MongoDB and DynamoDB — and generates queries trained on your specific schema. It also handles query optimization, syntax fixing, test data generation, and lightweight dashboarding. Pricing starts at the lowest end of any tool in this list, making it a strong choice for individuals and very small teams. ![SQLAI.ai assistant generating a schema-aware SQL query](/images/blogs/best-ai-sql-tools/sqlai-screenshot.png) ### Key strengths - Broad coverage: 30+ databases including NoSQL - Free-forever tier - Schema-aware query generation - Built-in optimizer and test data generator ### Limitations - Less collaborative than full BI suites - Lighter visualization layer than dedicated dashboard tools ### Pricing Free plan available. Paid plans start around **$6/month**. --- ## 5. Vanna AI **Best AI SQL tool for:** Developers who want to embed text-to-SQL directly into their own products or run a fully self-hosted setup with their own LLM. [Vanna AI](https://vanna.ai/) is an open-source, RAG-powered text-to-SQL framework. Instead of being a finished product, it's a Python library and agent platform you wire into your own application. You train it on your schema, sample queries, and documentation; it then generates SQL grounded in that context. Vanna 2.0 (released late 2025) added an agent layer, streaming charts, and a pre-built `` UI component. It's model-agnostic — you can plug in OpenAI, Anthropic, Gemini, Bedrock, or local models via Ollama — which makes it a strong choice for teams with strict data residency requirements who don't want their schema sent to a third-party API. The trade-off is that Vanna is a building block, not a turnkey product: you assemble the dashboard, sharing, and alerting layers yourself. ![Vanna AI homepage — open-source SQL agent with multi-database, multi-turn, access-controlled querying](/images/blogs/best-ai-sql-tools/vanna-screenshot.png) ### Key strengths - MIT-licensed open source with a large developer community - Model-agnostic — works with OpenAI, Anthropic, Gemini, Bedrock, or local LLMs - RAG-based architecture grounds queries in your real schema and historical queries - Pre-built UI components for embedding chat into your app - Cloud, VPC, and fully self-hosted deployment options ### Limitations - Requires engineering effort to set up and maintain - Less polished out-of-the-box than full BI tools — you build the dashboard layer yourself - Visualization layer is lighter than dedicated BI products ### Pricing Free open-source. Vanna Cloud Pro starts around **$50/user/month**; Enterprise is custom. --- ## 6. Text2SQL.AI **Best AI SQL tool for:** Developers who need a lightweight NL-to-SQL generator with public API access. [Text2SQL.AI](https://www.text2sql.ai/) is a focused tool that converts natural language to SQL, Excel formulas, or regex. You can paste a schema or import it directly, and it returns generated SQL with an optional explainer. The standout feature is its public API, which gives developers 100 free monthly requests for integrating text-to-SQL into their own products. Its privacy posture is strong — it stores schema only, never your row data — making it suitable for teams with data sensitivity concerns. ### Key strengths - Schema-only architecture — no row data stored - Public API with a generous free tier - Multi-format output: SQL, Excel formulas, regex - Multi-dialect SQL support ### Limitations - Not a full dashboard or BI platform - More of a developer utility than a team tool ### Pricing Free tier available. Paid plans typically start around **$5–$10/month**. --- ## 7. AskYourDatabase **Best AI SQL tool for:** Non-technical business users who want to chat with large production databases and get auto-generated dashboards. [AskYourDatabase](https://www.askyourdatabase.com/) is built for the use case of "I have a giant production database and a non-technical team that needs answers from it." It's a chat-style interface that handles schemas with hundreds of tables, generates queries, builds dashboards automatically, and self-corrects when queries fail. It supports both SQL and NoSQL databases and offers a desktop app alongside a web chatbot for embedding in internal tools. ### Key strengths - Handles very large schemas (hundreds of tables) - Automatic dashboard generation from chat - Self-correcting queries on failure - Desktop app and embeddable chatbot - Works with SQL and NoSQL ### Limitations - Pricing has trended upward - Visualization layer less mature than dedicated BI tools ### Pricing Professional **$39/month**, Ultimate **$69/month** (with Claude-4 / GPT-4.1 access). Enterprise and chatbot pricing are custom. --- ## 8. Defog AI (SQLCoder) **Best AI SQL tool for:** Enterprises with strict data residency or compliance requirements that need fine-tuned, self-hosted text-to-SQL. [Defog AI](https://defog.ai/) is an enterprise text-to-SQL platform powered by SQLCoder, an open-source LLM family fine-tuned specifically for SQL generation. The SQLCoder 8B, 14B, and 32B models are available on Hugging Face and have outperformed GPT-4 on Defog's internal SQL evaluation benchmarks. For teams that can't send schema or data to third-party APIs — hospitals, banks, government agencies — Defog's on-prem and fine-tuned options are some of the only viable text-to-SQL solutions. ### Key strengths - SQLCoder open-source models (free, on Hugging Face) - On-prem deployment for regulated industries - Fine-tuning on your enterprise data improves accuracy significantly - Strong benchmark performance against general-purpose LLMs ### Limitations - Geared at engineering-heavy teams — non-trivial setup - Cloud tier is enterprise-priced ### Pricing SQLCoder model weights are free. Cloud and self-hosted product tiers are typically enterprise-priced (around $5,000/month for unlimited cloud usage). --- ## 9. Hex (Magic AI) **Best AI SQL tool for:** Data teams that work in a notebook-style collaborative environment with SQL, Python, and visualization side by side. [Hex](https://hex.tech/) is a collaborative data workspace — think Jupyter meets Notion — with strong AI features baked in. Hex Magic generates SQL and Python from natural language, suggests next-step analyses, and automatically corrects errors. Magic understands your semantic model, so suggestions are grounded in real metrics, not guesses. It's a stronger fit for data analysts and analytics engineers than for non-technical business users, but for teams that want a polished notebook with serious AI assistance, Hex is one of the best. ### Key strengths - Magic AI for SQL and Python generation - Notebook Agent for multi-step analysis - Semantic-model-aware suggestions - Strong collaboration: comments, sharing, version history - Deep integrations with Snowflake, BigQuery, Databricks, Redshift ### Limitations - Built for data teams, not business users - Per-seat pricing scales quickly with team size ### Pricing Free tier available. Paid plans start at **$28/user/month**; Enterprise is custom. --- ## 10. Metabase (Metabot AI) **Best AI SQL tool for:** Teams already running Metabase who want to add AI question-answering on top of their existing setup. [Metabase](https://www.metabase.com/) is one of the most widely used open-source BI tools, and its **Metabot AI** add-on layers natural-language SQL generation, chart creation, and query debugging onto its existing dashboard interface. You ask a question in plain English, Metabot generates SQL grounded in your data model, runs it, and returns a chart with a narrative summary. Metabot only works on paid Metabase Cloud plans and requires a separate AI add-on, which makes the total cost roughly $200/month for small teams. The free open-source edition does not include Metabot. ### Key strengths - Mature, well-known BI platform with a large community - Metabot generates SQL, builds charts, and debugs queries - Embeddable AI chat SDK for adding the assistant to your app - Works with semantic models for governed answers ### Limitations - Metabot requires paid Cloud plan plus separate AI add-on - Open-source self-hosted edition does not include Metabot AI - Less polished AI experience than purpose-built standalone tools ### Pricing Open Source is free. Cloud Starter from **$85/month**. Metabot AI add-on starts at **$100/month** for 500 requests. --- ## 11. DataChat **Best AI SQL tool for:** Niche enterprises evaluating no-code, chat-style analytics — though most teams will be better served by the tools above. [DataChat](https://datachat.ai/) is a no-code natural-language analytics platform built around its proprietary "GUDU" interaction language (Get, Understand, Display, Use). The pitch is that non-technical users can analyze data through guided chat without ever seeing SQL. In practice, it's one of the older entries in this category, and it shows. The product is real and active, but it sits well behind the leaders on most dimensions that matter in 2026. Pricing is sales-led with no public tiers, public reviews and community traction are thin compared to Hex or Metabase, the visualization layer is less polished than purpose-built dashboard tools, and database coverage is narrower than tools like SQLAI.ai or Chat2DB. The AI experience also leans on DataChat's own abstraction language rather than the schema-grounded LLM approach that the rest of this list has standardized around. If your team already runs DataChat or is evaluating it as part of a broader enterprise procurement, it can do the job. For most teams starting fresh in 2026, almost any other option in this guide is a faster, cheaper, more flexible starting point. ### Key strengths - No-code, chat-style interface aimed at non-technical users - Guided GUDU workflow for step-by-step analysis - Long-running product with enterprise references ### Limitations - Sales-led pricing with no public transparency makes evaluation slow - Limited public reviews and community traction compared to leaders - Narrower database coverage than tools like SQLAI.ai, Chat2DB, or Draxlr - Visualization and dashboarding are less polished than purpose-built BI tools - Custom abstraction language adds a learning curve where modern tools just use plain English ### Pricing Sales-led. No public pricing — contact sales for a quote. --- ## Honorable mentions: enterprise BI tools with AI SQL features Several large BI platforms now include AI SQL features as part of broader analytics suites. They aren't pure AI SQL tools, but they're worth knowing about if your organization is already on one of these. We covered these in more depth in our [best AI-powered BI tools guide](/blogs/ai-powered-bi-tools). - **[Looker (with Gemini)](https://cloud.google.com/looker)** — Google Cloud's BI platform now includes Gemini-powered Conversational Analytics and a LookML Assistant. Strong if you're deep in BigQuery and have a mature LookML model. Custom pricing, typically $5,000+/month. - **[ThoughtSpot Spotter](https://www.thoughtspot.com/product/agents/spotter)** — Enterprise agentic analytics with a governed semantic layer. Search-style queries (rather than raw text-to-SQL) and proactive anomaly alerts. Enterprise-priced. - **[Tableau (Pulse + Tableau Agent)](https://www.tableau.com/products/tableau-pulse)** — Salesforce-owned BI suite where Tableau Pulse delivers AI-detected insights and Tableau Agent enables NL exploration. AI add-on around $15/user/month on top of Tableau seats. These tools shine in large organizations with existing BI infrastructure. For smaller teams or teams that want AI SQL as the primary feature, the standalone tools above are usually faster, cheaper, and easier to adopt. --- ## How to choose the right AI SQL tool The best AI SQL tool depends less on benchmark scores and more on three practical questions about your team. Work through them in order. ### 1. Who is going to use it? If your primary users are non-technical — founders, marketers, product managers, support — pick a tool that puts the chat interface front and center and hides SQL by default. Draxlr and AskYourDatabase are the strongest fits here. If your users are developers and analysts who already know SQL but want to move faster, AI-enhanced clients like Chat2DB, AI2sql, or Hex are a better fit. ### 2. Where does your data live? If you're on a SQL database (PostgreSQL, MySQL, BigQuery, Snowflake, etc.) and want AI plus dashboards in one tool, Draxlr is purpose-built for this. If you also need NoSQL coverage, look at SQLAI.ai or Chat2DB. If you have strict data residency requirements (healthcare, finance, government), Vanna AI's self-hosted setup, Defog AI's SQLCoder, or Draxlr's self-host option are some of the only viable choices. ### 3. Do you need dashboards and sharing, or just SQL generation? If you just want to generate queries and copy them into your own tools, lightweight options like AI2sql, Text2SQL.AI, or SQLAI.ai are cheapest. If you need to save charts, schedule reports, send Slack alerts, or [embed dashboards](/blogs/best-embedded-analytics-tools) in your own product, you need a full platform — Draxlr, Metabase + Metabot, or Hex. ### 4. What's your budget? For teams under 50 people, standalone AI SQL tools usually beat enterprise BI suites on price by a wide margin. Draxlr's flat $25/month start is around 1/40th the cost of ThoughtSpot's enterprise floor and a fraction of Looker's typical contract. The main reason to pay enterprise prices is governance, compliance, or integration with an existing BI investment. --- ## Conclusion The **best AI SQL tools** in 2026 share three things: schema-aware query generation, transparent and editable SQL, and a fast path from question to chart. What separates them is who they're built for. If your team needs a fast, affordable, all-in-one tool for AI-powered SQL and dashboards, **[Draxlr](https://www.draxlr.com/)** is the strongest pick — flat pricing, no AI add-on tax, and a query-to-dashboard flow that takes minutes. Developers building text-to-SQL into their own products should look at **Vanna AI** or **Defog AI**. Non-technical teams chatting with very large databases should try **AskYourDatabase**. Teams already committed to Metabase or a major BI suite should evaluate the AI add-on rather than switch tools. Whatever you pick, the fundamentals are the same: a good AI SQL tool removes the queue between a business question and a business answer. That's the win. ### Try Draxlr today Connect a SQL database or use our demo data — no credit card required. Generate your first AI-powered query in under 5 minutes, and pin it to a dashboard in one click. Flat $25/month when you're ready to upgrade. Connect your Database --- ## FAQs ### 1. What is the best AI SQL tool in 2026? The best AI SQL tool depends on your team and use case. For most small to mid-size teams that want AI-generated SQL plus dashboards on a flat price, Draxlr is the strongest pick. For developers embedding text-to-SQL into their own products, Vanna AI is the leading open-source option. Enterprises with strict data residency needs typically pick Defog AI's self-hosted SQLCoder. ### 2. Can AI really write accurate SQL queries? Yes — when grounded in your actual database schema. Modern AI SQL tools index your tables, columns, and relationships before generating queries, which dramatically reduces hallucinations. Tools that pass your question to a generic LLM with no schema context are far less reliable. For complex multi-step analysis, you can usually view and edit the generated SQL before saving it. ### 3. Are AI SQL tools safe to use with production databases? They can be, if you take basic precautions. Use a read-only database user, connect through a read replica when possible, and pick a tool that encrypts credentials at rest and never copies row data outside your warehouse. Tools like Draxlr support read-only connections and only read your schema, not your row data. ### 4. Is there a free AI SQL tool? Yes. Vanna AI is free and open-source under the MIT license. SQLAI.ai, Text2SQL.AI, AI2sql, Chat2DB Community Edition, and Hex all offer free tiers. Metabase's open-source edition is free, though Metabot AI requires a paid Cloud plan. Defog AI also publishes its SQLCoder model weights for free on Hugging Face for teams that want to self-host. ### 5. What's the difference between an AI SQL tool and an AI BI tool? AI SQL tools focus specifically on generating, editing, and running SQL queries from natural language. AI BI tools (like ThoughtSpot, Looker, or Power BI) include text-to-SQL as one feature among many, alongside semantic modeling, governance, and enterprise reporting. AI SQL tools are usually faster to set up and cheaper for small teams. AI BI tools are stronger for large organizations with existing BI infrastructure. ### 6. Which databases do AI SQL tools support? The leading AI SQL tools support most popular SQL databases: PostgreSQL, MySQL, MariaDB, MS SQL Server, BigQuery, Snowflake, Redshift, Supabase, ClickHouse, CockroachDB, YugabyteDB, PlanetScale, Databricks, and more. Tools like SQLAI.ai and Chat2DB also support NoSQL databases like MongoDB and Redis. ### 7. Do I need to know SQL to use an AI SQL tool? No. The whole point of an AI SQL tool is to remove that requirement. You ask questions in plain English and get back charts or queries. That said, if you do know SQL, you can inspect and edit the generated queries — which is useful for verifying complex analyses. ### 8. How is an AI SQL tool different from ChatGPT? ChatGPT can write SQL if you paste in your schema, but it has three practical limits. First, it has no live connection to your database — you have to copy queries out, run them elsewhere, and copy results back. Second, it doesn't produce dashboards, scheduled reports, or shareable links. Third, pasting schema into a general-purpose chatbot raises governance questions that purpose-built AI SQL tools handle with read-only connections and schema-only access. ### 9. Are AI SQL tools accurate enough for production use? For most read-only analytics use cases, yes. Modern schema-grounded AI SQL tools generate working queries on the first try at high rates — often 85–95% — for standard joins, filters, and aggregations. Accuracy drops on ambiguous questions ("top customers" by what?) and on highly complex multi-step analyses (cohort retention, attribution models). The right safety net is to keep the generated SQL visible and editable, use a read-only database connection, and review queries before pinning them to a production dashboard. Tools like Draxlr, Vanna AI, and Defog AI are built around this assumption — the AI gets you 90% of the way, and a human verifies the last 10%. ### 10. Can AI SQL tools replace data analysts? No, and the goal isn't to. AI SQL tools take over repetitive ad-hoc query work — the "can you pull this number for me?" requests that fill an analyst's inbox. That frees analysts to focus on harder problems: cohort analysis, attribution modeling, experiment design, and data quality work that AI can't reliably do. In practice, teams that adopt an AI SQL tool see analysts spend less time on one-off SQL and more time on strategic work, while non-technical teammates self-serve on the routine questions.

Best Embedded Analytics Tools for SaaS Teams in 2026

hello@draxlr.com (Draxlr) — Thu, 30 Apr 2026 00:00:00 GMT

Looking for a single tool recommendation? See our full [embedded analytics tool](/embedded-analytics-tool/) page. Embedded analytics is no longer a feature teams build from scratch. The embedded analytics market has matured to the point where SaaS teams can choose from a range of purpose-built tools, each with different pricing models, embed methods, and implementation timelines. The challenge is that most [embedded analytics tools](/embedded-analytics-tool/) were designed for enterprise data teams, not the product teams who need to ship customer-facing dashboards quickly. Pricing is often opaque, implementation is complex, and the cost of serving dashboards to external customers scales in ways that catch teams off guard. This guide covers the best embedded analytics tools in 2026 with verified pricing, honest trade-offs, and a clear picture of which tool works best for which kind of team. We cover Draxlr, Luzmo, Metabase, Power BI Embedded, Looker, Sisense, Qlik, and ThoughtSpot. --- ## What Is Embedded Analytics? Embedded analytics refers to integrating data dashboards, charts, and reporting directly inside a software product rather than directing users to a separate [BI platform](/self-hosted-bi-tool/). Instead of exporting data to spreadsheets or logging into a standalone tool, customers see live, interactive dashboards built into the product they already use. For SaaS teams, embedded analytics serves two purposes. Internally, it gives teams visibility into product usage, revenue, and operations. Externally, it gives customers access to their own data inside the product, which drives retention, reduces churn, and increases the perceived value of a platform. The most common embedded analytics use cases include customer-facing usage dashboards, revenue and performance reports embedded inside client portals, operational analytics embedded in logistics or supply chain products, and multi-tenant SaaS products where each customer sees only their own data. --- ## What to Look for in an Embedded Analytics Tool Before evaluating specific embedded analytics tools, it helps to understand what factors actually determine whether a tool works in a production SaaS environment. **White labeling.** Some tools offer [white labeling](/white-label-bi-solution/) only on enterprise tiers. Others include it from the first paid plan. Verify exactly what is included, covering custom domain, logo removal, custom fonts, color palettes, and suppression of all vendor branding, as these are all separate considerations. **Multi-tenant support.** In a SaaS product, each customer must only ever see their own data. Some tools handle tenant isolation natively with built-in workspace or row-level security. Others require manual implementation using filtered embed tokens. The former is significantly easier to maintain at scale. **Pricing model at scale.** Per-user pricing compounds quickly for customer-facing deployments. A product with 500 customers and 5 users each can turn a manageable monthly fee into a very large bill. Capacity-based, MAU-based, and flat pricing models are generally more predictable for SaaS deployments than per-seat models. **Time to first embed.** Some tools require data modeling layers and infrastructure setup before a single dashboard can be shown. Others connect directly to a database and get to an embedded dashboard in days. Prioritize tools with direct database connectivity and pre-built SDKs if speed matters. **Embed method.** The difference between pasting an iframe into an app and using a React or Vue SDK is significant. Iframes are easy to set up but create visual inconsistencies and break on mobile. SDK-based embedding allows dashboards to inherit the app's design system and behave as native features. If customers interact with analytics frequently, SDK embedding is worth the setup time. --- ## Quick Comparison: Best Embedded Analytics Tools in 2026

Tool	Starting Price (Embedding)	Pricing Model	White Labeling	Customer Viewer Fees	Free Trial
Draxlr	$75/month	Flat pricing	Included	None, unlimited	7 days
Luzmo	$495/month billed annually	MAU-based	Premium only	MAU scaling	Yes
Metabase	$575/month (Pro, 10 users included)	Per-seat (interactive embedding)	Pro and above	$12/user/month (interactive only)	14 days
Power BI Embedded	~$735/month (A1 SKU)	Capacity-based	Yes	Yes	Limited
Looker	~$60,000/year	Quote-based	Yes	~$400/user/year	No
Sisense	$399/month	Tier-based	Grow and above ($1,299/mo)	Yes	7 days
Qlik	$300/month	Capacity-based	Yes	Yes	Yes
ThoughtSpot	$50/user/month, min. 25 users ($1,250/month)	Per-user	Enterprise only	Yes	Yes

Competitor pricing based on publicly available information and third-party data. Enterprise quotes may vary. --- ## 1. Draxlr **Best Embedded Analytics Tool for SaaS teams that need fast embedded analytics without enterprise pricing** [Draxlr](/) is an embedded analytics platform built for SaaS teams that need to ship customer-facing dashboards quickly without building a custom BI layer from scratch. It connects directly to SQL databases, removes the need for a data modeling or semantic layer, and supports iframe, ReactJS SDK, VueJS SDK and backend API embedding. Non-technical team members can build and iterate on dashboards using the [AI SQL tool](/free-tools/ai-sql-tool/) to generate queries from plain English or the drag-and-drop builder, while engineers can write raw SQL. No semantic modeling layer required, no sales call to find out the price, and no per-seat fees as your customer base grows. ### Who Should Consider Draxlr Draxlr is the strongest fit for SaaS startups and mid-market teams that need [affordable embedded analytics](/affordable-embedded-analytics-tool/) in weeks rather than months, want full white labeling without it being gated behind an enterprise plan, need multi-tenant data isolation without manual configuration, and cannot justify per-seat viewer fees that scale against them as their customer base grows. ### Embed Options Draxlr supports iframe, ReactJS SDK, and VueJS SDK, and backend API embedding. Ready-to-use code snippets are available directly from the dashboard. White labeling, including full removal of Draxlr branding, custom colors, and custom themes, is included in the Premium plan at $75/month. ### Key Features Draxlr includes multi-tenant filtering with predefined values per customer, interactive visualizations with [drill-down](/features/drill-down/), filters, tooltips, and zoom, real-time data with configurable refresh intervals, responsive design for desktop and mobile, export controls per embed, and AI-powered text-to-SQL for building queries without writing code. Supported databases include PostgreSQL, MySQL, MariaDB, Microsoft SQL Server, Supabase, PlanetScale, CockroachDB, YugabyteDB, Amazon Redshift, Google BigQuery, Snowflake, ClickHouse, Databricks, Neon, Airtable, and Google Sheets. ### Pricing Draxlr uses flat-rate pricing with no per-viewer or per-embed fees. The Lite plan starts at $25/month with no embedding. **The Premium plan at $75/month includes all embed options, white labeling, 2 databases, 10 internal users, and unlimited customer viewers**. The Power plan at $125/month adds 5 databases and 30 internal users. Customer viewers, meaning the customers who view embedded dashboards inside a product, are unlimited on every plan at no additional cost. All plans include a 7-day free trial with no credit card required. ### Trade-offs Draxlr works directly with SQL databases and does not include a semantic modeling or LookML-style layer. --- ## 2. Luzmo **Best Embedded Analytics Tool for SaaS teams building complex embedded dashboards with a design-forward approach** Luzmo is a purpose-built embedded analytics platform with a strong developer SDK and a drag-and-drop dashboard builder that allows non-technical team members to build and iterate on dashboards independently. It is a strong choice for SaaS teams where analytics is a core product differentiator and the visual quality of embedded dashboards matters significantly. ### Who Should Consider Luzmo Luzmo suits growth-stage SaaS teams where product managers or designers need to build dashboards without engineering support, teams that need advanced charting options, and organizations comfortable with MAU-based pricing that can model their costs based on projected active user counts. ### Embed Options Luzmo provides a JavaScript SDK and web components for native embedding with no iframes required. White labeling is not included on the Starter plan and requires the Premium plan at $1,995/month. ### Key Features Luzmo includes a drag-and-drop dashboard builder for non-technical users, live and cached data modes, multi-tenant support with row-level security via JWT and user attributes, themed components that inherit the application's design system, and MAU-based pricing that does not charge per seat. ### Pricing Luzmo's Starter plan begins at $495/month billed annually. This tier does not include white labeling. The Premium plan at $1,995/month billed annually adds full white labeling, advanced analytics, and proactive AI insights. Pricing scales with Monthly Active Users across all plans, so as more customers actively use embedded dashboards, the bill increases. ### Trade-offs Luzmo's MAU-based pricing model introduces cost unpredictability during growth phases. A product that sees a spike in customer engagement will see a corresponding increase in analytics costs. The requirement for the $1,995/month Premium plan to access white labeling makes it significantly more expensive than its Starter entry price suggests for teams that need a fully branded embedded experience. --- ## 3. Metabase **Best Embedded Analytics Tool for internal BI teams that also need embedded dashboards** Metabase is one of the most widely used BI tools in the world, known for its clean interface and accessible query builder. Its open-source tier is free to self-host, making it popular for internal reporting. For embedded analytics, however, Metabase's per-seat pricing model and feature gating create challenges for SaaS teams with large customer bases. ### Who Should Consider Metabase Metabase suits teams that are already using it for internal reporting and want to extend some of that capability to customers without switching platforms, small-scale embedding use cases where the customer viewer count is low and predictable, and organizations that are comfortable with self-hosting and have DevOps capacity to maintain the open-source version. ### Embed Options Metabase's open-source tier supports guest embedding but requires leaving a "Powered by Metabase" badge on all embedded dashboards. White labeling and interactive embedding require the Pro plan. The React SDK for native component-level embedding is available on Pro and above. ### Key Features Metabase includes a visual query builder, SQL editor, an extensive chart library, scheduled reports, alerts, and a well-documented API. The Pro plan adds SSO, white labeling, interactive embedding, and multi-tenant support. ### Pricing Metabase's Starter plan begins at $100/month plus $6/month per user (first 5 users included), cloud hosted only. The Pro plan starts at $575/month with the first 10 users included and additional users at $12/month each. For embedded analytics specifically, Metabase has two modes. Static embedding lets customers view pre-configured dashboards with no Metabase login required and no per-viewer cost. The limitation is that static embeds are read-only — customers cannot apply filters, drill down, or interact with the data beyond what was pre-set. Interactive embedding unlocks full interactivity including filters, drill-through, and customer-controlled exploration, but requires customers to sign in to Metabase. Every signed-in customer counts as a billable user at $12/month. For a SaaS product with 500 customers using interactive embedded dashboards, that structure becomes $6,000/month in viewer fees before infrastructure costs. Draxlr embedded dashboards are fully interactive with filters, drill-down, and tooltips, with no customer login required and no per-viewer fee at any scale. ### Trade-offs The core challenge for customer-facing use cases is the interactive embedding model. Static embeds require no login and no per-viewer cost, but customers cannot interact with the data. Interactive embedding gives customers full control but requires a Metabase login for every viewer, with each viewer billed at $12/month. Teams that need fully interactive embedded dashboards at scale face costs that compound quickly as the customer base grows. Draxlr embedded dashboards deliver full interactivity with no customer login and no per-viewer fee. --- ## 4. Power BI Embedded **Best Embedded Analytics Tool for teams already invested in the Microsoft Azure ecosystem** Power BI Embedded is Microsoft's solution for embedding Power BI reports and dashboards inside external applications. It is capacity-based rather than per-user, which makes it more predictable for customer-facing deployments than tools like Metabase or Looker. It is the natural choice for teams already building on Azure or using Power BI internally for reporting. ### Who Should Consider Power BI Embedded Power BI Embedded suits organizations already using Power BI internally who want to surface existing dashboards to customers, teams building on Microsoft Azure where native integration reduces infrastructure overhead, and enterprises in industries where Microsoft tooling is a standard requirement. ### Embed Options Power BI Embedded provides a JavaScript API for native embedding of reports, dashboards, tiles, and Q&A experiences. Row-level security supports tenant-based data isolation. Both app-owns-data and user-owns-data embedding scenarios are supported. ### Key Features Power BI Embedded includes token-based authentication, capacity-based A-SKU pricing, an extensive custom visuals marketplace, integration with Azure Active Directory and Microsoft Fabric, and support for the full Power BI feature set in embedded contexts. ### Pricing Power BI Embedded uses capacity-based A-SKU pricing. The A1 SKU starts at approximately $735/month. Advanced embedding features require Premium Per Capacity or Premium Per User plans, which are significantly more expensive. The capacity model means costs are more predictable than per-seat models, but scaling capacity to handle higher concurrency increases costs. ### Trade-offs Power BI Embedded is tightly coupled to the Microsoft ecosystem. Teams not already using Azure will face additional infrastructure overhead. The UI flexibility for white-labeled products is more limited than dedicated embedded analytics platforms, and the implementation complexity is higher than lighter-weight alternatives. --- ## 5. Looker **Best Embedded Analytics Tool for enterprises with complex data models and existing Google Cloud investment** Looker is Google Cloud's enterprise BI platform built around LookML, a proprietary modeling language that defines metrics, dimensions, and relationships in a reusable and version-controlled way. It is the most sophisticated tool on this list in terms of data governance and semantic modeling, and also the most expensive for embedded use cases. ### Who Should Consider Looker Looker suits enterprises with large data warehouses and dedicated data engineering teams who can invest in learning and maintaining LookML models, organizations already deeply embedded in Google Cloud or BigQuery, and teams that need a single consistent metric definition shared across internal and external analytics. ### Embed Options Looker provides an Embed SDK for embedding individual charts, full dashboards, or the full Looker explore interface. LookML handles reusable metric definitions. SSO integration and user attribute-based row-level security support multi-tenant deployments. ### Key Features Looker includes LookML semantic layer, Gemini AI integration for natural language querying, full white labeling with custom theming, REST API for programmatic access to all Looker resources, Git integration for version control of data model changes, and deep integration with BigQuery and Google Cloud. ### Pricing Looker does not publish pricing publicly. The base platform starts at approximately $60,000 per year before viewer or creator seat costs. Viewer seats add $400 per user per year. For a SaaS product with 500 customers viewing embedded dashboards, the viewer fee alone adds $200,000 per year on top of the platform license. Embedded use cases that require the Embed Edition are priced higher still, with total annual costs commonly ranging from $100,000 to $500,000 depending on usage and scale. ### Trade-offs Looker's per-viewer pricing model makes it economically unworkable for most SaaS teams serving large customer bases. The LookML learning curve requires dedicated data engineering investment. Google's acquisition of Looker has introduced slower development cycles that have been noted in user reviews over the past two years. --- ## 6. Sisense **Best Embedded Analytics Tool for teams that need full embedded BI with public, transparent pricing** Sisense is an analytics platform built for embedded analytics use cases. Unlike most enterprise BI tools in this category, Sisense now offers public pricing with a 7-day free trial and no sales call required. Its Compose SDK enables fully customized embedded experiences using React components, making it a strong option for product teams that want flexibility in how analytics are surfaced inside their application. ### Who Should Consider Sisense Sisense suits SaaS teams that want a fully customized embedded analytics experience built with React components, organizations that need a transparent pricing model they can evaluate without a sales call, and teams that require white labeling and SSO as part of a mid-tier plan rather than a fully custom enterprise contract. ### Embed Options Sisense provides iframe, Compose SDK, and Embed SDK embedding options across all plans. The Compose SDK enables component-level React embedding with full UI customization. White labeling is available from the Grow plan at $1,299/month. SSO for user login and viewer persistency is also included from the Grow plan. ### Key Features Sisense includes row-level security on all plans, Sisense Intelligence for AI-powered insights and natural language querying, iframe and SDK-based embedding, scheduled and automated reports, and connectivity to all major data sources. The Grow plan adds white labeling, SSO, advanced reports, and multi-environment support. Multi-tenant support is available on the custom Scale plan. ### Pricing Sisense now offers public pricing with three tiers. The Launch plan starts at $399/month and includes basic embedding, row-level security, 20GB of storage, 2 designer seats, and 50 viewer seats, but does not include white labeling. The Grow plan at $1,299/month adds white labeling, SSO, full BI embedding with viewer persistency, and multi-environment support. The Scale plan has custom pricing and adds multi-tenant support, advanced security, auto scaling, and a 99.99% SLA. All plans include a 7-day free trial with no sales call required. ### Trade-offs White labeling is gated behind the $1,299/month Grow plan, which is a significant step up from the $399/month Launch tier. Multi-tenant support is only available on the custom Scale plan, which requires contacting sales. For SaaS teams that need multi-tenant data isolation as a core requirement from the start, Draxlr includes it from $75/month without requiring a custom plan. --- ## 7. Qlik **Best Embedded Analytics Tool for enterprises with complex, multi-dimensional data that need associative exploration** Qlik is one of the longest-standing analytics platforms, known for its associative engine that holds all data relationships in memory and allows users to explore them without predefined query paths. Since March 2025, Qlik has moved to a capacity-based pricing model for all new cloud subscriptions, removing per-user fees in favor of data volume-based pricing. ### Who Should Consider Qlik Qlik suits enterprises with complex, multi-dimensional datasets where users need to freely explore data relationships rather than consume fixed dashboards, teams already in the Qlik ecosystem looking to extend analytics to customer-facing products, and organizations where exploratory analysis rather than standard dashboard consumption is the core use case. ### Embed Options Qlik provides JavaScript APIs, the newer qlik-embed web components, and REST APIs for programmatic embedding. Nebula.js allows building fully custom visualization components on top of Qlik's associative engine. White labeling and multi-tenant deployment are supported. ### Key Features Qlik includes its associative in-memory engine for instant cross-filter interactions, Qlik Staige for AI-powered insights and natural language querying, row-level security, SSO, real-time alerting, and Active Intelligence for automated action triggers. Since March 2025, new subscriptions use capacity-based pricing. ### Pricing Qlik uses capacity-based pricing tied to data volume rather than user count. The Starter plan begins at $300/month billed annually for 10 users and 10GB of data. The Standard plan starts at $825/month for 25GB of data with unlimited users. The Premium plan at $2,750/month adds predictive AI, anonymous public access, additional GenAI capacity, and SAP connectors. The Enterprise plan is custom priced and starts at 250GB of data. A free trial is available on the Qlik Cloud Analytics tier. ### Trade-offs Qlik's Starter plan at $300/month is accessible but limited to 10 users and 10GB of data with no ability to purchase additional data capacity. Meaningful production embedded analytics use cases typically require the Standard plan at $825/month or above. The associative engine, while powerful for exploration, is not designed for teams that primarily need standard dashboard consumption. Setup requires Qlik expertise that is less common than familiarity with SQL or other BI tools. --- ## 8. ThoughtSpot **Best Embedded Analytics Tool for organizations that need AI-driven, natural language analytics for end users** ThoughtSpot, now positioning itself as an Agentic Analytics Platform, is built around natural language search and AI-driven insights rather than pre-built dashboards. Users ask questions in plain English and ThoughtSpot's AI engine translates them into SQL, selects the most relevant chart type, and returns instant answers. ThoughtSpot now offers public pricing with a free Developer tier for embedded use cases, making it more accessible than it previously was for teams exploring the platform. ### Who Should Consider ThoughtSpot ThoughtSpot suits organizations that want to give non-technical end users the ability to explore data freely without relying on pre-built dashboards, enterprises where self-service analytics is a core product differentiator rather than a supplementary feature, and teams with well-structured data models that can support reliable natural language querying at scale. ### Embed Options ThoughtSpot provides a Visual Embed SDK for native component-level embedding of charts, Liveboards, or the search bar independently. SpotterCode allows AI-assisted embedding code generation. White labeling and theming support are included. SSO and row-level security handle multi-tenant data isolation. ### Key Features ThoughtSpot includes Spotter AI Agent for natural language querying, automated anomaly detection via SpotIQ, embeddable Liveboards that respond to filters in real time, Analyst Studio for deeper modeling with SQL, Python, or R, and connections to major cloud data warehouses including Snowflake, BigQuery, Databricks, and Redshift. ### Pricing ThoughtSpot now publishes pricing for its Embedded product line. The Developer tier is free for up to 10 users and 25M rows, making it a genuine way to evaluate the platform before committing. The Embedded Pro plan starts at $50/user/month billed annually with a minimum of 25 users, putting the minimum monthly spend at $1,250/month. It covers up to 1,000 users and up to 250M rows, and includes AI-infused dashboards and Spotter AI Agent at 25 queries per month per user. The Embedded Enterprise plan has flexible pricing aligned to use case and GTM requirements and requires contacting sales. Multi-tenant support by organization is available on the Embedded Enterprise plan only. ### Trade-offs The Embedded Pro plan requires a minimum of 25 users, which means the minimum monthly commitment is $1,250/month even if a team only needs a handful of embedded users to start. Multi-tenant support by organization is only available on the Embedded Enterprise plan, which requires a custom pricing conversation. The free Developer tier is limited to 10 users and 25M rows, which is suitable for proof-of-concept but not production scale. The implementation complexity remains high, as realizing the AI-driven search experience requires significant data preparation, schema design, and engineering investment upfront. Analyst Studio for SQL, Python, and R analysis is an add-on on all plans. --- ## How to choose the right embedded analytics tool ### 1. Identify your primary audience - Internal team dashboards → Draxlr, Tableau, Power BI - Customer-facing SaaS dashboards → Draxlr, Looker, Sisense, Domo ### 2. Decide how much control you need over UI If you want the dashboard to feel native to your product, choose tools with strong white-label options. ### 3. Consider your data skillset - If you rely on SQL → Draxlr - If you rely on modelling layer → Looker - If you want drag-and-drop → Draxlr, Tableau, Power BI ### 4. Plan for scale early Multi-tenancy, row-level filtering, and secure embed tokens will matter as your customer base grows. ### 5. Evaluate pricing with real usage Most tools charge based on viewers, renders, sessions, or compute. Always request TCO. --- ## Conclusion Embedded analytics is no longer a “nice-to-have” but it’s a core product feature for SaaS teams that want to retain customers, improve decision-making, and deliver value instantly. The best embedded analytics tool depends on where your product is today and where it’s heading. Run a small pilot, embed one dashboard inside your app, and evaluate performance, branding, and user engagement before committing long-term. With the right tool, analytics becomes a natural part of your product not a separate experience your users have to learn. --- ## FAQs ### 1. What are embedded analytics tools? Tools that let you embed dashboards, charts, and reporting directly inside your application using secure components or iframes. ### 2. What is the best embedded analytics tool for SaaS startups? Draxlr is the strongest option for SaaS startups. It offers all embed options, white labeling, multi-tenant filtering, and unlimited customer viewers from $75/month, with no per-seat fees, no sales call required, and a 7-day free trial. Setup time to first embedded dashboard is typically one to two days. ### 3. Which embedded analytics tool has the most affordable pricing? Draxlr starts at $75/month for full embedding including white labeling and unlimited customer viewers. Luzmo starts at $495/month but white labeling requires the $1,995/month Premium plan. Metabase starts at $575/month Pro but adds $12/user/month in viewer fees. Power BI Embedded starts at approximately $735/month. ThoughtSpot has a free Developer tier for embedded up to 10 users and 25M rows, with the Embedded Pro plan at $50/user/month and Enterprise embedded on custom pricing. ### 4. Do embedded analytics tools charge per customer viewer? It depends on the tool. Metabase charges $12/month per user viewing embedded dashboards. Looker charges $400 per viewer per year. ThoughtSpot uses consumption-based billing that scales with query volume and dashboard views. Draxlr offers unlimited customer viewers on every plan at no additional cost. ### 5. How long does it take to embed dashboards using these tools? Draxlr typically gets teams to their first embedded dashboard in one to two days. Luzmo targets under 10 days. Looker, Sisense, and ThoughtSpot typically require significantly longer implementation timelines, often several weeks to months, due to data modeling, infrastructure setup, and engineering investment required. ### 6. Can I white-label embedded analytics dashboards? Yes, most tools support white labeling, but it is gated differently. Draxlr includes white labeling in the $75/month Premium plan. Metabase includes it from the $575/month Pro plan. Luzmo includes it from the $1,995/month Premium plan. Sisense includes it from the $1,299/month Grow plan. Looker, Qlik, and ThoughtSpot include white labeling but require enterprise contracts.

Natural Language to SQL: How It Works Under the Hood

hello@draxlr.com (Draxlr) — Mon, 27 Apr 2026 00:00:00 GMT

Natural language to SQL (NL2SQL) sounds deceptively simple on the surface: a user types a question, SQL comes out. But the gap between a sentence in plain English and a valid, semantically correct SQL query is enormous. Bridging it requires multiple stages of transformation — each with its own failure modes, design tradeoffs, and opportunities for improvement. This article is a thorough technical breakdown of how modern NL2SQL pipelines work, from the moment a user submits a question to the moment a query executes against a real database. --- ## The NL2SQL Pipeline: A Bird's-Eye View Before diving into each component, here is the overall pipeline that a production-grade NL2SQL system runs through: 1. **Input normalization** — clean and tokenize the user's natural language input 2. **Schema retrieval** — fetch the relevant portion of the database schema 3. **Schema linking** — map tokens in the user's question to concrete schema elements 4. **SQL generation** — produce a candidate SQL query using an LLM or a structured decoder 5. **Post-processing and validation** — parse the output, catch syntax errors, and verify executability 6. **Execution** — run the query and return results Each of these stages involves nontrivial decisions. A failure at any point silently propagates: a wrong schema link leads to a hallucinated column name, which produces a syntactically valid but semantically wrong query that returns results with no error — just incorrect data. --- ## Stage 1: Input Normalization Raw user input is messy. Before any semantic understanding can happen, the input needs to be normalized. ### Tokenization The user's question is split into tokens — words, subwords, or characters depending on the tokenization strategy. Modern LLMs (GPT-4, Claude, Llama) use **byte-pair encoding (BPE)** or **SentencePiece**, which handles out-of-vocabulary words by decomposing them into known subword units. For example, `"revenue"` maps to a single token, but `"MRR"` might tokenize as `["M", "RR"]` in a model that has never seen it. This matters because domain-specific terms like database column names (`monthly_recurring_revenue`) may be tokenized unpredictably, affecting attention patterns downstream. ### Casing, punctuation, and spelling Production systems typically apply: - **Lowercasing** for matching against schema identifiers (which are often lowercase) - **Punctuation stripping** for question marks, commas, and sentence-terminal tokens that carry no semantic load for SQL generation - **Spell correction** using a domain-aware dictionary, since typos in entity names (`"revenu"` instead of `"revenue"`) will cause schema linking to fail silently ### Query intent classification Before schema linking, some systems run a lightweight classifier to detect the **query intent**: is this a `SELECT`, an aggregation, a time-series comparison, a filter-heavy lookup, or something the system cannot answer (e.g., a write operation or an ambiguous meta-question)? This gates the rest of the pipeline and avoids wasted LLM calls on unanswerable inputs. --- ## Stage 2: Schema Retrieval A real-world database can have hundreds of tables and thousands of columns. Passing the entire schema into an LLM's context window is: 1. **Expensive** — schema DDL can easily consume 50–150k tokens for large databases 2. **Harmful to accuracy** — LLMs suffer from "lost in the middle" degradation; relevant schema buried deep in a long context gets underweighted during attention The solution is **schema retrieval**: given the user's question, select only the subset of tables and columns that are likely relevant. ### Embedding-based retrieval Table and column names (along with their descriptions and sample values, if available) are embedded into a vector space using a dense retrieval model. The user's question is also embedded, and **cosine similarity** is used to rank schema elements by relevance. The typical retrieval model is a fine-tuned **bi-encoder** (e.g., `text-embedding-3-large`, or a fine-tuned `BERT`-class model) trained on (question, schema element) pairs from datasets like Spider, BIRD, or WikiSQL. Top-K schema elements (commonly K=10–20 tables) are selected and passed to the next stage. ### Hybrid retrieval Pure embedding-based retrieval misses exact-match signals. A column named `user_id` should always be retrieved if the question mentions "user id", regardless of embedding proximity. Production systems layer **BM25 keyword retrieval** on top of embedding similarity, combining scores via reciprocal rank fusion (RRF): ``` final_score = α * embedding_score + β * bm25_score ``` Tuning `α` and `β` is an empirical process that varies by domain and schema vocabulary. ### Schema graph traversal Even after retrieval, join paths between retrieved tables must be resolved. A system that retrieves `orders` and `customers` must also know that these are joined on `orders.customer_id = customers.id`. This join metadata is stored in a **schema graph** (an in-memory directed graph where nodes are tables and edges are foreign key relationships) and is included in the LLM prompt alongside the selected tables. --- ## Stage 3: Schema Linking Schema linking is the process of grounding tokens in the user's question to specific schema elements. It is the single most important stage for output accuracy, and also the most error-prone. ### Types of schema links

Link type	Example	Maps to
Column mention	"revenue"	`orders.total_amount`
Table mention	"customers"	`customers` table
Value mention	"United States"	`users.country = 'US'`
Aggregation mention	"total", "average"	`SUM(...)`, `AVG(...)`
Temporal mention	"last month"	`WHERE created_at >= ...`
Comparison mention	"more than 100"	`> 100`

### Lexical matching The simplest schema linking approach is **string-matching**: compare each token in the user question against the set of all table and column names. Exact matches are high-confidence links; fuzzy matches (Levenshtein distance ≤ 2) are flagged as candidates for LLM resolution. ### Semantic matching Lexical matching fails when the user's vocabulary doesn't align with schema naming. A user asking about `"monthly recurring revenue"` won't match a column named `mrr`. This requires embedding-based semantic matching against: - Column names - Column descriptions (if stored in schema metadata) - Sample values (for value mentions like country names or status enums) ### Value grounding Value mentions are the hardest links to resolve. When a user asks "orders from Germany", `"Germany"` must be linked to the exact value stored in the `country` column — which might be `"DE"`, `"Germany"`, or `"GERMANY"` depending on how the data was loaded. Robust systems index sample values for categorical columns (typically up to 10,000 distinct values per column) and run fuzzy matching at query time to resolve value mentions to their canonical stored form before injecting them into the SQL template. --- ## Stage 4: SQL Generation With the schema context assembled and schema links annotated, the actual query generation begins. There are three main approaches in active use. ### Approach 1: Rule-based / template systems (legacy) Early NL2SQL systems compiled user questions into SQL via hand-authored grammar rules and slot-filling templates. A question matching the pattern `"top N by "` would be mapped to: ```sql SELECT , SUM() AS total FROM GROUP BY ORDER BY total DESC LIMIT N; ``` These systems are fast and fully deterministic, but they break on questions outside the grammar and require significant manual maintenance as the schema evolves. They are mostly obsolete for production use. ### Approach 2: Sequence-to-sequence neural models (fine-tuned) Between 2018 and 2022, the dominant approach was fine-tuned **encoder-decoder Transformers** (T5, BART, CodeT5) trained on NL2SQL datasets. The input sequence is `[question] [SEP] [schema DDL]` and the output sequence is the SQL string. These models achieved strong performance on controlled benchmarks (Spider: ~85% exact match with the best fine-tuned models) but suffered in production because: - They are brittle to schema changes — a renamed column breaks the model - They require expensive fine-tuning when new tables or domains are added - They do not generalize to multi-hop questions with complex subqueries ### Approach 3: LLM-based generation with structured prompting (current state of the art) Modern production NL2SQL systems use large language models (GPT-4, Claude 3.5, Gemini 1.5 Pro, or open-source equivalents like Llama 3 70B fine-tuned on SQL) with a carefully engineered prompt. The LLM is not fine-tuned on domain-specific data; instead, all domain knowledge is injected at inference time through the prompt. A typical prompt structure looks like: ``` You are an expert SQL writer. Given the database schema and a user question, write a syntactically correct SQL query. DATABASE DIALECT: PostgreSQL 15 SCHEMA: Table: orders (id INT PK, customer_id INT FK→customers.id, total_amount DECIMAL, status VARCHAR, created_at TIMESTAMP) Table: customers (id INT PK, name VARCHAR, country VARCHAR, email VARCHAR) Table: products (id INT PK, name VARCHAR, category VARCHAR, price DECIMAL) Table: order_items (id INT PK, order_id INT FK→orders.id, product_id INT FK→products.id, quantity INT, unit_price DECIMAL) FOREIGN KEYS: - orders.customer_id → customers.id - order_items.order_id → orders.id - order_items.product_id → products.id USER QUESTION: "What were the top 5 product categories by revenue last month?" RULES: - Only use tables and columns from the schema above - Do not use column names that do not exist in the schema - For date filtering, today is 2026-04-24 - Return only the SQL query, no explanation SQL: ``` The LLM completes the prompt by generating the SQL. This approach is powerful because: - It leverages the LLM's deep understanding of SQL syntax, functions, and idiomatic patterns - Schema context is injected fresh at every call, so schema changes don't require model retraining - Few-shot examples in the prompt can steer the model toward dialect-specific syntax (PostgreSQL window functions, BigQuery ARRAY functions, etc.) --- ## Stage 4a: Prompt Engineering Depth The quality of the generated SQL is almost entirely determined by the quality of the prompt. Several techniques significantly improve output accuracy. ### Few-shot examples Including 3–5 (question, SQL) examples directly in the prompt — selected to be semantically similar to the current question — dramatically improves output quality. This is called **in-context learning** and is more sample-efficient than fine-tuning for most production databases. Example selection is typically done by embedding similarity: the system finds the K most similar questions from a curated example bank and inserts them into the prompt. ### Chain-of-thought (CoT) prompting For complex queries (multi-table joins, nested subqueries, window functions), adding a scratchpad step before the final SQL output improves accuracy. The model is instructed to: 1. Identify which tables are needed 2. Identify which columns are needed 3. Determine the join conditions 4. Determine the aggregation logic 5. Write the final SQL This mirrors how a skilled analyst would approach the problem and significantly reduces errors on queries that require multi-step reasoning. ### Dialect-specific instructions SQL syntax varies significantly across databases. `DATE_TRUNC` exists in PostgreSQL and BigQuery but not MySQL. `TOP N` is SQL Server syntax; `LIMIT N` is MySQL/PostgreSQL. Window functions are available in PostgreSQL 8.4+ but have limited support in some older MySQL versions. The prompt must explicitly specify the database dialect and version, and optionally include dialect-specific function examples. ### Self-consistency sampling For high-stakes queries, the system generates N independent SQL candidates (N=5–10) with temperature > 0, then selects the most common output via **majority voting** or executes all and selects the one that returns the most plausible result. This is computationally expensive but dramatically reduces one-off hallucination errors. --- ## Stage 5: Post-Processing and Validation The LLM output cannot be trusted blindly. Before execution, the generated SQL must pass a validation layer. ### Syntax parsing The SQL string is parsed using a database-specific parser (e.g., `sqlglot`, `pglast`, `sqlparse`) to verify syntactic correctness. If parsing fails, the error message is fed back to the LLM as a second-pass correction prompt: ``` The following SQL has a syntax error: [error message] Original query: [query] Corrected query: ``` This self-repair loop resolves the majority of LLM syntax errors, which are usually minor (missing commas, unclosed parentheses, wrong function name casing). ### Schema validation After parsing, the AST (abstract syntax tree) of the query is traversed to verify: - Every referenced table exists in the schema - Every referenced column exists in the referenced table - Every function call is valid for the target database dialect References that fail validation are flagged for correction. This catches the most common hallucination: the LLM inventing a column name that sounds plausible but doesn't exist. ### Semantic validation Some semantic errors can be caught statically: - **Aggregation without GROUP BY**: `SELECT customer_id, SUM(amount) FROM orders` — `customer_id` is ungrouped in an aggregate query - **Ambiguous column references**: `SELECT name FROM orders JOIN customers ON ...` — `name` exists in `customers` but not `orders`; some dialects will error, others will silently return wrong data - **Cross-dialect functions**: calling a PostgreSQL function against a MySQL connection ### Dry-run execution (EXPLAIN / LIMIT 0) For SQL that passes syntactic and semantic validation, a final check runs the query with `EXPLAIN` (or `LIMIT 0` for SELECT) to verify the query planner can resolve it. This catches runtime errors like: - Type mismatches (`WHERE created_at > 'not a date'`) - Schema drift (a column was dropped after the schema cache was last updated) - Permission errors (the query user lacks SELECT on a referenced table) --- ## Stage 6: Execution and Result Handling The validated query is executed against the database through a connection pool. Result handling includes: ### Result set sizing Unbounded queries (`SELECT * FROM orders`) can return millions of rows. The execution layer always wraps user queries with a `LIMIT` clause (typically 1000–10000) unless the user's intent requires a full aggregation. For aggregation queries, row counts are naturally bounded by the number of distinct group values. ### Automatic visualization suggestion The result set schema (column names, data types, cardinality) is analyzed to suggest the appropriate visualization:

Result shape	Suggested chart
1 column, 1 row	KPI / metric card
1 numeric column, N rows	Bar chart
1 date column + 1 numeric column	Line chart
2 columns (category + numeric)	Bar or pie chart
Many columns, many rows	Data table
2 numeric columns	Scatter plot

This analysis is deterministic and based on column type inspection — it does not require another LLM call. ### Error surfacing If the query fails at execution time despite passing all validation stages, the raw database error is translated into a user-friendly message and fed back into the correction loop. Production systems keep a retry budget (typically 2–3 attempts) before surfacing a failure to the user with an explanation of what went wrong. --- ## Key Challenges and Failure Modes Understanding where NL2SQL systems fail is as important as understanding how they work. ### Ambiguity Natural language is inherently ambiguous. "Show me the top customers" could mean: - Top customers by total spend - Top customers by order count - Top customers by recency - Top customers in a specific region Without disambiguation, the system must make a default interpretation. Production systems handle this by either asking a clarifying question (at the cost of user experience latency) or generating the most statistically common interpretation and surfacing it transparently to the user. ### Multi-hop reasoning Questions that require chaining multiple logical steps — "Which customers placed orders this quarter but not last quarter?" — require the model to generate subqueries or CTEs. LLMs with strong code understanding handle this well, but smaller or less capable models often flatten these into incorrect single-level queries. ### Temporal reasoning Temporal expressions like "last month", "this quarter", "year to date", "rolling 30 days" must be resolved to concrete date boundaries at query time. This requires injecting the current timestamp into the prompt and ensuring the model can correctly compute: - `DATE_TRUNC('month', CURRENT_DATE) - INTERVAL '1 month'` for "last month start" - `DATE_TRUNC('month', CURRENT_DATE) - INTERVAL '1 day'` for "last month end" Errors here are silent: the query runs and returns results, but for the wrong date range. ### Schema sparsity When a database schema has minimal metadata — no column descriptions, no sample values, ambiguous naming conventions (`col_a`, `val1`, `flag_x`) — schema linking fails and the LLM hallucinates. The quality of NL2SQL output is fundamentally bounded by the quality of schema documentation. ### Dialect fragmentation Deploying one NL2SQL system across PostgreSQL, MySQL, BigQuery, Redshift, Snowflake, and SQL Server requires handling significant SQL dialect differences. A single prompt template cannot cover all dialects. Production systems maintain per-dialect prompt templates and use the schema retrieval stage to inject dialect-appropriate function examples. --- ## Accuracy Benchmarks The standard NL2SQL benchmark is **Spider** (Yale, 2018) — 10,181 questions across 200 databases. Current state-of-the-art systems achieve:

System	Spider exact match
RESDSQL (2022, fine-tuned T5)	79.9%
DIN-SQL + GPT-4 (2023)	82.8%
DAIL-SQL + GPT-4 (2023)	86.6%
MCS-SQL + GPT-4 (2024)	89.6%
Claude 3.5 Sonnet (few-shot)	~87–89%

The **BIRD benchmark** (2023) is considered harder and more realistic — it includes value linking, external knowledge requirements, and noisier schemas. Top systems score ~65–72% on BIRD, exposing a significant gap between benchmark performance and real-world deployment quality. Production accuracy on real enterprise databases is typically lower than benchmark numbers due to schema complexity, domain-specific terminology, and the long tail of unusual question patterns. --- ## Architecture Summary A production NL2SQL system looks like this end-to-end: ![NL2SQL Pipeline Architecture](/images/blogs/natural-language-to-sql/images/architecture.svg) Every stage is independently cacheable. Schema retrieval results can be cached per-database with a TTL tied to schema change events. Few-shot examples can be precomputed and stored in a vector index. LLM outputs can be cached for identical (question + schema hash) inputs, eliminating redundant inference costs for repeated questions. --- ## What Makes a Good NL2SQL System The gap between a toy demo and a production-ready NL2SQL system comes down to a few properties: **Schema grounding.** The system must work against your actual schema, not a generic one. Every table, every column, every foreign key must be available to the model at inference time with accurate metadata. **Validation depth.** SQL that parses correctly can still be semantically wrong. A robust validation layer that catches schema violations, aggregation errors, and type mismatches before execution is non-negotiable. **Dialect fidelity.** A system that only works on one database type is limited. Production NL2SQL must handle the dialect your database actually speaks. **Failure transparency.** When the system cannot answer a question confidently, it should say so rather than returning plausibly wrong results. Silent failures are the most dangerous outcome in a data analytics context. **Self-improvement loop.** Each query that fails or gets corrected by a user is a labeled training example. Systems that capture this signal and use it to improve few-shot examples or fine-tune retrieval models improve continuously over time. --- NL2SQL has moved far beyond research benchmarks. The underlying technology is mature enough for production use at scale, and the systems that deploy it well are the ones that treat it as an engineering discipline — with attention to schema quality, validation rigor, and feedback loops — rather than a one-shot LLM call. If you want to see these principles in action without building the pipeline yourself, [Draxlr's AI SQL tool](/blogs/ai-sql-tool) implements schema-grounded NL2SQL end-to-end — letting your team query any connected database in plain English and get back charts and dashboards instantly.

AI SQL Tool: What it is and How to Build Dashboards from Plain-English Questions

hello@draxlr.com (Draxlr) — Thu, 23 Apr 2026 00:00:00 GMT

The **AI SQL tool** is changing how teams access data. Yet, writing SQL still feels intimidating for many. Not everyone on your team can run a "SELECT \* FROM" confidently. But they do have questions about the data. _"Which campaign got us the most users?"_ _"What’s our revenue in Europe?"_ These questions often sit in Slack threads or email drafts, waiting on someone with SQL expertise. That’s where AI SQL tools come in. In this guide, you’ll learn exactly how AI SQL tools work, why they matter, and how Draxlr makes querying, charting, and dashboard building as easy as asking a question. --- ## What is an AI SQL tool? An **AI SQL Tool** is software that turns plain English into working SQL queries. It removes the need for manual SQL writing and opens access to data for all roles across the company. Think of it as a translator between humans and databases. Using it feels like chatting with your database, just like you would with ChatGPT. You ask a question, the AI understands your intent, and gives you a ready-to-use SQL query along with a clear visual result. You type "List of orders grouped by employee's name" and get an SQL query and visualization. ![Draxlr is the best AI SQL tool](/images/blogs/ai-sql-tool/images/ai-chat.png) --- ## How does an AI SQL tool work under the hood? An AI SQL tool is not magic. It combines three things to turn your question into an accurate query: 1. **Schema awareness:** The tool reads your database schema, including table names, column names, data types, and relationships between tables. This gives the AI the vocabulary it needs to map your question to real data. 2. **A large language model (LLM):** Your plain-English question is sent to an LLM along with the schema context. The model uses both to infer intent, pick the right tables, apply the right joins, and generate a syntactically valid SQL statement. 3. **Execution and visualization layer:** The generated SQL is run against your database, and the result set is analyzed to suggest the best chart type, whether that is a bar chart, pie chart, time series, or table. The quality of an AI SQL tool depends heavily on how well it grounds the LLM in your actual schema. Tools that only pass the question to a generic model without schema context tend to hallucinate column names and produce queries that fail. For a deeper technical breakdown of every stage in this pipeline — tokenization, schema linking, prompt assembly, validation, and the self-repair loop — see [Natural Language to SQL: How It Works Under the Hood](/blogs/natural-language-to-sql). --- ## Who Uses the AI SQL Tool? The AI SQL Tool meets people where they are, making data instantly usable for different roles across the company. 1. **Founders:** Track feature adoption, product usage, and growth trends without relying on an analyst. 2. **Product Managers:** Get real-time answers about user behavior, funnel drop-offs, and feature engagement using plain English. 3. **Marketers:** Analyze campaign performance, channel ROI, and regional impact without sifting through complex spreadsheets. 4. **Executives:** Ask high-level questions about revenue, churn, or monthly active users and get visual answers you can act on. If you ask questions and care about data-driven decisions, AI SQL Tool is built for you. --- ## How an AI SQL Tool Benefits Your Team When evaluating an AI SQL tool, it's essential to understand exactly how it improves your daily workflow and contributes to your team's productivity. **1. Simplicity and ease of use**
A great AI SQL tool removes complexity completely. It lets anyone on your team, no matter their technical expertise, ask data questions naturally. Evaluate this carefully: - Can your marketing manager easily get insights from your database without relying on a data analyst? **2. Accuracy and trust in data**
Data decisions are only valuable if the underlying data is accurate. Verify: - Does the tool consistently provide accurate, error-free queries, building confidence rather than doubt? **3. Actionable insights, not just raw queries**
An effective AI SQL tool should do more than just write queries. It should deliver visual, actionable insights ready to integrate into your workflows. Confirm: - Can you instantly create dashboards, schedule automated reports, or embed analytics directly into your business tools? --- ## A typical before-and-after: what teams actually save Consider a mid-size SaaS team with one data analyst and a steady stream of ad-hoc requests. **Before an AI SQL tool:** A product manager pings the analyst asking for weekly signup breakdowns by plan. The analyst is mid-sprint on a retention model, so the request waits two days. When the query is finally written, the PM wants a small tweak — another half-day round trip. Multiply this across marketing, support, and leadership, and the analyst spends 10–15 hours a week on repeat one-off queries. **After adopting Draxlr:** The same PM types "weekly signups by plan over the last 8 weeks" directly into Draxlr, gets the SQL, the chart, and pins it to a dashboard in under two minutes. The analyst gets those 10–15 hours back for the retention model. Decisions that used to wait days happen in the same meeting they are raised in. The real return isn't just faster queries — it's the end of the queue between a business question and a business answer. > "Draxlr helps me turn raw SQL data into clear dashboards without needing a data team. It saves time by letting me build and share reports directly from my database, using either AI, SQL, or their Visual Query Builder." > > — [Verified G2 Reviewer](https://www.g2.com/products/draxlr/reviews) ⭐ 4.9/5 on G2 --- ## How Draxlr Compares to Other AI SQL Tools Choosing the right AI SQL tool depends on your team’s size, technical depth, and budget. Here is how the leading options stack up:

Tool	Starting Price	AI SQL Generation	Dashboard Building	Best For
Draxlr	$25/month	Yes	Yes	Small to mid-size teams wanting fast, affordable insights
Looker (with Gemini)	~$5,000/month (custom)	Yes	Yes	Enterprises already on Google Cloud needing governed BI
Metabase + Metabot AI	~$200/month ($100 base + $100 AI add-on)	Yes	Yes	Technical teams comfortable with self-hosted BI
Tableau + Tableau Agent	$70+/user/month	Yes	Yes	Enterprise teams already in the Salesforce ecosystem
ThoughtSpot	~$1,250/month	Yes	Yes	Large enterprises with high query volume and big budgets

**Why Draxlr stands out:** - **Flat pricing, not per-seat.** $25/month covers your whole team, unlike Tableau ($70/user) or ThoughtSpot (enterprise contracts). - **No add-on tax for AI.** AI SQL generation is included, not a $100/month bolt-on like Metabot. - **Zero setup overhead.** No self-hosting like Metabase, no Salesforce ecosystem lock-in like Tableau, and no sales call to see pricing like ThoughtSpot. - **Built for business users first.** Looker and Tableau require a modeling layer (LookML) or a Salesforce-trained admin; Draxlr is designed so a marketer or founder can get a dashboard without ever seeing SQL or setup overhead. If your team wants AI-powered insights without enterprise budgets or dedicated BI engineers, Draxlr is the most accessible entry point. ### Draxlr pricing at a glance

Plan	Price	Databases	Users	Best For
Starter	$25/month	1	Small team	Founders and solo operators getting started
Growth	$75/month	2	10 users	Growing teams needing multi-DB access and collaboration
Scale	$125/month	5	30 users	Mid-size companies with multiple teams and data sources

All plans include AI SQL generation, dashboards, scheduled alerts, and embeds — no feature gating, no per-seat surprises. --- ## How to build dashboards using Draxlr’s AI SQL tool With [Draxlr](https://www.draxlr.com/), you type your question in plain English. The tool generates the SQL, runs the query on your database, and shows the result as a visualization. The entire process takes minutes and requires no coding. Here’s how it works: ### 1. Connect your database Begin by securely connecting your SQL database to Draxlr. It supports popular databases like PostgreSQL, MySQL, MSSQL, Databricks, and more. Instead of syncing or storing your data, Draxlr only reads the schema, which is the structure of your data, including table names, columns, and how they relate to each other. **Time to first dashboard: under 5 minutes.** Most teams go from signup to their first AI-generated chart in a single coffee break — no DBA involvement, no schema modeling, no waiting on a sales demo. If you want to skip the database step entirely, Draxlr ships with demo data so you can try it immediately. ### 2. Ask a question in plain English Once your database is connected, or if you choose to use Draxlr’s demo data, you can begin asking questions immediately. For example, with the demo database, you can try 1. "Show me total revenue by country for the last three months." 2. "Give me all the products by category." Draxlr AI understands your question in plain English and converts it into an SQL query that works with your actual schema. You do not need to know the structure of your tables or the exact column names. ### 3. Get SQL and visualizations that match your query result. After you ask a question, Draxlr analyzes it and generates an SQL query that matches your database structure. The SQL is built with full awareness of your table names, column naming patterns, and how your data is connected. This includes: 1. Joins across related tables 2. Filters for date ranges, categories, or custom conditions 3. Grouping and aggregations based on the intent of your question You can choose to view or hide the generated SQL. Along with the query, Draxlr also returns visual representations of the result. Whether the data fits a bar chart, pie chart, time series, or table, Draxlr suggests chart types that best match the output. There is no need to configure anything manually. ### 4. Build and Organize Dashboards After a visualization is generated, users will primarily see two buttons below the result. The first is **Add to Dashboard**, which adds the current visualization to a selected dashboard. Users can choose from existing dashboards, making it easy to organize and build views over time. The second is **Customize**, which takes users to Draxlr’s raw query builder. This is where deeper changes can be made to the visualization, such as enabling or disabling grid lines, formatting number values, adding a trend line, and adjusting other more visual settings. ![Draxlr - Add AI queries to Dashboard](/images/blogs/ai-sql-tool/images/dashboard.png) ### 5. Share, Automate, and Embed Once your dashboard is ready, Draxlr offers several ways to bring it into your team’s workflow: 1. **Slack and email digests.** Schedule updates to be delivered via [Slack or email](https://www.draxlr.com/blogs/how-to-set-email-alerts-when-your-sql-data-changes/) at regular intervals or specific times, so your team stays informed without checking manually. 2. **Threshold-based alerts.** Trigger a notification when a KPI crosses a limit — for example, when daily signups drop below 50 or refunds spike above a set percentage. 3. **Secure dashboard sharing.** Share a live dashboard link with anyone by setting a PIN, giving external stakeholders access without requiring login. 4. **Embeddable dashboards.** Embed dashboards into your internal tools, customer portals, or web apps so insights appear where your team and customers already work. 5. **CSV and Excel exports.** Any chart or table can be exported for finance reviews, board packs, or further analysis in spreadsheets. 6. **Webhooks for custom workflows.** Push query results or alerts to any endpoint, so you can wire Draxlr into internal tools, incident channels, or automation platforms like Zapier and n8n. --- ## Security and privacy: what happens to your data? Connecting a database to an AI tool raises valid concerns. Here is how Draxlr handles it: 1. **Schema only, no data sync:** Draxlr reads only the structure of your database (table names, column names, and relationships). Your actual rows are never copied, stored, or used to train any model. 2. **Queries run on your database:** When a query is generated, it executes directly against your database. Results are returned to your browser session and are not retained beyond what is needed to render the dashboard. 3. **Read-only connections supported:** You can connect Draxlr using a read-only database user, so the tool cannot modify, delete, or insert data under any circumstances. 4. **Encrypted credentials:** Database credentials are encrypted at rest, and all connections use SSL where supported by your database. If you work with sensitive data, we recommend connecting through a read replica with a read-only user and restricting access to the specific schemas you want the AI to see. --- ## Limitations: where AI SQL tools still struggle AI SQL tools have come a long way, but they are not a full replacement for a human analyst in every situation. It helps to know where they fall short so you can use them effectively. 1. **Read-only by design.** Draxlr is built for querying and analysis, not data entry or updates. If you need to insert or modify records, you will need a separate database client. 2. **Ambiguous questions:** If your question can be interpreted in multiple ways ("top customers" by revenue? by order count? by recency?), the AI has to guess. The more specific your question, the more accurate the result. 3. **Complex multi-step analysis:** Questions that require several chained calculations, window functions, or subtle business logic (like cohort retention or attribution) may need manual refinement in the query builder. 4. **Non-standard schema naming:** If your tables and columns use cryptic names or abbreviations with no documentation, the AI has less context to work with. Adding table and column descriptions improves accuracy significantly. 5. **Business logic that lives outside the database:** If "active user" is defined by a rule your team keeps in a Notion doc rather than in the data, the AI cannot know about it unless you spell it out in the question. ### How Draxlr addresses these limitations Draxlr is built with these real-world challenges in mind, and gives you two powerful ways to close the gap: 1. **App-wide and table-level context:** You can provide Draxlr with custom context for your entire application, including business logic, domain definitions, and descriptions at both the table and field level. For example, you can tell Draxlr that "active user" means anyone who logged in within the last 30 days, or that the `orders.status` column uses specific codes like `P` for pending and `F` for fulfilled. This context is passed to the AI along with your schema, so queries reflect how your business actually works, not just what the raw column names suggest. 2. **Full control over SQL and visualizations:** Every query Draxlr generates is fully editable. You can view the SQL, tweak joins or filters, and rerun it instantly. On the visualization side, the raw query builder lets you change chart types, adjust axes, format numbers, toggle grid lines, add trend lines, and fine-tune the look of every dashboard tile. The AI gives you a strong starting point, and you stay in control of the final result. Together, these two features mean you are never stuck with an AI guess. You can teach Draxlr about your business once, and refine any individual query or chart whenever you need to. --- ## Final Thoughts Most teams don’t lack data, they lack a fast, reliable way to use it. Draxlr’s AI SQL tool removes the need to write queries, build charts manually, or depend on analysts for answers. It’s not just a shortcut. It’s a shift in how teams interact with their databases, turning plain-English questions into live dashboards, instantly and accurately. If your team is spending more time asking for data than acting on it, it’s time to try a better way. ### Try Draxlr today Connect a database or use our demo data — no credit card required. Build your first AI-generated dashboard in under 5 minutes. Flat $25/month when you’re ready to upgrade. Connect your Database --- ## FAQs ### 1. Is there an AI for SQL? Yes. AI SQL tools let you ask questions in plain English and get valid SQL queries in return. Tools like Draxlr generate queries based on your database schema, so you don’t need to write SQL manually. ### 2. Can AI create dashboards? Yes. AI tools like Draxlr can create dashboards by generating queries from plain English and organizing the results into charts. You can save these charts, arrange them into dashboards, and schedule reports—without writing SQL or configuring visuals manually. ### 3. What is the best AI SQL tool? The best AI SQL tool depends on your team’s needs, but top tools help you write queries in plain English, understand your database schema, and generate visual dashboards instantly. Draxlr stands out by combining accurate SQL generation with schema awareness, chart suggestions, and live database connectivity—making it a strong choice for teams that want speed without sacrificing control. ### 4. Is it safe to connect my database to an AI SQL tool? Yes, when the tool is built with the right safeguards. Draxlr only reads your schema (not your data), supports read-only database users, and encrypts credentials at rest. Your row-level data is never used to train any AI model. For sensitive workloads, connect through a read replica with a restricted user. ### 5. Which databases does Draxlr’s AI SQL tool work with? Draxlr supports most popular SQL databases, including PostgreSQL, MySQL, MSSQL, MariaDB, Databricks, Amazon Redshift, and more. As long as your database speaks SQL and is reachable over a secure connection, Draxlr can connect to it and generate queries against its schema. ### 6. How accurate are AI-generated SQL queries? Accuracy depends on two things: how specific your question is and how well the tool grounds the AI in your actual schema. Draxlr reads your real table and column names before generating a query, which significantly reduces hallucinations compared to generic LLM-based tools. For ambiguous or highly complex questions, you can always view the generated SQL and refine it in the query builder before saving it to a dashboard. ### 7. Do I need to know SQL to use an AI SQL tool? No. The entire point of an AI SQL tool is to remove that requirement. You ask questions in plain English and get back charts and dashboards. That said, if you do know SQL, you can inspect and edit the generated queries for full control—making it useful for both non-technical users and analysts. ### 8. How is this different from just using ChatGPT with my database schema? ChatGPT can write SQL if you paste in your schema, but it has three practical limits. First, it has no live connection to your database, so you have to copy queries out, run them elsewhere, and copy results back — every single time. Second, it does not produce dashboards, charts, scheduled alerts, or shareable links. Third, pasting schema into a general-purpose chatbot raises data-governance questions that a purpose-built tool handles with read-only connections, encrypted credentials, and schema-only access. Draxlr is built end-to-end for the query-to-dashboard loop, so the AI, the database, and the visualization layer all work together in one place.

How to Add Analytics to Your SaaS Product (2026 Complete Guide)

hello@draxlr.com (Draxlr) — Sat, 18 Apr 2026 00:00:00 GMT

Your users are sitting inside your product right now and they are exporting data to spreadsheets to answer questions your app should already be answering for them. That is the gap an [embedded analytics tool](/embedded-analytics-tool/) closes. And in 2026, closing that gap is no longer optional. It is what separates products users stick with from ones they quietly churn away from. This guide walks you through exactly how to add analytics to your SaaS product, from choosing your approach to implementing multi-tenant security to picking the right tools for your stack. --- ## What Does Adding Analytics to a SaaS Product Actually Mean? Before you write a single line of code, it helps to be precise about what you are building. There are two very different things people mean when they talk about adding analytics to a SaaS product. **Product analytics** tracks how your users behave inside your product. Tools like Mixpanel, Amplitude, and PostHog fall into this category. This is internal data you use to improve your product. **Embedded analytics** surfaces data insights to your users, inside your product, so they can make better decisions without leaving your app. This is a customer-facing feature. This guide covers embedded analytics, the kind that becomes part of your product experience rather than a tool your engineering team uses in the background. When done right, embedded analytics means a user logs into your SaaS platform and sees dashboards, charts, and reports that reflect their data, styled to match your brand, without ever knowing they are powered by a separate analytics engine. --- ## Why SaaS Products Need Embedded Analytics in 2026 The business case has never been stronger. ### 1. Retention When users build workflows around dashboards inside your product, the switching cost of leaving goes up dramatically. Analytics features create daily habits. Daily habits create long-term customers. ### 2. Monetization Analytics is one of the most natural upsell levers in SaaS. Basic reporting in the free tier, advanced dashboards and scheduled exports in Pro. This is a premium feature customers are consistently willing to pay for without feeling nickel-and-dimed. ### 3. Reduced support load Custom report requests are one of the biggest drains on engineering and customer success teams. A well-built self-service analytics layer lets users answer their own questions. One financial SaaS company cut custom report requests by 60% after adding conversational analytics. ### 4. Competitive differentiation In crowded SaaS categories, analytics depth is increasingly a buying criterion. Buyers doing product evaluations are asking what your reporting looks like. If the answer is export to CSV, you are losing deals to competitors who embedded it properly. --- ## The Core Decision: Build vs Buy This is the question every SaaS team faces, and getting it wrong is expensive in both directions. ### Building from scratch You assemble the stack yourself with a charting library like D3.js, Chart.js, or Apache ECharts, plus a data aggregation layer, query optimization, caching, and a security model. Total control, but the real cost is almost always underestimated. Industry data puts average custom analytics build timelines at **8 to 18 months** with costs **exceeding $400,000** before you factor in ongoing maintenance. Building makes sense in exactly one scenario. When analytics is your core product differentiator. If your pitch is that you are the most intelligent data platform in your category, building gives you the competitive moat you need. For everyone else, it is engineering time that does not move your core product forward. ### Embedding a third-party platform You license an embedded analytics platform like [Draxlr](/), integrate it via SDK or API, and white-label it so it looks native to your product. Time to market is days to weeks rather than months. The tradeoff is less control over the roadmap and a per-seat or usage-based licensing cost to factor in. ### Using component libraries Pre-built data visualization components that you style and integrate into your product's UI sit in the middle ground. More control than a full platform, faster than building from scratch. **Rule of thumb.** If your team has fewer than 3 engineers to dedicate to analytics infrastructure long-term, buy. The build option requires sustained investment that most SaaS teams underestimate. --- ## Step by Step: How to Add Analytics to Your SaaS Product ### Step 1: Define what your users actually need to see Before you pick a tool or write any code, talk to your users. The biggest mistake teams make is building dashboards based on what data they have rather than what decisions their users are trying to make. Run 5 to 10 user interviews focused on these questions: - What data do you currently export from this product to answer questions? - What reports do you regularly request from our team? - What decisions are you making weekly where better data would help? Their answers tell you exactly what to build. Prioritize the dashboards that replace the most common export-and-spreadsheet workflows. ### Step 2: Design your multi-tenant data model This is the most critical architectural decision. In a SaaS environment, you have many customers sharing the same infrastructure and each one must only ever see their own data. There is no room for error here. You have three main approaches to multi-tenant data isolation. - **Separate databases per tenant** offers the strongest isolation and highest infrastructure cost. It is best for enterprise or regulated industries like healthcare and fintech. - **Separate schemas per tenant** is a solid middle ground. Each tenant gets their own schema within a shared database. Easier to manage than separate databases and still delivers strong isolation. - **Shared schema with tenant ID filtering** is the most common approach for SaaS at scale. Every table has a tenant ID column and all queries are automatically scoped to the current tenant via row-level security policies enforced at the database or application layer. Whichever approach you choose, never rely on application-layer filtering alone as your security boundary. Enforce tenant isolation at the database level with row-level security policies and test it aggressively. ### Step 3: Choose your embedding method Once you have a data model, you need to decide how the analytics will appear inside your product. There are two main approaches. - **iframe embedding** lets you embed a dashboard from your analytics platform inside your product using an iframe tag. Faster to implement, but customization is limited and performance can be poor. The analytics always feel slightly bolted-on rather than native. - **SDK or component embedding** uses a JavaScript SDK or web component that renders analytics directly in your DOM, inheriting your product's styling, interactions, and state. More engineering work upfront, but the result is indistinguishable from a native feature. For most SaaS products, SDK-based embedding is the right target. Iframes are acceptable for a proof of concept or an MVP, but plan to migrate if you are serious about product quality. ### Step 4: Implement authentication and authorization Your analytics layer needs to know who the user is, what tenant they belong to, and what data they are allowed to see. The standard approach is JWT-based authentication, which stands for JSON Web Token. Here is the general flow. 1. User logs into your SaaS product 2. Your backend generates a signed JWT containing the user ID, tenant ID, and permission scope 3. Your frontend passes this token when initializing the analytics component 4. The analytics platform validates the token and scopes all queries to the user's tenant Never expose raw database credentials or unscoped API keys to the frontend. Every analytics query should pass through your authentication layer. Permissions typically fall into three tiers. Viewers can see dashboards, apply filters, and drill down. Editors can customize dashboards and create saved views. Admins can create new reports and manage data access for their team. ### Step 5: Build your first dashboard Start with the highest-value, lowest-complexity dashboard your user research identified. Do not try to build everything at once. A good first embedded dashboard typically includes 4 to 6 KPI cards for key numbers at a glance, 1 to 2 time-series charts for trends over time, a filterable table for exploration, and a date range picker. Ship this, get user feedback, then iterate. The mistake most teams make is spending months building a comprehensive analytics suite that users never actually wanted. ### Step 6: White-label the experience For analytics to feel native to your product, it needs to look like your product. This means choosing a [white-label embedded BI solution](/white-label-bi-solution/) that lets you match your brand's color palette, typography, and spacing, remove vendor branding from the analytics experience, and use terminology that fits your product instead of generic BI language. If users notice the analytics looks different, you have not finished the integration. The goal is for them to forget it is a separate system at all. ### Step 7: Connect to your data sources Your analytics need to read from where your application data lives. Modern [embedded analytics tools](/embedded-analytics-tool/) connect directly to cloud data warehouses like Snowflake, BigQuery, Databricks, Redshift, and ClickHouse without requiring separate ETL pipelines or data exports. If your application writes to a PostgreSQL or MySQL database, you can connect directly. For larger SaaS products with significant data volumes, pushing data to a warehouse and querying it from there gives you better performance and keeps analytics queries from impacting your operational database. Implement query caching early. Frequently accessed dashboards should serve from cache rather than hitting your database on every page load. Most embedded analytics tools handle this automatically, but you will want to configure cache TTLs to match how fresh your data needs to be. --- ## Choosing the Right Embedded Analytics Tool Picking the right platform is where most SaaS teams lose weeks of time. Here is an honest breakdown of what is available in 2026, starting with our top recommendation for most teams. ### Draxlr (Recommended for Most SaaS Teams) Draxlr is purpose-built for exactly the use case this guide covers. It lets you embed interactive, white-labeled analytics directly into a SaaS product without months of engineering overhead. What makes it stand out is how it handles the full spectrum of users on your team. Non-technical users can build dashboards using a visual query builder or simply ask questions in plain English through an AI text-to-SQL interface. Technical users get full SQL access with complex joins, virtual columns, and database-aware autocomplete. Both work inside the same platform without getting in each other's way. For embedding, Draxlr offers React and Vue SDKs alongside iframe options, giving your team flexibility depending on how deeply native you want the integration to feel. White-labeling is available across all plans, not locked behind an enterprise tier, which means your users see your brand from day one. Multi-tenancy is built in from the ground up, with row-level security and secure embed tokens enforcing data isolation per customer. It connects to PostgreSQL, MySQL, Snowflake, BigQuery, Redshift, Databricks, ClickHouse, Supabase, and more without needing a separate ETL layer. Draxlr is an [affordable embedded analytics tool](/affordable-embedded-analytics-tool/) with pricing starting at **$75/month**, including unlimited external users and no per-user viewing charges. This means your analytics costs stay predictable as your customer base grows. Many competing platforms charge per viewer or dashboard session, which can become expensive and difficult to forecast at scale. Draxlr also offers a 7-day free trial with no credit card required. **Best for** SaaS teams that want fast embedding, full white-label control, SQL-driven workflows, and a platform that works for both technical and non-technical users without requiring a dedicated BI team. ### Other Embedded Analytics Tools Worth Considering

Tool	Starting Price	Pricing Model	Key Strength	Watch Out For
Draxlr	$75/month	Flat Pricing	Self-serve AI-powered tool without the enterprise overhead	Supports SQL databases
Luzmo	$495/month, Billed annually	Usage-based, scales with Monthly Active Viewers	Good white-label options	Gets expensive at larger scale
Holistics	$800/month	Flat monthly, first 10 users included then $12.50 per additional user	Developer-friendly with version control	Limited interactivity for end users
Power BI	From ~$735/month (A1 SKU, 24/7) or ~$262/month (Fabric F2)	Capacity-based via Azure, no per-viewer fees but creators need Pro at $14/month each	Familiar to enterprise users	Complex capacity planning, hidden Azure and infrastructure costs that teams consistently underestimate
Metabase	$575/month (Pro, required for interactive embedding)	Per-seat, $12/month per viewer beyond 10. At 1,000 external viewers this reaches ~$149,000/year. Open-source only allows static embedding with Metabase branding	Open-source option, good for internal BI	Costs scale sharply with external viewers, white-labeling requires Pro or above
ThoughtSpot	Talk to Sales	Per-query consumption, can reach $5 to $6 per dashboard load	Natural language querying	Unpredictable costs at scale

Key evaluation criteria to weigh across any platform include the multi-tenant security model, SDK versus iframe embedding support, white-labeling depth covering fonts, colors, and custom terminology, self-service capabilities for end users, data warehouse connectivity, developer documentation quality, and pricing model whether per-embed, per-seat, or usage-based. --- ## Common Mistakes to Avoid **Skipping user research.** Building dashboards based on available data rather than user decisions leads to analytics nobody uses. **Underestimating multi-tenancy complexity.** Data isolation in a multi-tenant environment is harder than it looks. Plan your security model before you write a line of analytics code. **Starting with iframes and never migrating.** iframes feel fast to implement but create technical debt that compounds. If native integration is your goal, start there. **Launching too much at once.** Users do not adopt complex analytics overnight. Start with one high-value dashboard, teach users how to use it, then expand. **Ignoring performance.** Slow dashboards kill adoption faster than missing features. Implement caching, paginate large datasets, and set query timeouts from day one. **Not planning for self-service.** Users will eventually want to ask questions you did not anticipate. Building in some level of self-service exploration from the start is far easier than retrofitting it later. --- ## Monetizing Your Embedded Analytics Once you have built it, here is how SaaS companies typically package analytics as a revenue lever and structure their [embedded analytics pricing](/embedded-analytics-pricing/). **Tiered feature access** puts basic charts in the Free or Starter plan, advanced dashboards and drill-downs in Pro, and full self-service analytics with scheduled exports in Enterprise. **Usage-based limits** give the free tier a set number of dashboard views with paid tiers getting unlimited access. Simple to understand and predictable for both sides. **Analytics add-on** sells advanced reporting as a separate module priced per seat or per tenant. This works well when your core product is already priced on a different dimension. **Premium data experiences** brand advanced analytics as a Pro Analytics or Intelligence Pack standalone upsell. Customers are often willing to pay meaningfully more for analytics that is clearly positioned as high value. --- ## Conclusion Adding analytics to your SaaS product is one of the highest-ROI investments you can make in 2026. It increases retention, enables premium pricing, and reduces support load all at the same time. Start by talking to five of your users this week about what data they wish they could see without leaving your product. Their answers will tell you exactly where to begin. If you are ready to get started, try Draxlr which lets you connect your SQL database, build your first dashboard, and embed it into your product in days. No per-user view fees, no enterprise contract required, and a 7-day free trial with no credit card needed. Connect your Database --- ## FAQs ### 1. How long does it take to add analytics to a SaaS product? Using a third-party embedded analytics platform, most teams ship a first working dashboard in 1 to 2 weeks. Full production-ready implementation with multi-tenant security, white-labeling, and self-service typically takes 2 to 4 weeks for tools like Draxlr. ### 2. What is the difference between embedded analytics and product analytics? Product analytics tools like Mixpanel and Amplitude track how your users interact with your product. That is internal data for your team. Embedded analytics surfaces data insights to your users as a feature inside your product. ### 3. Do I need a data warehouse to implement embedded analytics? Not necessarily for a basic implementation. Many platforms can query your application database directly. However, for performance at scale and to avoid impacting your operational database, a data warehouse is strongly recommended. ### 4. How do I keep customer data separate in a multi-tenant setup? The most common approach is row-level security combined with tenant-scoped JWT authentication. Every query is automatically filtered to the authenticated tenant's data and enforced at the database level. ### 5. Can I white-label embedded analytics completely? Yes. Most modern embedded analytics platforms support full white-labeling including custom colors, fonts, logos, and terminology. The goal is for your users to never realize they are using a third-party analytics engine.

Best PostgreSQL Reporting Tools

hello@draxlr.com (Draxlr) — Tue, 14 Apr 2026 00:00:00 GMT

PostgreSQL is one of the most widely deployed databases in the world, and for good reason. It's reliable, flexible, and capable of handling everything from transactional workloads to analytical queries. But once your team moves beyond development and into production, the built-in psql interface and ad-hoc SQL stop being enough. You need dashboards your team can actually use without running queries by hand every time someone has a question. The challenge isn't the database. PostgreSQL can handle the workload. The challenge is access. Developers and data analysts can write queries, but product managers, support teams, and founders often can't. Without a dedicated reporting layer, every data question becomes a ticket and every ticket delays someone. Engineering time gets consumed by ad-hoc requests that should have been answered by a dashboard three weeks ago. **PostgreSQL reporting tools** solve this problem by sitting between your Postgres database and the people who need answers. They connect directly to your schema, let users explore data through visual interfaces or SQL, and help you build dashboards that update automatically without manual intervention. In this article, we break down the most reliable PostgreSQL reporting tools to consider for 2026. --- ## What are PostgreSQL reporting tools? **PostgreSQL reporting tools** connect directly to your Postgres database and turn live data into dashboards, charts, and shared reports without requiring SQL for every question. Rather than exposing raw database access to everyone on your team, these tools provide a controlled interface for querying, visualizing, and distributing data insights. Because these tools connect natively to PostgreSQL, there's no need for ETL pipelines or data duplication. Queries execute against your real schema using Postgres credentials and standard connections. Your data stays live, permissions stay consistent, and both technical and nontechnical users can get what they need from a single reporting workspace. --- ## How to choose a PostgreSQL reporting tool Choosing the right PostgreSQL reporting tool depends on what you want to achieve with your data. A SaaS team that needs product metrics will prioritize different features than a company looking to embed analytics for customers or replace a legacy BI stack. Start by identifying your primary goal. ### 1. If your goal is internal reporting for your team Pick a tool with strong SQL support, an intuitive dashboard builder, and simple sharing. You want clarity without adding another system to maintain. Tools that let non-technical teammates explore data without writing queries are especially valuable for keeping engineering time focused. ### 2. If your goal is customer-facing analytics Look for embedding, white-label controls, and row-level filtering. Your users should see analytics that look like part of your product, not an iframe pasted on top. This requires secure embed tokens and multi-tenant data filtering at the query level. ### 3. If your goal is replacing an existing analytics stack Prioritize tools with flexible permissions, reusable queries, scheduling, and scalable embedding. These features help you migrate and streamline an entire analytics layer rather than just adding another tool on top of what you already have. ### 4. If your goal is minimal engineering involvement Pick a tool that lets product, support, or operations teams explore data on their own. Features like visual query builders and AI text-to-SQL reduce the number of ad-hoc requests developers get pulled into. The best tools make self-service analytics genuinely accessible to nontechnical teammates. Ultimately, the right PostgreSQL reporting tool depends on whether you prioritize speed, customer experience, or long-term scalability. --- ## Key features to look for in PostgreSQL reporting tools A quick search for Postgres analytics or reporting tools will surface plenty of options, but they're not all built the same. Choosing the right one becomes much easier when you know which capabilities matter most for teams working directly on PostgreSQL. Here are the core features to evaluate when comparing PostgreSQL reporting tools. ### 1. Native PostgreSQL compatibility Your PostgreSQL reporting tool should connect directly to Postgres without ETL, sync jobs, or schema duplication. Native support means: 1. Queries run on your actual schema 2. No pipelines or replication 3. No new infrastructure to manage 4. Consistent permissions tied to Postgres roles Tools that require you to move data into a warehouse first will slow you down and add unnecessary complexity to your stack. ### 2. Ease of dashboard building Most teams using PostgreSQL don't have a dedicated BI department, so reporting has to be accessible. Look for features like: 1. Visual query building for nontechnical users 2. [Text to SQL AI](/blogs/ai-sql-tool/) 3. Reusable charts and saved queries 4. Filters and drilldowns 5. Clear layout controls 6. [Simple sharing via link or embed](/blogs/create-a-public-dashboard-url-for-your-sql-database/) The goal is that anyone on your team should be able to answer basic questions without developer involvement. ### 3. Embeddability (if you want customer-facing dashboards) If you plan to [embed analytics](/blogs/best-embedded-analytics-tools/) inside your product, choose a tool with: 1. Secure, stateless embed tokens 2. Row-level filtering for multi-tenant apps 3. White-label options Dashboards should feel native inside your product, not bolted on after the fact. ### 4. API access for automation Postgres teams often automate workflows like report generation, alerts, scheduled queries, or embedding logic. An [API-first reporting tool](/blogs/build-sql-dashboard-with-ai/) gives you: 1. Programmatic control over dashboards and queries 2. Flexibility for future integrations 3. Easier connection to your authentication layer 4. Automation of recurring reporting tasks Developers should be able to trigger or manage analytics via API just like any other part of their stack. ### 5. Data security and access control Your PostgreSQL reporting tool must respect the same security expectations your application has. Important features include: 1. No data copying or external storage 2. User-scoped dashboards and query access 3. Secure embedding with filtered views 4. Strong team permission controls 5. [Self-host option for sensitive environments](/blogs/ensuring-seamless-adoption-of-self-service-bi/) Since everything connects to your live production schema, access control is critical. ### 6. Documentation, support, and reliability Reporting is often implemented under pressure, late in the product cycle. Good documentation, responsive support, and a reliable UI save hours of frustration. Whether through docs, Slack communities, or direct support channels, pick a tool that won't leave you stuck when your team needs answers fast. --- ## The best PostgreSQL reporting tools to consider in 2026 Without further delay, here's a breakdown of the top PostgreSQL reporting tools worth exploring in 2026 and beyond. ### 1. Draxlr: Best PostgreSQL reporting tool for dashboards, team insights, and embedded analytics #### Best for SaaS teams using PostgreSQL who want a straightforward way to build dashboards, explore data, and offer embedded analytics without introducing a complicated BI stack. Connect your Database [Draxlr](/) works directly with your PostgreSQL database and gives teams a clean interface for creating reports, running SQL, and building dashboards without setup overhead. Built for modern product teams, it helps you ship reporting fast — whether the goal is internal visibility or customer-facing analytics. For technical users, Draxlr offers full SQL control, virtual columns, filters, drill-through features, and database-aware autocomplete that understands your Postgres schema. For nontechnical teammates, the visual query builder and AI text-to-SQL make data exploration accessible without needing to open a SQL editor. If your product includes customer analytics, Draxlr supports secure embedded dashboards with row-level filtering, white-label options, and React and Vue components that integrate naturally into your app. Teams can also automate reporting with scheduled emails, Slack alerts, and CSV, Excel, and PDF exports. Draxlr runs on your existing PostgreSQL infrastructure — no pipelines, warehouses, or modeling layers required. Everything connects directly to your live schema. #### Key features 1. Connects directly to PostgreSQL with no ETL or data prep work required 2. Visual Query Builder, full SQL editor, and AI Chat for text-to-SQL 3. Dashboard builder with filters, drill-through, and virtual columns 4. Embedded dashboards with secure tokens, row-level filters, and white-label controls 5. React and Vue SDKs for easy in-product analytics 6. Export options (CSV, Excel, PDF) and scheduled reports 7. Team-level permissions for safe access to production data 8. Supports both internal reporting and customer-facing analytics from the same workspace #### What users say about Draxlr > Draxlr offers a clean, modern interface and makes it incredibly easy to build dashboards — even for non-technical users. I especially like the visual quality and flexibility of the available graph types. It's intuitive, fast to set up, and has excellent usability from the start. The ability to securely share dashboards via link is a game-changer for our client reporting. — [Review from a SaaS Founder on G2](https://www.g2.com/products/draxlr/reviews/draxlr-review-11185807) #### Pricing Draxlr offers multiple plans depending on team size, embedding needs, and data volume. Pricing starts at **$25/month** --- ### 2. Metabase: Best for teams that want quick, no-code dashboards on Postgres #### Best for Small to mid-sized teams that want fast self-service reporting on PostgreSQL with minimal setup and a friendly interface for nontechnical users. [Metabase](https://www.metabase.com/) is one of the most widely adopted open-source BI tools for PostgreSQL. It connects natively to Postgres databases and provides a question-and-answer interface that allows users to explore data, build dashboards, and share reports without writing SQL. Setup is straightforward, and the open-source version is free to self-host. For teams that need quick wins on internal reporting, Metabase is hard to beat at the entry level. It supports SQL questions for power users while keeping the no-code query builder accessible for everyone else. The tool also supports scheduling and email delivery of reports, making it a common first choice for growing product teams. That said, advanced features like row-level security, white-label embedding, and SSO require paid plans. #### Key features 1. Native PostgreSQL connection with no configuration overhead 2. No-code Question Builder and full SQL editor for mixed-skill teams 3. Scheduled reports and automated email delivery #### What users say about Metabase > What I like best about Metabase is how fast it connects to PostgreSQL and MySQL databases without complex configuration. The no-code query builder is excellent for non-technical users to explore data, create charts, and build dashboards without writing SQL. #### Pricing Open-source self-hosted version is free. Starter plan starts at **$100/month**. Pro plan with SSO, row-level security, and embedding is **$575/month**. --- ### 3. Grafana: Best for operational and time-series dashboards on PostgreSQL #### Best for Engineering and DevOps teams that need real-time operational dashboards, metrics monitoring, and time-series visualization directly from a PostgreSQL datasource. [Grafana](https://grafana.com/) has a first-class PostgreSQL data source plugin that supports templating, annotations, alerting, and SQL-based panels. It is the standard choice for operational dashboards, especially for teams already using Prometheus, Loki, or other observability tooling. If you need to monitor application health, track database performance, or visualize time-series data from Postgres, Grafana is purpose-built for that use case. Where Grafana is less suited is for business reporting or product analytics aimed at nontechnical users. The interface requires comfort with metrics concepts and SQL, and building polished business dashboards takes more effort compared to purpose-built BI tools. But for engineering teams that live in dashboards, Grafana's flexibility and deep Postgres integration make it a strong fit. #### Key features 1. Native PostgreSQL datasource with full SQL panel support and macros 2. Alerting based on Postgres query results with notification routing 3. Extensive visualization library and community dashboard templates for Postgres monitoring #### What users say about Grafana > Grafana makes it straightforward to create interactive, real-time dashboards by pulling data from multiple sources. The customizable dashboards and intuitive interface are excellent, and the plugin ecosystem means you can connect to almost anything including PostgreSQL. #### Pricing Open-source version is free to self-host. Grafana Cloud starts at **$19/month** plus usage-based billing for logs and metrics. --- ### 4. Tableau: Best for analyst-driven reporting and complex visualizations on Postgres #### Best for Organizations with dedicated BI analysts who need sophisticated visualization, complex calculated fields, and enterprise governance connected to a PostgreSQL database. [Tableau](https://www.tableau.com/) has a native PostgreSQL connector and is one of the most capable visualization tools in the BI market. It supports complex data modeling, advanced calculated fields, and a broad library of chart types that go well beyond standard bar and line charts. For teams with analyst resources and a need for polished, deeply customized dashboards, Tableau delivers. The tradeoff is cost and complexity. Tableau is designed for structured BI workflows with dedicated analysts, not for fast self-service by product teams. Getting up to speed requires significant learning time, and the pricing model is built for enterprise deployments. For SaaS teams or small companies looking for quick access to their Postgres data, Tableau is often more than what's needed. #### Key features 1. Native PostgreSQL connector with live query and extract modes 2. Advanced calculated fields, LOD expressions, and complex data modeling 3. Enterprise governance with role-based access and workspace management #### What users say about Tableau > What I like best about Tableau is its ability to turn complex data into clear, interactive visualizations. It makes it easy to explore data, identify trends, and surface insights without needing deep technical skills. — G2 Review #### Pricing Creator license starts at **$75/user/month**. Explorer and Viewer licenses available at lower price points. --- ### 5. Apache Superset: Best open-source BI for PostgreSQL teams with engineering resources #### Best for Companies that want a powerful, open-source BI platform for PostgreSQL and have in-house engineering capacity to host and maintain their own analytics infrastructure. [Apache Superset](https://superset.apache.org/) is a widely used open-source analytics platform with native support for PostgreSQL. It provides a SQL IDE for writing queries, a no-code chart builder for visualizations, and interactive dashboards with filters and drilldowns. Being open-source and self-hosted, it gives teams complete control over their reporting infrastructure and no per-seat licensing fees. The main consideration is operational overhead. Superset requires self-hosting, configuration, and ongoing maintenance. Security hardening, upgrades, and performance tuning require dedicated engineering attention. For teams with the capacity to manage it, Superset is a strong and cost-effective option. For teams that want a managed solution, the operational burden typically outweighs the savings. #### Key features 1. SQL IDE and no-code chart builder with native PostgreSQL support 2. Broad library of visualizations with interactive dashboard filtering 3. Fully open-source and self-hosted with no per-user licensing costs #### What users say about Apache Superset > We can combine data from different systems for thorough analysis because the platform supports an extensive amount of data sources, including databases like Postgres, SQL Server and more. Easy creation and sharing of interactive dashboards with extensive chart types is one of its main features. — G2 Review #### Pricing Free and open-source. Operational costs depend on hosting, infrastructure, and internal engineering time. --- ### 6. Power BI: Best for organizations already using Microsoft tools #### Best for Large organizations that already operate within the Microsoft ecosystem and want to add BI reporting connected to PostgreSQL alongside Excel, Teams, and Azure services. [Power BI](https://www.microsoft.com/en-us/power-platform/products/power-bi) connects to PostgreSQL databases via the built-in Postgres connector and offers a comprehensive feature set for enterprise reporting. It supports DAX expressions, complex data modeling, and integration with the broader Microsoft stack including Azure, Excel, and Teams. For companies that are already standardized on Microsoft tools, Power BI's ecosystem fit makes it a natural choice. Outside the Microsoft ecosystem, Power BI is less compelling. Its desktop-first workflow, Windows dependency for some features, and enterprise-focused pricing can slow down product teams that need fast, lightweight reporting directly from Postgres. Embedding analytics in a web product also requires premium licensing and additional configuration that simpler tools handle out of the box. #### Key features 1. Native PostgreSQL connector for live and imported data models 2. Advanced DAX modeling with a large library of visualization types 3. Deep integration with Microsoft 365, Azure, and Teams for enterprise workflows #### What users say about Power BI > What I like most about Power BI is how it turns complex data into something easy to understand. The ability to create interactive dashboards and dynamic reports is a game changer for presenting insights to stakeholders. #### Pricing Power BI Desktop is free. Pro plan starts at **$10/user/month**. Premium capacity plans for embedded analytics start at **$20/user/month**. --- ## Implementing your PostgreSQL reporting tool effectively Choosing the right PostgreSQL reporting tool is only half the job. A tool can give you clean dashboards and powerful querying, but it won't deliver value on its own. Like any part of your product, success depends on how well you implement it and how quickly your team starts using it. Here's how to get the most out of whichever PostgreSQL reporting tool you choose. ### 1. Onboarding your team Invest time in proper onboarding. Developers, product managers, support teams, and anyone responsible for reporting should understand how the tool works from day one. A short, focused onboarding session prevents future bottlenecks and avoids misconfigurations that lead to misleading dashboards. ### 2. Start with a pilot Instead of rolling dashboards out to everyone at once, start with a small group. A controlled pilot helps you validate metrics, refine permissions, and confirm that the dashboards you built actually answer the questions teams have. It also reduces the chance of misaligned metric definitions or noisy, unused reports cluttering your workspace. ### 3. Collect feedback continuously Reporting is not a "set it and forget it" feature. Once dashboards go live, actively ask your users — internal or customer-facing — what works and what needs improvement. As your Postgres schema and product evolve, your reporting layer should evolve alongside it. ### 4. Monitor performance and usage Track how often dashboards are viewed, which queries run most frequently, and which reports are ignored. If certain dashboards never get opened, refine them. If certain queries slow down at scale, optimize them or add indexes to support the reporting workload. Understanding how your users interact with analytics helps you measure ROI and improve reporting over time. --- ## Conclusion The PostgreSQL reporting tool you choose becomes part of how your team understands the business. It shapes how founders track growth, how support teams identify issues, and how customers interpret their own data inside your product. It needs to connect reliably to your Postgres schema, serve the right people at the right level of access, and update without manual effort. If you want a reporting layer that works naturally with PostgreSQL without extra infrastructure or data pipelines, tools like Draxlr help you ship dashboards quickly while keeping your stack simple. Whether you need internal visibility, customer-facing analytics, or embedded dashboards inside your product, the right tool should help you move fast without compromising on clarity or reliability. If you'd like to explore a Postgres-native approach to dashboards and reporting, you can [try Draxlr](https://app.draxlr.com/register/) and see how it fits your workflow. --- ## FAQs ### 1. What is a PostgreSQL reporting tool? A PostgreSQL reporting tool connects directly to your Postgres database and lets you build dashboards, charts, and reports without writing SQL for every request. These tools help teams explore live data safely without setting up ETL pipelines or extra infrastructure. ### 2. Can I build customer-facing dashboards with PostgreSQL? Yes. Several reporting tools support embedding dashboards inside your product. Look for options with secure embed tokens, row-level filtering for multi-tenant applications, and styling controls so the dashboards match your product's UI. ### 3. Does PostgreSQL have built-in reporting? PostgreSQL has a command-line interface (psql) and supports SQL queries, but it does not include dashboards, charts, or shared reporting features. A dedicated reporting tool is required to turn Postgres data into visual insights for your team. ### 4. Is it safe to connect a reporting tool directly to PostgreSQL? Yes, as long as the tool respects Postgres permissions, uses secure connections (SSL/TLS), and supports user-level filtering when embedding dashboards. Most modern reporting tools offer these safeguards. Avoid tools that require exporting or duplicating your data outside your environment. ### 5. Can non-technical team members use PostgreSQL reporting tools? Yes. Choose a tool that offers visual query builders, saved reports, dashboard filters, and easy sharing so nontechnical users can work with Postgres data without writing SQL. AI text-to-SQL features make this even more accessible. ### 6. Do I need a data warehouse for reporting on PostgreSQL? No. Most reporting tools connect directly to PostgreSQL without requiring a separate warehouse. Unless you have extremely large analytical workloads or need cross-database joins across multiple systems, a direct Postgres connection is faster to set up, easier to maintain, and sufficient for most product and business reporting needs.

Best Explo (Omni Analytics) Alternatives in 2026

hello@draxlr.com (Draxlr) — Mon, 13 Apr 2026 00:00:00 GMT

If you've been using Explo, you've probably already received the news: **Explo was acquired by Omni in October 2025**, and the platform is being wound down over a 12-month transition period. All existing customers are expected to migrate to Omni before the Explo platform is sunset. That's a significant disruption, especially if your product is built around it. There are strong Explo (Omni Analytics) alternatives available right now. This guide covers the best ones. --- ## Why Teams Are Looking for Explo Alternatives in 2026 In October 2025, Omni acquired Explo and announced that the Explo platform would be sunset within 12 months. Every existing customer is being migrated to Omni, whether they chose to switch or not. That's the core reason teams are looking for Explo alternatives right now. Some don't want to be forced onto a platform they didn't evaluate. Others are using the moment to ask whether there's a better fit for where their product is headed. Either way, the window to make a considered switch is open now, before the migration pressure kicks in. --- ## What to Look for in an Explo (Omni Analytics) Alternative Before jumping into the Explo (Omni Analytics) Alternatives in 2026, here are the key criteria to evaluate: - **Quick setup** — you shouldn't need months of engineering work to ship dashboards - **Ease of use** — a BI tool is only valuable if your team actually uses it. Look for Explo alternatives that work for both technical and non-technical users. - **Data connectivity** — the tool needs to work with your existing databases and warehouses without requiring a separate pipeline. - **Visualization and reporting** — look for a range of chart types, customizable dashboards, and the ability to schedule and share reports. - **Dashboard embedding** — iframe or SDK-based embeds, white-labeling, and multi-tenant architecture - **Row-level security** — so each of your customers only sees their own data - **Self-hosting option** — important for teams with data residency or compliance requirements - **AI capabilities** — natural language querying and automated insights are increasingly standard. Check how mature these features are before treating them as a differentiator. - **Pricing transparency** — many BI platforms have complex pricing that scales poorly. Know what you're getting into before you commit. --- ## Quick Comparison: Best Explo (Omni Analytics) Alternatives in 2026

Tool	Ideal For	Pricing	Hidden Costs
Draxlr	Teams who want a self-serve AI-powered BI platform without the enterprise overhead	From $25/month	None
Power BI	Organizations already running on Microsoft infrastructure	From $14/user/month, Billed annually	Pro license required to share, Azure service costs, refresh limits push you to Premium
Sisense	Enterprises with complex requirements and dedicated BI engineers	From $399/month	Gantt charts locked to Custom plan, white-labeling starts at Grow, viewer seat caps force upgrades, VPN needed for on-prem on Scale
Luzmo	Mid to large teams that want fast deployment	From $495/month, Billed annually	Whitelabeling, embedding, and SSO locked to Premium at $1,995/mo and pricing scales with MAUs
Metabase	Developer-led teams who want to explore their data without buying a BI tool	Open Source	Self-hosting infrastructure costs on free plan. SSO, sandboxing, and audit logs require Pro at $500/mo, per-user pricing scales quickly

--- ## 1. Draxlr [Draxlr](https://www.draxlr.com) is a modern, AI-powered BI platform that lets any team connect to their SQL database, explore data, build dashboards, and share or embed insights without relying on a dedicated data team. It's built for self-service, so you can go from a database connection to a working dashboard in under an hour. ### Main Features - Visual Query Builder for non-technical users to filter, sort, and summarize data without SQL - AI Chat interface for text-to-SQL in plain English - Full SQL editor with autocomplete and query version control - Connects to SQL Databases like PostgreSQL, MySQL, BigQuery, Snowflake, Redshift, Supabase, ClickHouse, and more - Automated Slack and email alerts when metrics cross thresholds - Automatically send dashboards or reports on a recurring schedule via Slack and email - AI-assisted dashboarding and AI summary widget - Advanced analytics with drill-down capabilities, virtual columns - CSV, PDF, Excel, Image, and Google Sheets export - Self-hosted deployment option for data-sensitive teams - Embed dashboards into your product using React, Vue, or iframe with white-labeling and multi-tenant support - Google Sheets integration and dedicated API ### Pros and Cons

Pros	Cons
Fast to set up, no data warehouse needed	SQL-first, not suited for non-relational databases
Works for both technical and non-technical users	No heavy semantic modeling layer
Transparent, startup-friendly pricing
Self-hosting available
Responsive customer support

### Pricing Draxlr offers four plans: **Lite** at $25/month includes 1 database, unlimited external viewers, 15 AI credits. **Premium** at $75/month includes 2 databases, unlimited external viewers, 100 AI credits, white-labeled embed dashboard. **Power** at $125/month includes 6 databases, unlimited external viewers, 300 AI credits, white-labeled embed dashboard. **Custom** plan includes custom databases, unlimited external viewers, custom AI credits, white-labeled embed dashboard, dedicated server. There is also a **Self Host** option at $500/month which includes Docker containers, custom AI models, white label, and no Draxlr logo. A 7-day free trial is available at [draxlr.com](https://www.draxlr.com). Connect your Database --- ## 2. Power BI Power BI is Microsoft's flagship BI platform and one of the most widely used analytics tools in the world. It integrates natively with Excel, Azure, Teams, and the broader Microsoft ecosystem, making it a natural fit for organizations already running on Microsoft infrastructure. ### Main Features - Drag-and-drop dashboard builder with a wide visualization library - Power Query for data transformation and DAX for advanced calculations - Native integration with Excel, Azure, Teams, and SharePoint - AI-powered natural language Q&A - Row-level security and role-based access controls - Both desktop (free) and cloud-based deployment ### Pros and Cons

Pros	Cons
Deep Microsoft ecosystem integration	DAX and Power Query have a steep learning curve
Free desktop version available	Pricing escalates quickly for larger teams
Large community and extensive documentation	Less compelling outside the Microsoft ecosystem
Strong visualization library	Cloud version requires Power BI Pro or Premium

### Pricing Power BI Desktop is free. Power BI Pro starts at $14/user/month, billed annually. Power BI Premium has capacity-based pricing starting at $24/user/month, billed annually. Worth knowing before you commit is that the free Desktop version is local only, so the moment you need to share or collaborate, everyone needs a Pro license. Data refreshes are capped at 8 per day on Pro, pushing teams toward Premium. And if you're using Azure services alongside Power BI, those come with their own billing that can quietly add up. What looks like $14/user/month, billed annually at the start often lands significantly higher in practice. --- ## 3. Sisense Sisense is a mature enterprise BI platform known for its in-memory processing engine, extensive SDK support, and strong AI capabilities. It's built for large organizations that need highly customized analytics experiences backed by enterprise-grade infrastructure. ### Main Features - In-memory processing engine (ElastiCube) for high-performance analytics - Extensive SDK support for custom analytics development - AI-powered insights and natural language querying - Cloud and on-prem deployment options - Connects to a broad range of data sources - Role-based access controls and data governance ### Pros and Cons

Pros	Cons
Deep customization via extensive SDKs	Steep learning curve and complex implementation
Strong AI and natural language querying	High cost, requires a sales conversation
Scales for large enterprise workloads	Not suited for teams without dedicated BI engineers
Cloud and on-prem flexibility	Performance issues with ElastiCube reported by some users

### Pricing Sisense offers three plans: **Launch** at $399/month includes 20GB storage, 2 designer seats, 50 viewer seats, basic reporting, row-level data security, and community support. **Grow** at $1,299/month includes 60GB storage, 5 designer seats, 100 viewer seats, white-labeling, multi-environment support, performance scale-ups, staging environments, and multi-region support (coming soon). **Scale** is custom pricing and includes flexible deployment options, advanced data connectivity, advanced SSO, auto-scaling, multi-tenant support, HIPAA-ready infrastructure, 99.99% SLA, premium support, and a dedicated SAM. Worth noting: Gantt charts are only available on the Scale plan. White-labeling starts at Grow. Viewer seats are capped on Launch and Grow — if your team grows beyond those limits, you're pushed to a higher tier or custom pricing. On-prem data connectivity is SSH on Launch and Grow but requires VPN and more on Scale, which adds infrastructure overhead. --- ## 4. Luzmo Luzmo is a BI platform designed for fast deployment and clean out-of-the-box design. It publishes its pricing upfront, which is a genuine advantage for teams that want to avoid lengthy enterprise sales cycles. ### Main Features - Drag-and-drop dashboard builder with a polished default UI - Connects to popular databases and data sources - White-labeling and branding customization - Published pricing tiers with no sales cycle required - Scheduled reports and data alerts - Multi-tenant data support ### Pros and Cons

Pros	Cons
Fast to deploy with a clean UI	More limited SQL-native querying
Transparent, published pricing	Smaller ecosystem and community
No lengthy sales cycle	Fewer integrations than established players

### Pricing **Starter** at $495/month for teams getting started with customer-facing analytics. Includes drag and drop dashboard editor, unlimited dashboards, responsive dashboards, custom dashboard themes, multilingual/currency/timezone support, PDF/image/table exports, and scheduled exports. **Premium** at $1,995/month for growing platforms delivering advanced analytics to customers. Adds alerting, AI-assisted dashboarding, AI summary widget, advanced analytics, whitelabeling, dashboard and chart embedding, custom plug-in API, secure multi-tenant authentication, custom events, and single sign-on. **Enterprise** is custom pricing for businesses that need custom deployment, security, and compliance capabilities. Includes dedicated infrastructure, custom SLAs, flexible hosting options, and support for large-scale deployments. Pricing scales with Monthly Active Users (MAUs), so costs grow as your customer base grows. Worth factoring in before committing. Worth noting: whitelabeling, embedding, SSO, and advanced analytics are all locked to the Premium plan at $1,995/month. The Starter plan is more limited than the price tag suggests. --- ## 5. Metabase Metabase is one of the most popular open-source BI tools available. It's designed to be approachable for non-technical users while still giving data teams enough flexibility for more complex reporting. The 2025 cloud version added AI-assisted query suggestions and improved data sandboxing. ### Main Features - Intuitive question builder for non-technical users - Full SQL editor for advanced queries - AI-assisted query suggestions - Scheduled reports and dashboard subscriptions - Self-hosted (open source) and cloud-hosted options - SSO, advanced permissions, and white-labeling on paid plans ### Pros and Cons

Pros	Cons
Fast to deploy with a clean UI	Limited for complex data modeling needs
Fast setup for basic dashboard needs	White-labeling requires the commercial plan
Less suited to large-scale enterprise use
Advanced governance features require paid tier
3-day support even on paid plans

### Pricing Metabase offers four plans: **Open Source** is free and self-hosted. Includes core analytics features, unlimited questions and dashboards, and basic permissions. **Starter** starts at $100/month + $6/month/user for up to 5 users. **Pro** starts at $575/month + $12/month/user for up to 10 users. This plan includes Embedding. **Enterprise** is custom pricing. Includes everything in Pro plus advanced SSO, dedicated customer success, SLA support, and SAML. Add-ons available separately include additional users, Metabase AI, and storage. A 14-day free trial is available on paid plans. Worth noting: the free Open Source plan is self-hosted only, meaning your team handles deployment and maintenance. SSO, advanced permissions, sandboxing, and audit logs all require Pro or above. Costs scale per user, so larger teams can see bills climb quickly. --- ## Conclusion Losing Explo to an acquisition is disruptive, but it's also a chance to land on a better tool. The five Explo alternatives covered here each serve a different kind of team, from lean startups to large enterprises and each has real trade-offs worth understanding before you commit. Draxlr covers more ground than Explo did, at a fraction of the cost of the enterprise tools on this list. If you don't have months to spend on implementation or a dedicated BI team to manage it, that matters. **[Get started with Draxlr for free](https://app.draxlr.com/)** --- ## FAQs ### 1. Is Explo shutting down? Explo was acquired by Omni in October 2025 and will be sunset over a 12-month transition period. All Explo customers are expected to migrate to Omni's platform. The Explo platform will remain operational during this window, but it is no longer a viable long-term solution. ### 2. What is the best Explo alternative in 2026? For most teams, Draxlr is the best replacement. It's a full BI platform that's fast to set up, works for technical and non-technical users alike, and is priced transparently. ### 3. How do I migrate from Explo to another BI tool? Most BI tools connect to the same databases Explo was querying, so your data infrastructure usually stays the same. The main work is rebuilding your dashboards in the new tool. If you migrate to Draxlr, the team will do that for you for a one-time fee. ### 4. Which Explo alternative supports self-hosting? Draxlr offers a self-hosted option at $500/month that includes Docker containers, custom AI models, white-labeling, and no Draxlr branding. Metabase also has a free self-hosted open-source version. ### 5. Which Explo alternative is best for embedded analytics? Draxlr. You can embed dashboards into your product using React, Vue SDKs, or iframe, with unlimited external users, full white-labeling, multi-tenant support, and row-level security. It's available on all paid plans starting at $75/month, with no separate embedding fee.

Best AI-Powered BI Tools in 2026

hello@draxlr.com (Draxlr) — Wed, 08 Apr 2026 00:00:00 GMT

**AI-powered BI tools** in 2026 are reshaping how businesses interact with their data. Where traditional business intelligence once demanded dedicated analysts, complex queries, and days of dashboard-building, today's AI-powered BI tools put instant, actionable insights in the hands of every team member with no technical background required. I spent time testing multiple AI-powered BI tools to see which ones actually help teams move faster with data. I focused on tools that connect directly to SQL databases, generate accurate queries, and make it easy to build dashboards without technical expertise. To make this useful, I tested each tool on common analytics scenarios and evaluated AI query accuracy, dashboard creation, and ease of use for non-technical users. If you want faster insights without relying on SQL or data teams, these AI-powered BI tools can save hours every week. Below, I’ll break down the best tools, what they do well, and how to choose the right one for your team. --- ## What Are AI-Powered BI Tools? AI-powered BI tools are business intelligence platforms that use artificial intelligence to simplify data analysis. Unlike traditional BI tools that require analysts to build dashboards manually and write complex queries, modern AI-powered BI tools let any team member simply ask a question in plain English and get an instant, visual answer. Think of it this way: instead of waiting days for a report, a marketing manager can type _"Which campaigns drove the most conversions last quarter?"_ and get a live chart in seconds. A CFO can ask _"Where are we trending against budget this quarter?"_ and get an immediate, drill-down-ready answer. Instead of manually writing queries or building reports, you can: - Ask questions in natural language - Automatically generate SQL queries - Build dashboards instantly - Get insights and summaries from your data --- ## Why AI-Powered BI Tools Matter in 2026 Data volumes are exploding. Every business, whether a 10-person startup or a 10,000-employee enterprise, generates more data today than it can manually analyze. Valuable insights get buried, decisions get delayed, and opportunities go missed. Here's why AI-powered BI tools have become essential: - **Speed** Companies using the right BI tools report making decisions up to **5x faster** than those relying on manual analysis. - **Accessibility** Non-technical users can now explore data independently without waiting on analysts. - **Accuracy** AI reduces human error in data aggregation and reporting. - **Predictive power** Machine learning models forecast demand, churn, revenue, and more in real time. - **Cost efficiency** Automating reporting and insight generation reduces the burden on expensive data teams. The market reflects this urgency. In 2026, AI has moved from a nice-to-have feature in BI platforms to a core requirement. Natural language querying, auto-generated insights, anomaly detection, and AI-driven forecasting are now standard expectations rather than differentiators. --- ## Key Features to Look For in AI-Powered BI Tools Before comparing platforms, here are the critical capabilities every AI-powered BI tool should offer: ### 1. Natural Language Querying (NLQ) The ability to ask questions in plain English and receive immediate, accurate data responses. This is the cornerstone of a truly AI-powered BI tool. Look for NLQ that understands business context, not just keywords. ### 2. Semantic Layer / Data Governance A shared layer where your team defines metrics, KPIs, and business terms consistently. This ensures everyone is working from the same "source of truth," not conflicting numbers. ### 3. Self-Service Analytics The ability for business users, not just data engineers, to explore data, build dashboards, and run analyses without writing SQL or code. ### 4. Integration & Connectivity Connections to your existing SQL databases including cloud warehouses like [Snowflake](/databases/snowflake/), [BigQuery](/databases/bigquery/), and [Redshift](/databases/redshift/), as well as [PostgreSQL](/databases/postgres/), [MySQL](/databases/mysql/), [Supabase](/blogs/how-to-build-dashboards-from-supabase-data/), and more. ### 5. AI Governance & Explainability Especially important for regulated industries. Can you audit how the AI reached a conclusion? Are the insights transparent and traceable? ### 6. Embedded Analytics For SaaS companies or product teams, the ability to embed AI-powered analytics directly inside your own application or product. --- ## Quick Glance at AI-Powered BI Tools in 2026

Tool	Best for	Pricing(starts at)
Draxlr	Startups and mid-size teams looking for AI-powered, self-serve BI tool on a budget	$25/month
Microsoft Power BI	Microsoft-centric organizations that need enterprise-scale reporting and AI-powered analytics	$20/user/month for full Copilot AI
ThoughtSpot	Governed associative analytics	$50/user/month (min. 25 users, billed annually)
Sigma Computing	Data teams that need transparent, auditable AI analytics directly inside their cloud warehouse	Custom/enterprise
Metabase	Startups and technical teams looking for an open-source BI tool	Cloud from $100/month; Metabot AI add-on +$100/month

--- ## 1. Draxlr **Best AI-powered BI tool for:** Teams that want an AI-powered, self-serve BI tool that's fast to set up and easy for everyone to use [Draxlr](/) is built for getting your team from raw SQL data to actionable dashboards as fast as possible, without needing a data engineer to make it happen. Built from the ground up for speed, simplicity, and genuine self-service, Draxlr lets any team member technical or not connect to their SQL database and start getting answers in minutes. No data engineering background required. No waiting on analysts. No steep learning curve. The core of Draxlr's AI experience is its **Text-to-SQL engine**. You type a question in plain English and Draxlr instantly converts it into an executable SQL query, runs it, and returns a chart or visualization. Unlike most NLQ tools, Draxlr shows you the generated SQL so your team can verify and modify it. That transparency is rare, and it matters. ### Who is Draxlr built for Draxlr is a great fit across a surprisingly wide range of teams: - **Non-technical business users** who need answers from data without learning SQL - **Product and engineering teams** who want to give internal stakeholders self-serve access to production data - **SaaS companies** that need to embed analytics directly inside their own product under their own brand - **Data-conscious organizations** that need full control over where their data lives, thanks to Draxlr's self-hosted deployment option Connect your Database ### Draxlr's Standout Capabilities Draxlr isn't trying to do everything. It's focused on doing the things that matter most to data-driven teams really well. Here's where it genuinely pulls ahead. **Database connectivity that covers your stack** Most BI tools support a handful of popular databases and call it a day. Draxlr takes a much broader approach to connecting to SQL databases and [visualizing SQL data](/blogs/sql-data-visualization/). It works natively with PostgreSQL, MySQL, MariaDB, Microsoft SQL Server, Snowflake, BigQuery, Amazon Redshift, Supabase, ClickHouse, CockroachDB, YugabyteDB, and PlanetScale. Whether you're running a startup on Supabase or an enterprise data warehouse on Snowflake, Draxlr connects directly to your existing stack with no data duplication and no ETL overhead. **Self-hosted for teams that take data security seriously** Data security isn't an afterthought for every team and for some, it's the deciding factor. Draxlr offers a self-hosted deployment option that lets your team run the entire platform on your own infrastructure. Your data never leaves your servers, which matters a lot if you're in a regulated industry like healthcare or finance, or if your company simply has a strict policy about where data is stored and processed. **Embedded analytics and white-labeling for SaaS teams** If you're building a product and want to offer your customers analytics without building a BI platform from scratch, Draxlr's embedded analytics capabilities are genuinely impressive. Using React and Vue SDKs, you can embed dashboards, charts directly into your application under your own brand, with your own colors and domain. Each customer gets isolated data and permissions, all managed from a single admin interface. **Smart alerts that keep your team proactive** Draxlr's data alert system monitors your dashboards in real time and automatically sends Email or Slack notifications when a metric crosses a threshold or a significant data change is detected. Instead of your team checking dashboards manually, Draxlr comes to them, keeping everyone informed without adding to anyone's workload. **Pricing that scales without punishing growth** Draxlr's pricing is designed to grow with your team rather than punish you for it. Small teams can get started on the Lite plan at $25/month, and as your analytics needs mature, the Premium and Power plans unlock more databases, more users, and a higher AI credits ceiling. One thing worth calling out is that all plans include **unlimited external viewers**. If you need to share dashboards with clients, stakeholders, or partners outside your core team, you can do that without worrying about extra costs. Dashboard embedding, which is critical for SaaS teams, is available from the Premium plan upward. ### Key strengths - Text-to-SQL AI converts plain English questions into instant, explainable SQL queries and charts - Connects to SQL databases including PostgreSQL, MySQL, Snowflake, BigQuery, Redshift, Supabase, and more - Self-hosted deployment with full feature access and no cloud dependency required - White-label and embedded analytics with React and Vue SDKs for SaaS products - Real-time data alerts via email and Slack - Unlimited user invites on premium plans with no per-seat cost as your team scales - Intuitive query builder accessible to both technical and non-technical users - Clean, fast interface with minimal onboarding time ### Limitations - Optimized for SQL databases ### Pricing Starts at $25/month --- ## 2. Microsoft Power BI **Best AI-powered BI tool for:** Mid-to-large organizations in the Microsoft ecosystem Power BI remains the dominant force in business intelligence for good reason. It integrates seamlessly with the Microsoft stack including Excel, Azure, Teams, and Dynamics, making adoption frictionless for organizations already using Microsoft tools. Its AI features include Copilot (natural language report generation), smart narratives, anomaly detection, and automated machine learning. ### Key strengths - Deep Microsoft 365 integration - Strong governance and security features - Accessible for non-technical users with drag-and-drop dashboards - Copilot AI generates reports, DAX formulas, and narrative summaries in plain English ### Limitations - Full Copilot AI requires Power BI Premium Per User ($20/user/month) — basic Pro ($14/user/month) only includes limited AI features like Q&A and Smart Narratives - Advanced analysis still requires learning DAX (Data Analysis Expressions) - Less flexible for organizations outside the Microsoft ecosystem ### Pricing Full AI features (Copilot) require Premium Per User at $20/user/month --- ## 3. ThoughtSpot **Best AI-powered BI tool for:** Organizations prioritizing conversational, AI-first analytics ThoughtSpot was built AI-first, and it shows. Powered by Spotter AI (now in its third generation as of 2026), ThoughtSpot lets users search and explore data the way they'd use a search engine with no dashboards required. Its SpotIQ feature continuously analyzes data and proactively sends anomaly alerts. It connects live to cloud warehouses like Snowflake, BigQuery, and Redshift without requiring data extracts. ### Key strengths - Industry-leading natural language querying - Proactive anomaly detection and alerts - Answer Explainer feature for full transparency in calculations - Zero-copy architecture that runs queries live on your warehouse ### Limitations - Spotter AI is capped at 25 queries/user/month on the Pro plan, so heavy users may hit limits quickly - Less suited for pixel-perfect, formatted reporting ### Pricing From $50/user/month (min. 25 users, billed annually) which includes Spotter AI Agent and AI-infused dashboards --- ## 4. Sigma Computing **Best AI-powered BI tool for:** Teams who want AI embedded directly in their cloud data warehouse Sigma Computing takes a refreshingly different approach to AI-powered BI. Rather than treating AI as a front-end assistant layered on top of data, it embeds AI directly into the analytics layer using LLMs via SQL functions and agentic workflows that are visible, editable, and explainable. Its "Ask Sigma" feature acts like an AI data analyst, building entire analyses step by step and showing its reasoning. ### Key strengths - AI that's transparent and auditable rather than a black box - Direct warehouse integration (Snowflake, Databricks, BigQuery, Redshift) - Agentic AI capabilities with visible, editable decision logic - Spreadsheet-like interface lowers the adoption barrier ### Limitations - Requires an existing cloud data warehouse to get the most value - Newer platform — still maturing in some feature areas --- ## 5. Metabase **Best AI-powered BI tool for:** Startups and technical teams that want open-source Metabase is one of the most widely adopted open-source BI tools in the world. It has now grown from a simple dashboard builder into a capable AI-powered analytics platform. Its Metabot AI assistant lets users ask questions in plain English, automatically converts them into SQL, runs the query, and returns a chart and narrative summary. ### Key strengths - Metabot AI: natural language to SQL, chart generation, and query debugging - Data Studio for semantic layer, metric definitions, and lineage tracking - Embeddable analytics with components for charts, dashboards, and AI chat - Connects to your database and builds dashboards fast - Active open-source community ### Limitations - Metabot AI is a paid add-on ($100/month for 500 requests) on top of any Cloud plan and not included in base pricing, and not available on the free self-hosted version - Less powerful for advanced enterprise reporting compared to Power BI or ThoughtSpot ### Pricing Cloud plans from $100/month with Metabot AI add-on from $100/month extra --- ## How to Choose the Right AI-Powered BI Tool With five strong options, selection comes down to your specific context. Work through these questions: ### 1. What's your existing tech stack? If you're deep in Microsoft — Azure, Teams, Excel, Dynamics — Power BI is the natural fit. If your team lives in a cloud data warehouse like Snowflake or BigQuery, Draxlr will give you the most power. ### 2. Who are your primary users? For non-technical business users who need instant answers, Draxlr's conversational AI is hard to beat. For developer or data teams who want SQL control with an easy interface, Draxlr or Metabase are excellent fits. For organizations that need enterprise-scale analytics, Power BI or Sigma are the stronger choices. ### 3. What's your budget reality? Draxlr is the most budget-friendly option which starts at $25/month. Rest of the BI tools are enterprise-priced. Always factor in implementation, training, and ongoing maintenance costs. ### 4. Do you need embedded analytics? If you're building analytics into a product for your customers, Draxlr (white-label, React/Vue SDKs), Metabase (embeddable components), and Sigma are the strongest choices in this list. ### 5. How mature is your data infrastructure? Sigma and ThoughtSpot assume you already have a cloud data warehouse. If your data lives in a SQL database and you want fast setup, Draxlr or Metabase are the most pragmatic starting points. --- ## Conclusion **AI-powered BI tools** have fundamentally changed what's possible in business intelligence. What once required a team of analysts, weeks of dashboard-building, and specialized technical skills can now happen in seconds with a plain English question. The right tool depends on your team size, technical maturity, existing infrastructure, and budget. Draxlr is the fastest path from SQL database to live dashboard. Power BI leads for Microsoft-centric organizations. ThoughtSpot leads in conversational, search-style analytics. Metabase works well for technical teams that want an open-source base with AI capabilities as a paid add-on. And Sigma Computing brings the most transparency and control for warehouse-native AI analytics. What's non-negotiable in 2026 is **data quality comes first, governance matters more than ever, and adoption is the real measure of success.** Pick a tool your whole team will actually use and invest in the data foundation that makes its AI insights trustworthy. --- ## FAQs ### 1. What is an AI-powered BI tool? An AI-powered BI (Business Intelligence) tool is a data analytics platform that uses artificial intelligence including machine learning, NLP, and generative AI to automate the discovery, analysis, and communication of business insights from your data. ### 2. How are AI-powered BI tools different from traditional BI? Traditional BI tools require analysts to build dashboards and write queries manually. AI-powered BI tools automate these tasks, letting any user ask questions in natural language and receive instant, accurate insights without needing to code or write SQL. ### 3. Which AI-powered BI tool is best for small businesses? Draxlr is the strongest pick for small businesses. It starts at $25/month, connects directly to your SQL database, and gets your team from zero to live dashboards in minutes. The AI query feature means non-technical team members can get answers from data without relying on a developer, which is exactly what small teams need. ### 4. Can AI-powered BI tools replace data analysts? No, and that's not the goal. AI-powered BI tools amplify what data teams can do, freeing analysts from repetitive reporting tasks so they can focus on deeper, more strategic work. They also empower non-technical users to self-serve on routine data questions. ### 5. What's the most important feature to look for in an AI-powered BI tool? Natural language querying (NLQ) quality is the most impactful feature for most teams. It determines whether non-technical users can actually use the platform independently. ### 6. How much do AI-powered BI tools cost? It depends on what you actually need from the AI. Some tools bundle AI into their base plan, others charge it as a separate add-on. Draxlr starts at $25/month with AI credits included. Always factor in implementation, training, and ongoing maintenance when calculating total cost, not just the license price.

SQL Dashboard Tools for Embedding in 2026

hello@draxlr.com (Draxlr) — Sun, 29 Mar 2026 00:00:00 GMT

Learn about some of the best **SQL dashboard tools for embedding** and discover how modern [embedded analytics tools](/embedded-analytics-tool/) help teams deliver in-product analytics without building everything from scratch. Transitioning from raw data to in-product analytics requires the right infrastructure, the right tools, and a clear implementation strategy. On this journey, a SQL dashboard tool is a must-have in your product stack. A powerful SQL dashboard tool helps you connect to your SQL database, build visualizations, and embed live dashboards directly inside your application by giving users instant access to their data without leaving your product. In this blog post, we will give you an in-depth understanding of SQL dashboard tools for embedding, their key features and benefits, the best options to consider, and how to choose the right one for your product. Let's explore the basics. --- ## What are SQL Dashboard Tools for Embedding? **SQL dashboard tools** for embedding are all-in-one solutions to connect directly to your database, build data visualizations, manage access permissions, and embed live dashboards into your application to deliver analytics at pace and scale. Unlike teams that build analytics from scratch, product teams often struggle with fragmented data, limited engineering bandwidth, and the complexity of building secure, performant dashboards inside an existing application. These challenges make it difficult to ship analytics features quickly without turning them into a months-long infrastructure project. Here's where a dedicated SQL dashboard tool comes into play. SQL dashboard tools for embedding are built to scale seamlessly as your product grows, accommodating increasing data volumes, expanding user bases, and evolving reporting needs. They help you: - Query and visualize data directly from your database - Embed dashboards seamlessly into your application - Manage user-level access and permissions - Share insights without building custom infrastructure - Automate reporting and alerts Ultimately, it helps product teams deliver meaningful data experiences to their users, **reduce time-to-insight**, and ship [embedded analytics](/embedded-analytics-tool/) without building everything from the ground up. --- ## What are the benefits of SQL Dashboard Tools for Embedding? Teams often start by building dashboards internally or sharing raw data through spreadsheets. Some even rely on separate tools for queries, visualizations, and reporting. But as your product grows, this approach becomes hard to manage. With limited engineering bandwidth and increasing demand for in-product analytics, teams need solutions that are easy to implement, scalable, and built for real user-facing experiences. What if you could deliver dashboards directly inside your product without building everything from scratch? Here's how embedding SQL dashboard tools like **Draxlr** benefits your product and your users: ### 1. Data Lives Inside Your Product Users access their data without leaving your application. No redirects, no context switching. ### 2. Faster to Ship You skip building a reporting module from scratch. SQL Dashboard Tools get you to a working embedded dashboard without weeks of engineering work. ### 3. Always Live Data Dashboards connect directly to your database. What users see is current, not a snapshot from last night's export. ### 4. Looks Like Your Product You apply your own branding. The dashboard feels like a native part of your app, not a third-party tool dropped in. ### 5. Users Only See Their Data Access controls ensure each user sees only what belongs to them. This matters especially in multi-tenant products. ### 6. Less Work for Your Engineering Team New charts and reports do not require code changes every time. Anyone on the team can update dashboards directly. ### 7. Grows With Your User Base Adding more users does not mean reworking your setup. The same embedded dashboard configuration works whether you have 10 customers or 10,000. --- ## What Are the Essential Features of SQL Dashboard Tools for Embedding? When choosing a SQL dashboard tool for embedding, it is important to find one that fits your current technical setup and grows with your product. The right tool should make it easy to deliver data to your users without adding complexity to your codebase. Let's check the key features ### 1. Direct SQL connectivity The foundation of any good SQL dashboard tool is its ability to connect directly to your database. This allows you to: - Query live data - Avoid data duplication - Keep dashboards always up to date Look for tools that support common databases like Postgres, MySQL, and SQL Server. ### 2. Flexible embedding options Embedding is the core requirement. The tool should support: - Secure embedding - Private or token-based embeds - Customizable dashboard views This ensures you can integrate dashboards smoothly into your product. ### 3. Row-level access control Not all users should see the same data. A strong SQL dashboard tool for embedding should provide: - User-based data filtering - Role-based permissions - Secure multi-tenant support This is critical for SaaS products serving multiple customers. ### 4. Performance and scalability Embedded dashboards must load fast inside your product. The tool should handle: - Large datasets - Concurrent users - Query optimization Performance directly impacts user experience. ### 5. Customization and white-labeling For embedded use cases, your dashboards should match your product. Important features: - Custom branding - Layout control - UI customization This ensures a consistent user experience. ### 6. AI-powered querying Modern SQL dashboard tools now include AI to simplify data exploration. Features may include: - Text-to-SQL generation - Query explanations - Auto-generated insights This reduces dependency on engineers for writing queries. ### 7. Easy dashboard creation You should not need to build dashboards from scratch every time. Look for tools that offer: - Visual query builders, AI-powered querying - Pre-built charts and widgets - Reusable queries This helps both technical and non-technical users create dashboards quickly. ### 8. Sharing and external access Beyond embedding, you may need to share dashboards externally. Look for: - Public or private links - Controlled access sharing - Export options This is useful for stakeholders and reporting. ### 9. Alerts and automation Instead of checking dashboards manually, users should be notified automatically. Key capabilities include: - Email alerts - Slack notifications - Scheduled reports This helps teams act on insights in real time. ### 10. Developer-friendly integrations Even though these are no-code tools, developer flexibility is still important. Look for: - APIs for data access - Embed configuration options - Easy integration with your stack This helps you extend functionality as your product evolves. Choosing the right SQL dashboard tool with these features ensures you can deliver fast, secure, and scalable embedded analytics without building everything from scratch. --- ## Quick Comparison: SQL Dashboard Tools for Embedding in 2026

Tool	Best for	Pricing starts at
Draxlr	Teams that want fast SQL-based embedded dashboards with minimal setup	$75/month
Looker	Teams that want a large-scale centralized semantic layer with deep Google Cloud integration	$180,000+ annual TCO (source: reddit)
Domo	Teams that want a full-stack BI platform with enterprise-grade analytics	No public pricing, contact sales
Luzmo	Teams that want developer-first lightweight embedded analytics components	$495/month billed annually
Metabase	Teams that want open-source BI with customizable embedding and self-hosted control	$575/month + $12/month/user

For a full pricing comparison across multiple embedded analytics tools, see our [embedded analytics pricing](/embedded-analytics-pricing/) guide. --- ## 1. Draxlr Draxlr is a SQL-first dashboard tool built for teams that want embedded analytics without the complexity. It connects directly to your database, meaning your users always see live, accurate data without any intermediate data duplication or syncing. What sets Draxlr apart is how approachable it is. You can write raw SQL for full control or use the visual query builder or AI-powered querying if you prefer a no-code approach. All options live in the same interface, so different members of your team can work in the way that suits them best. For embedding, Draxlr gives you multiple integration options including iframe embed support, React, and Vue SDKs, so you can embed dashboards directly into your existing stack without workarounds. Each embed comes with a configuration object that lets you control headers, export options, background colors, filters, [drill-downs](/features/drill-down/), and more. White-labeling ensures the dashboard looks and feels like a native part of your product. Access controls let you manage what each user sees, which is essential for multi-tenant products. Draxlr is also competitively priced compared to enterprise BI tools, making it a practical choice for product teams that need embedded analytics up and running without a long implementation cycle. **Best SQL Dashboard Tools for Embedding** for teams that want fast SQL-based embedded dashboards with minimal setup ### Key Features - **Secure embedding and access control:** Draxlr secures embeds at every layer. Row-level access controls filter data at query time so users only see their own data. JWT and token-based auth prevent unauthorized loading, and domain allowlists guard against unauthorized embedded usage. Fine-grained roles and per-screen permissions mean you can tailor access by user role. SSO with OAuth/SAML/OpenID ensures consistent identity flows across your app. - **Deployment model and architecture:** Choose hosted SaaS for speed or self-hosted Docker for complete infrastructure control. Self-hosted customers get the same security and embed capabilities, plus audit logging, white-labeling, and optional custom AI model support. - **Embedding flexibility:** Embed using iframe or SDK (React/Vue/plain JS) across frontend frameworks without compromise. Runtime filters and context props make each embed uniquely relevant to the viewer. Full UI control allows hiding headers, chrome, and branding so dashboards look native. - **Embed Preview Before You Ship:** Draxlr gives you a live embed preview in both desktop and mobile views before you publish anything. You see exactly what your users will see, so there are no surprises after deployment. - **Data stack integration:** Connect directly to SQL sources (Postgres, MySQL, SQL Server, major warehouses) and run queries live for always-current insights. Built-in visual query builder, AI-powered querying and SQL editor share a consistent query model, minimizing separate ETL or semantic layer needs. - **Query UI and experience:** Support technical users with SQL and non-technical users with drag-and-drop dashboards and AI. Visualizations include charts, tables, KPIs, pivot tables, drilldowns, tooltips, and filters to keep embedded analytics actionable. - **Exports on Your Terms:** You control exactly what viewers can download from the embedded dashboard. Whether you want to allow CSV exports, PDF downloads, or restrict exports entirely, Draxlr gives you that control without any code changes. - **Performance and scale:** Leverage caching and query optimization for faster loads. Paginate/virtualize large datasets. Configure data refresh windows and incremental updates to manage compute costs. - **Branding and white-labeling:** Full visual control like theme colors, layout, and UI elements. Embed as part of your product without third-party chrome. **Monitoring and observability** Embed metrics: load counts, error rates, latency. Query health checks and alerts (Slack/email). Audit logs capture user activity for security and compliance. ### Pricing Draxlr offers a 7-day trial to get started. Paid plans are available at affordable price points compared to enterprise BI tools, making it accessible for teams of all sizes. ## 2. Looker Looker is an enterprise-grade business intelligence platform now part of Google Cloud. It is built around a proprietary data modeling layer called LookML, which allows teams to define reusable metrics and relationships across their data. This makes it particularly powerful for organizations with complex, interconnected datasets that require consistent definitions across teams. For embedding, Looker offers the Looker Embedded SDK, which provides deep integration options including single sign-on, custom themes, and event-driven interactions between the embedded dashboard and the host application. It supports both full dashboard embeds and individual chart embeds depending on your use case. The tradeoff is complexity. Setting up Looker requires a meaningful investment of time, and getting the most out of it typically requires a dedicated data engineering or analytics engineering team. Pricing is also at the enterprise tier, which makes it less accessible for smaller teams or products in early stages. **Best SQL Dashboard Tools for Embedding** for large organizations with complex data requirements, dedicated data teams, and the budget to match. ### Key Features - LookML data modeling layer for consistent, reusable metrics - Looker Embedded SDK for deep and flexible embedding integration - Single sign-on support for seamless user authentication - Custom themes and branding for white-labeled deployments - Event-driven interactions between embedded dashboards and host applications - Enterprise-grade access controls and governance features ### Pricing Looker is priced at the enterprise level with custom quotes based on usage and user count. It does not publish standard pricing publicly. It is generally one of the more expensive options in the SQL dashboard space and is best suited for organizations with a significant analytics budget. ## 3. Domo Domo is a cloud-based business intelligence platform that covers data connectors, transformation tools, visualization, and collaboration all in one place. Its embedding offering, called Domo Everywhere, is designed specifically to help businesses share dashboards and analytics externally with their own customers and partners in a white-labeled environment. Domo Everywhere supports white-labeling, row-level security, and a variety of access control options. It also offers a programmatic filters feature that lets you control what data is surfaced to each user at the time of embed. The visualization library is extensive, covering everything from standard charts to custom-built cards. Where Domo can be limiting is cost. It is priced for enterprise customers, and the total cost of ownership can be significant when you factor in the number of users and the features you need. It is also a broader platform than most teams need if their primary goal is simply embedding dashboards. **Best SQL Dashboard Tools for Embedding** for enterprises that need a full-featured BI platform with strong embedding capabilities and have the resources to invest in a large-scale deployment. ### Key Features - Domo Everywhere for white-labeled, customer-facing dashboard embedding - Programmatic filters to control data visibility at the time of embed - Row-level security and granular access control options - Extensive visualization library including custom-built chart types - Built-in data connectors for hundreds of sources - Collaboration tools for teams working across the same data ### Pricing Domo uses custom enterprise pricing based on the number of users and features required. It does not publish standard pricing publicly. The total cost of ownership can be significant, making it better suited for larger organizations with dedicated budgets for business intelligence. ## 4. Luzmo Luzmo is purpose-built for embedded analytics, which means every feature it offers is designed with the embedding use case in mind. Unlike general-purpose BI tools that treat embedding as a secondary feature, Luzmo puts it front and center. It is particularly well-suited for SaaS companies that want to offer customer-facing analytics as part of their product. It offers a drag-and-drop dashboard builder that makes it easy to create polished, interactive dashboards without writing code. Its SDK gives developers fine-grained control over how dashboards are embedded and how they interact with the rest of the application. White-labeling is well-supported, and the output looks professional out of the box. Luzmo also handles multi-tenancy cleanly, with built-in support for user-level data access. This means each of your customers sees only their own data, which is a fundamental requirement for any customer-facing analytics feature. **Best SQL Dashboard Tools for Embedding** for SaaS products that need a dedicated embedded analytics solution with a polished, customer-facing UI. ### Key Features - Drag-and-drop dashboard builder requiring no coding - Robust SDK for deep embedding control and customization - Strong white-labeling support for a fully branded experience - Built-in multi-tenancy with user-level data access controls - Interactive and visually polished chart options out of the box - API-first architecture for flexible integration into any stack ### Pricing Luzmo operates on a subscription model with plans based on the number of embedded dashboards and end users. Pricing is on the higher end, reflecting its focus on enterprise SaaS use cases. It starts at $2,000/month billed annually. --- ## 5. Metabase Metabase is one of the most widely adopted SQL dashboard tools available, largely because of its open-source roots and strong community. It supports both a visual query builder and native SQL, making it accessible to a wide range of users from analysts to engineers. It works well as an embedded solution when paired with its paid plans. For embedding, Metabase uses signed URLs and iframes. It allows you to embed individual charts or full dashboards into your application, with parameters to control what data is displayed. The setup is well-documented and relatively straightforward for developers familiar with JWT-based authentication. One important consideration is that embedding features in Metabase **are not available on the free open-source plan**. You need a paid Pro or Enterprise subscription to unlock full embedding capabilities, including white-labeling and tighter access controls. Self-hosting also requires your own infrastructure and ongoing maintenance. **Best SQL Dashboard Tools for Embedding** for teams that want an open-source option, are comfortable with self-hosting, and are willing to pay for advanced embedding features. ### Key Features - Open-source with an active community and regular updates - Visual query builder alongside a full SQL editor - Embedding via signed URLs and iframes with parameter support - JWT-based authentication for secure embedded access - Wide range of visualization types and dashboard layouts - Supports a large number of databases and data warehouses ### Pricing Metabase is free to use and self-host on its open-source plan. Embedding features including white-labeling and advanced access controls are only available on the Pro plan starting at $575 per month or the Enterprise plan with custom pricing. --- ## Conclusion The best SQL dashboard tools for embedding are not just about charts. They are about how quickly you can get a polished data experience in front of your users, without compromising on security or flexibility. Draxlr has been built from the ground up with this in mind. Whether you are a developer shipping fast or a product team building a data-rich application, it is worth trying. Try Draxlr --- ## FAQs ### 1. What is an embedded SQL dashboard? An embedded SQL dashboard is a data visualization that lives inside your own application rather than in a separate BI tool. It connects directly to your SQL database and displays charts, tables, and KPIs within your product's UI, under your own branding. ### 2. Do I need to know SQL to use an embedded dashboard tool? Not necessarily. Most tools, including Draxlr, offer a visual query builder and AI-powered querying for non-technical users alongside a SQL editor for those who need more control. You can pick the approach that suits your team. ### 3. How long does it take to embed a dashboard into my product? With a tool like Draxlr, you can go from database connection to a working embedded dashboard in under an hour. Enterprise tools like Looker can take days or weeks depending on your setup and data model complexity. ### 4. Can I white-label an embedded dashboard? Yes. Tools like Draxlr let you remove their branding and apply your own colors, logos, and styling so the dashboard looks completely native to your product. ### 5. How is embedded analytics different from building dashboards in-house? Building in-house gives you full control but requires significant engineering time for charts, query management, access controls, and maintenance. Embedded SQL dashboard tools like Draxlr give you all of that out of the box so your team can focus on the product.

SQL Dashboard Tools for Small Teams in 2026

hello@draxlr.com (Draxlr) — Mon, 23 Mar 2026 00:00:00 GMT

If you're running a small team and drowning in raw database data, you already know the pain. Developers are heads-down building the product and they don't have time to field every "can you pull this report?" request. Without the right tooling, data requests pile up, CSVs get passed around, and insights arrive too late to matter. **SQL dashboard tools** solve exactly this problem by connecting directly to your database and turning queries into live, visual dashboards. But with so many options on the market, how do you pick the right SQL dashboard tool for a small team with limited time, budget, and engineering bandwidth? This guide covers everything you need to know about what SQL dashboard tools actually are, what to look for when choosing one, and the best SQL dashboard tools available in 2026. --- ## What Are SQL Dashboard Tools? SQL dashboard tools are platforms that connect directly to your SQL databases and transform raw query results into visual, interactive dashboards. Instead of running manual queries or exporting data into spreadsheets, these tools let you build charts, track KPIs, and monitor trends in real time, often without writing a single line of code. They're designed to make data accessible across the entire team, enabling faster insights and better decisions, whether you're in engineering, product, sales, or operations. A well-built SQL dashboard will typically let you: - Connect to databases like PostgreSQL, MySQL, BigQuery, Redshift, or Snowflake - Write or generate SQL queries and visualize the results instantly - Build and share dashboards with teammates or stakeholders - Set up alerts when data crosses a threshold - Schedule automated reports via email or Slack --- ## Why Small Teams Need SQL Dashboard Tools Large enterprises can afford dedicated BI teams and expensive tools but small teams can't and shouldn't have to. Here's what typically happens without a dedicated SQL dashboard tool: - **Developers get blocked.** Non-technical teammates constantly ask for data, pulling engineers away from product work. - **Data goes stale.** Manual exports and spreadsheets are outdated the moment they're created. - **Insights get missed.** Without real-time visibility, important changes in key metrics slip through the cracks. The right SQL dashboard tool fixes all three problems. It empowers non-technical team members to self-serve their data needs, keeps dashboards live and up-to-date, and lets you set up alerts so nothing important gets missed. --- ## What to Look for in SQL Dashboard Tools for Small Teams Not every tool is built with small teams in mind. Here's what actually matters when you're resource-constrained: ### 1. Ease of Setup and Use If a tool takes weeks to configure, it's already a liability. Look for a SQL dashboard tool that connects to your database in minutes, has an intuitive query builder, and ideally an AI layer that lets anyone ask questions in plain English with no SQL expertise required. ### 2. Pricing That Scales With You Enterprise pricing doesn't make sense for a 5-person team. Look for transparent pricing with a free trial, and plans that grow alongside your business. ### 3. Database Compatibility Make sure the tool supports your specific database, whether that's MySQL, PostgreSQL, Snowflake, BigQuery, Redshift, or something else. ### 4. Dashboard Sharing and Collaboration Small teams share data frequently. Look for easy sharing options such as shareable links, embeds, role-based access. ### 5. Alerts and Scheduled Reports Proactively knowing when something changes is far better than discovering it late. SQL dashboard tools should let you set up alerts on key metrics and schedule automated summaries. --- ## SQL Dashboard Tools for Small Teams in 2026: A Quick Comparison

Tool	Best for	Setup Time	Hidden Costs	Learning Curve	Pricing Starts at
Draxlr	Small teams wanting AI-powered, self-serve analytics	Minutes	None	None	$25/month
Zoho Analytics	Teams needing broad integrations	Hours	None	Moderate	$30/month
Power BI	Teams already in the Microsoft ecosystem	Days	Yes	Steep	$14/user/month + extras
Metabase	Developer-led teams	Hours	Yes	Moderate	Free / $85/month+

--- ## 1. Draxlr **Best SQL Dashboard Tool for AI-powered, Self-Serve Analytics** **[Draxlr](https://www.draxlr.com)** is a modern, self-serve business intelligence tool built specifically for growing businesses. It's designed around a simple idea that your whole team, not just engineers, should be able to get answers from your SQL databases. ### What Makes Draxlr Stand Out for Small Teams - **AI-Powered Query Builder.** Draxlr's Text-to-SQL AI lets you type a question in plain English and get a working SQL query, chart, and visualization instantly. - **Broad SQL Database Support.** Draxlr connects to over 10 SQL databases including PostgreSQL, MySQL, MariaDB, Microsoft SQL Server, Redshift, BigQuery, Supabase, ClickHouse, Snowflake, and more. - **Live Chat and Email Support on Every Plan.** Small teams do not always have time to dig through documentation. Every Draxlr plan, including the entry-level tier, includes access to live chat and email support so you can get real help quickly without paying for a premium support tier. - **Real-Time Dashboards.** Dashboards pull live data from your database, so your team always sees up-to-date metrics with no manual refreshes or stale exports. - **Automated Alerts and Scheduled Reports.** Set up alerts that trigger via Email or Slack when data changes, crosses a threshold, or on a set schedule. You can also schedule daily or weekly dashboard summaries. - **Embeddable Analytics.** Embed dashboards directly into your product or customer portal with row-level filters, so each customer sees only their own data. This is a huge feature for SaaS teams. - **Query Version Control.** Every query change is automatically tracked, so you can review history and revert to previous versions. - **Drill-Through Capabilities.** Click on any data point in a dashboard to drill through to the underlying detail, giving your team the ability to investigate spikes or anomalies without writing a new query. - **Granular Team Access Controls.** Invite multiple team members and assign each person read, full, or admin access so the right people see the right data without opening up everything to everyone. - **Password-Protected Shareable Links.** Share dashboards with clients or stakeholders via secure, password-protected links without requiring them to create an account or log into the system. - **CSV and Excel Exports.** Download any query result or dashboard data as a CSV or Excel file in one click, making it easy to share raw data with stakeholders who prefer spreadsheets. - **Flat, Transparent Pricing with No Hidden Costs.** The price you see is the price you pay. No per-viewer licensing, no surprise infrastructure add-ons, and no features locked behind a tier you did not know you needed. Annual billing also comes with a discount. ### Pricing - **Lite Plan** at $25/month - **Premium Plan** at $75/month - **Power Plan** at $125/month - **Self-Hosted** starts at $500/year - 7-day free trial with no credit card required ### Who It's For Draxlr is ideal for founders, operators, product teams, and small businesses that want clean dashboards, fast setup, and reliable support without needing a full-time data engineer. > Draxlr offers a clean, modern interface and makes it incredibly easy to build dashboards — even for non-technical users. I especially like the visual quality and flexibility of the available graph types. It's intuitive, fast to set up, and has excellent usability from the start. The ability to securely share dashboards via link is a game-changer for our client reporting. — [Review from a SaaS Founder on G2](https://www.g2.com/products/draxlr/reviews/draxlr-review-11185807) Connect your Database ## 2. Zoho Analytics **Best SQL Dashboard Tool for Zoho Ecosystem** Zoho Analytics is a cloud-based self-service BI and analytics platform built for businesses that want reporting without a dedicated data team. Its standout AI feature is Ask Zia, a conversational AI assistant that lets you type questions in plain English and get back reports, KPI widgets, and automated insights instantly. ### What Makes Zoho Analytics Stand Out - Type a question in plain English and Ask Zia generates the report, chart, or KPI widget instantly, no SQL knowledge needed. - Zoho Analytics connects to a very broad range of databases, cloud apps, spreadsheets, and third-party tools out of the box. - Embed interactive reports and dashboards into websites, web apps, or internal portals with white-label options available. - Set up automated report delivery to teammates or stakeholders on a daily, weekly, or monthly schedule. - Share reports and receive data updates directly inside Slack channels. ### Pricing - **Basic Plan** at $30/month - Free trial with no credit card required #### Who It's For Zoho Analytics works well for teams already in the Zoho ecosystem who want broad integrations. That said, users on review platforms consistently note that connecting data from outside Zoho can feel clunky, and getting the most out of advanced features often means you need someone with experience to set things up. If your team expects to be fully self-sufficient from day one, plan for a learning period that is steeper than the marketing suggests. ## 3. Microsoft Power BI **Best SQL Dashboard Tool for Microsoft Ecosystem** Microsoft Power BI is one of the most widely used BI tools in the world. It looks affordable on the surface at $14/user/month for Pro, and its brand recognition makes it a common first consideration for small teams. However, it comes with a set of real-world costs and complexities that often make it a poor fit for lean teams without dedicated data expertise. ### What Makes Power BI Stand Out - Power BI connects natively with Excel, Azure, Teams, and the broader Microsoft 365 suite, making it a natural fit for teams already in that ecosystem. - It supports hundreds of data connectors out of the box, covering databases, cloud services, and third-party platforms. - Reports and alerts can be automated through Power Automate without writing custom scripts. - A natural language query feature lets users ask questions about their data, though it requires proper data modeling to work reliably. - A wide base of tutorials, forums, and certified training resources makes it easier to find help when needed. ### Pricing - **Pro Plan** at $14/user/month - **Premium Plan** at $24/user/month - Additional infrastructure and training costs vary by team size ### Who It's For Power BI is a natural fit if your team already lives in Microsoft 365 and has someone who can own the setup. But real users are quick to point out that the free tier is essentially a personal tool since you cannot share reports without buying a Pro license for every viewer. Learning DAX and Power Query adds weeks of ramp-up time, and teams frequently discover that the $14/user price tag quietly grows once you factor in gateway infrastructure, Azure dependencies, and the training needed to unlock the tool's actual value. For a lean team without a dedicated analyst, the gap between what Power BI promises and what it delivers out of the box tends to be wide. ## 4. Metabase **Best Open-Source SQL Dashboard Tool** Metabase is one of the most well-known open-source BI tools available and a popular starting point for teams that want a self-hosted dashboard solution. It has a clean interface, supports SQL querying, and lets developer-led teams build charts and dashboards without heavy technical overhead. ### What Makes Metabase Stand Out - Metabase's open-source version is genuinely free, but self-hosting requires infrastructure costs that add up over time, especially as your data volume grows. - Non-technical users can build basic charts and summaries through a guided interface and AI without writing SQL. - For analysts who prefer direct query control, Metabase includes a full SQL editor alongside the no-code builder. - Set up dashboard subscriptions and threshold-based alerts that deliver updates via email or Slack. - Metabase connects to most major SQL databases and data warehouses including PostgreSQL, MySQL, BigQuery, Redshift, and Snowflake. ### Pricing - **Open-source** self-hosted tier - **Starter** cloud plan at $85/month for 5 users - **Pro** plan at $500/month ### Who It's For Metabase is a reasonable starting point if you have a developer who can manage a self-hosted setup and your reporting needs are basic. Users on G2 and Capterra appreciate its simplicity but consistently flag that dashboard customization is limited, queries can time out on larger datasets, and non-technical users still depend on someone who knows SQL when questions get complex. The free tier sounds attractive until you factor in server costs, maintenance, and the moment your team asks for white-labeling, SSO, or proper embedding, all of which sit behind a $500/month Pro plan. --- ## How to Choose the Right SQL Dashboard Tool for Your Team Here's a simple framework to make the decision: **If your team is mostly non-technical,** prioritize tools with AI query builders and no-code interfaces. Draxlr's Text-to-SQL AI is particularly well-suited here. **If you need a broad integration library,** Zoho Analytics covers over 500 data sources. **If you are already in the Microsoft ecosystem,** Power BI may feel familiar but factor in the per-viewer licensing and the learning curve before committing. **If you want a free starting point and have a developer who can manage infrastructure,** Metabase's open-source tier gets you started but be prepared to hit feature walls as your team grows. **If data privacy is critical,** look for tools with self-hosting options. Draxlr and Metabase both support this. **If you need to embed dashboards in your product,** Draxlr's embeddable analytics with row-level filtering is purpose-built for this use case. --- ## Conclusion SQL dashboard tools have become essential for small teams that want to move fast and stay data-informed. The right tool eliminates the bottleneck between raw data and business decisions, empowering every team member and not just engineers to get answers quickly. Among the options available in 2026, **Draxlr** stands out for small teams. It combines an AI-powered query builder, real-time dashboards, embeddable analytics, and automated alerts in one intuitive platform at a price point that makes sense for growing businesses. Whether you're a startup tracking product metrics, an agency reporting to clients, or a small ops team monitoring KPIs, the right SQL dashboard tool will save your engineers time, keep your stakeholders informed, and help your whole team make better decisions. --- ## FAQs ### 1. What is a SQL dashboard tool? A SQL dashboard tool is a platform that connects directly to your SQL database and transforms query results into visual charts, tables, and real-time dashboards. Instead of manually exporting data into spreadsheets, the tool lets your team build reports, track KPIs, and monitor key metrics in one place, often without writing a single line of SQL. ### 2. Do I need to know SQL to use a SQL dashboard tool? Not necessarily. Modern SQL dashboard tools like Draxlr include an AI query builder that lets you type questions in plain English and get a working query and visualization instantly. A visual query builder is also available for non-technical users who prefer a no-code approach. SQL knowledge is helpful for advanced use cases but is no longer a requirement to get started. ### 3. What databases do SQL dashboard tools support? Most SQL dashboard tools support the major relational databases including PostgreSQL, MySQL, MariaDB, Microsoft SQL Server, and data warehouses like BigQuery, Redshift, Snowflake, and ClickHouse. Always check compatibility with your specific database before committing to a tool. ### 4. How much do SQL dashboard tools cost for small teams? Pricing varies significantly. Some tools charge per user, which adds up quickly as your team grows. Others like Draxlr offer flat monthly pricing that covers the whole team without per-seat fees. Entry-level plans typically range from $30 to $100 per month. Always check whether viewer licenses are charged separately, as this is a common hidden cost. ### 5. Can SQL dashboard tools send automated reports and alerts? Yes, most modern SQL dashboard tools support scheduled reports delivered via email or Slack on a daily, weekly, or monthly basis. They also let you set threshold-based alerts so your team gets notified the moment a key metric goes above or below a defined value, without anyone having to check the dashboard manually. ### 6. Can non-technical teammates use SQL dashboard tools independently? That depends on the tool. The best SQL dashboard tools for small teams are built specifically so that non-technical users, such as product managers, operations leads, or sales teams, can explore data, build dashboards, and run reports on their own without involving a developer. This is the core reason small teams adopt tools like Draxlr in the first place. ### 7. How long does it take to set up a SQL dashboard tool? Setup time varies by tool. Some require days of configuration, data modeling, and training. Others like Draxlr are designed for fast onboarding and can be connected to your database and producing dashboards in under 30 minutes, with no technical setup required.

Metabase Alternatives for Embedded Analytics in 2026

hello@draxlr.com (Draxlr) — Mon, 16 Mar 2026 00:00:00 GMT

Metabase alternatives are in high demand right now. More SaaS teams are discovering that Metabase, while excellent for internal reporting, was never designed to power customer-facing analytics at scale. When you try to embed dashboards into your product, the cracks start to show. Iframes break your UI consistency, white-labeling sits behind expensive paywalls, and per-viewer pricing turns predictable budgets into moving targets. This guide covers the **best Metabase alternatives for [embedded analytics](/embedded-analytics-tool/) in 2026**. Whether you need a fast, developer-friendly SDK, a headless API-first engine, or an AI-powered self-service experience, there is a tool on this list built for exactly that job. We cover what each platform does well, where it falls short, and which kinds of teams will get the most value from it. --- ## Why Teams Look for Metabase Alternatives Metabase is built primarily around internal business intelligence. Its embedding capabilities exist, but they sit at the edges of what the product was designed to do, and that shows up in several real limitations that product teams run into quickly. **Most embedding features are locked to the top tiers.** On Metabase's two lower plans, the only embedding option is guest embeds, and those come with a forced "Powered by Metabase" badge that cannot be removed. Features that most product teams consider baseline requirements are completely unavailable on those tiers. This includes modular embedding, the React SDK, white-labeling with your own branding, custom colors and fonts, tenant isolation, embedded AI querying, an embedded query builder, multi-language embeds, and full-app embedding. All of these require the Pro plan or above. **Pricing that reflects those gates.** The Pro plan, where real embedding begins, starts at $575 per month plus $12 per user per month, with the first 10 users included. For a SaaS product serving dozens or hundreds of end customers, that structure becomes expensive quickly and the per-user model does not favour growth. **Performance limitations.** Even on paid plans, Metabase guest embeds rely on iframes which add rendering overhead before any query even runs. Larger datasets can also surface timeout issues that make the experience unreliable for customer-facing use. None of this makes Metabase a bad tool. It is genuinely excellent for internal dashboards and simple use cases. But as teams scale their analytics stack, they often explore other tools that better fit their evolving requirements. --- ## Quick Comparison of Metabase Alternatives for Embedded Analytics in 2026

Tool	Embedding Method	Best For
Draxlr	SDK + iFrame	AI-powered self-service
Power BI	iFrame	Microsoft ecosystem
Tableau	iFrame	Visualization-heavy BI
Luzmo	SDK + iFrame	SaaS dashboard embedding
Looker	SDK + iFrame	Google ecosystem
Sisense	SDK	Complex data environments
Qlik	SDK + iFrame	Associative data exploration
ThoughtSpot	SDK + iFrame	Natural language querying

--- ## 1. Draxlr **Best Metabase Alternative for SaaS Teams That Want Fast Embedded Analytics** [Draxlr](https://www.draxlr.com/) is a SQL-native embedded analytics platform built for SaaS teams that want to ship customer-facing dashboards quickly without building a custom BI layer from scratch. It gives developers HTML, React, and Vue embed options, a live desktop and mobile preview before shipping, and a clean settings panel to configure [drill-down](/features/drill-down/) behavior, export controls, background colors, and domain restrictions per embed. ### Who should consider Draxlr - SaaS teams that need each customer to see only their own data, enforced through row-level filtering at the database level - Organizations that want to give non-technical end users the ability to explore data freely - Teams that want complete white-labeling with custom domain, logo, and colors without it being gated behind an enterprise plan - Products that need fine-grained control over what end users can do inside each embed, including export permissions, drill-downs, and header visibility - Multi-tenant SaaS products that require workspace-level tenant isolation out of the box, not as a manual implementation - Development teams that want a secure backend API for generating embed tokens server-side, so credentials are never exposed on the client - Developers who prefer native React or Vue component embedding over relying on iframes ### Key Features - HTML, React, and Vue embed support with desktop and mobile preview - Embed settings panel with drill-down configuration, export controls, color customization, and domain allowlisting - Full white-label support with custom theme and complete removal of Draxlr branding - Row-level filtering for per-user and per-tenant data access - Dashboard level access to grant or restrict individual dashboards per user or tenant - Native multi-tenant architecture with isolated dashboards and permissions per workspace - SSO support for seamless authentication using your existing identity provider, keeping login flows consistent with the rest of your product - AI Chat that converts plain English questions into SQL queries, charts, and visualizations - Visual Query Builder, SQL editor, and AI Chat as three parallel ways to build queries - Backend API option for secure server-side embed token generation - Flat-rate pricing with no per-viewer fees as your customer base grows - Connects to PostgreSQL, MySQL, MariaDB, Microsoft SQL Server, BigQuery, Redshift, Snowflake, ClickHouse, Databricks, Supabase, PlanetScale, CockroachDB, and YugabyteDB ### Best for SaaS product teams who need to move fast, want full white-labeling and multi-tenant support out of the box, and prefer working directly with SQL databases without a complex semantic modeling setup. ### Pricing Draxlr uses flat-rate plans with no per-viewer or per-embed fees. Starts at **$75/month**. --- ## 2. Power BI **Best Metabase Alternative for Microsoft Ecosystem Shops** Power BI Embedded is Microsoft's solution for surfacing Power BI reports and dashboards inside external applications. It is one of the most widely deployed Metabase alternatives for teams already invested in the Azure and Office 365 stack, and its capacity-based pricing makes it more predictable than per-user models for customer-facing deployments. ### Who Should Consider Power BI - Teams already building on Microsoft Azure or using Power BI internally for reporting - Organizations in industries where Microsoft tooling is a standard requirement - Teams that want to surface existing Power BI content inside a product without rebuilding it ### Key Features - JavaScript API for native embedding of reports, dashboards, tiles, and Q&A experiences - Row-level security for tenant-based data isolation across customer deployments - Token-based authentication for secure, session-controlled embed access - Capacity-based A-SKU pricing — pay for reserved compute rather than per user - Extensive custom visuals marketplace with hundreds of chart types beyond the built-in library - Support for both app-owns-data and user-owns-data embedding scenarios - Integration with Azure Active Directory, Fabric, and the broader Microsoft data stack ### Best for Teams already invested in Microsoft Azure or using Power BI internally that need to surface existing dashboards and reports inside a web application without migrating to a new platform. ### Pricing Power BI Embedded uses capacity-based A-SKU pricing. The A1 SKU starts at approximately **$735/month**. Power BI Pro for internal users costs $10/user/month. Advanced embedding features require Premium Per Capacity or Premium Per User plans. --- ## 3. Tableau **Best Metabase Alternative for Advanced Data Storytelling and Visualization** Tableau is one of the most recognized BI platforms in the world. It is known for the depth and quality of its visualization library, a strong community of practitioners, and its ability to turn complex datasets into compelling, interactive visual narratives. As a Metabase alternative, it shines most for teams where the quality of visualization is the primary concern. ### Who Should Consider Tableau - Data teams that need an extensive variety of chart types and advanced visual formatting options - Organizations building analytics experiences for power users who want to explore and interact with data deeply - Teams already in the Salesforce ecosystem looking for native integration across CRM and analytics - Companies that need strong data storytelling capabilities for both internal and external audiences ### Key Features - Tableau JavaScript API for embedding individual views or full dashboards into web applications - Extensive visualization library with hundreds of chart types and formatting options - Tableau Prep for visual data preparation and transformation before analysis - SSO support and some white-labeling for embedded deployments - Tableau AI for AI-powered insights and natural language querying - Pulse for automated, AI-generated metric summaries delivered to users proactively - Large community and marketplace of pre-built connectors, extensions, and dashboard templates ### Best for Data teams and organizations that prioritize visualization quality and depth, work with power users who need rich interactive exploration, or are already embedded in the Salesforce ecosystem. ### Pricing Tableau's per-user pricing makes it one of the most expensive options on this list for customer-facing deployments. On the Enterprise tier, Viewer licenses cost $35 per user per month, Explorer $70, and Creator $115. Even if your customers only need to view dashboards, you still pay per viewer. For a SaaS product with 200 customers and 5 users each, that is **$35,000** per month in Viewer licenses alone before any development or infrastructure costs. --- ## 4. Luzmo **Best Metabase Alternative for SaaS Teams Building Embedded Dashboards** Luzmo is a developer-centric embedded analytics platform built from day one for SaaS teams that need to ship customer-facing dashboards quickly. It pairs an intuitive drag-and-drop dashboard builder with a JavaScript SDK, making it accessible to both technical and non-technical team members without requiring a complex setup. ### Who Should Consider Luzmo - Early-stage and growth-stage SaaS teams that need polished embedded dashboards without heavy engineering investment - Teams that want non-technical product managers or designers to build and iterate on dashboards independently - Products where analytics needs are well-defined and unlikely to require deep customization over time - Teams that want workspace-based pricing rather than a per-user model that scales against them as customers grow ### Key Features - JavaScript SDK and web components for native embedding with no iframes required - Full white-labeling with CSS-level customization and no visible Luzmo branding - Multi-tenant support with row-level security via JWT and user attributes - Drag-and-drop dashboard builder for non-technical users - Live and cached data modes for performance flexibility - Workspace-based pricing that does not penalize growth in end-user count - Themed components that inherit your application's design system ### Best for Early-stage and growth-stage SaaS teams that need polished, customer-facing embedded dashboards shipped quickly without heavy engineering investment upfront. ### Pricing Luzmo pricing starts at around **$495/month** on entry plans. Higher tiers unlock more data sources, users, and white-label customization. Enterprise plans are available through negotiation. --- ## 5. Looker **Best Metabase Alternative for Enterprise Teams with Complex Data Models** Looker is Google Cloud's enterprise BI platform built around LookML, a proprietary modelling language that defines metrics, dimensions, and relationships in a reusable and version-controlled way. It is a powerful Metabase alternative for organizations that need strong governance, consistent metric definitions across teams, and deep integration with Google's data infrastructure. ### Who Should Consider Looker - Enterprises with large data warehouses and established data teams who can invest in learning and maintaining LookML models - Organizations already deeply embedded in the Google Cloud or BigQuery ecosystem - Teams that need a single source of metric truth shared across internal and external analytics ### Key Features - Looker Embedded SDK for embedding individual charts, full dashboards, or the full Looker explore interface - LookML semantic layer for defining reusable, version-controlled metrics and dimensions - SSO integration and user attribute-based row-level security for multi-tenant deployments - Full white-labeling with custom theming options - Gemini AI integration for conversational analytics and natural language querying - REST API for programmatic access to all Looker resources - Git integration for version control of all data model changes ### Best for Enterprises with large data warehouses, dedicated data engineering teams, and existing investment in Google Cloud or BigQuery that need consistent metric governance across internal and customer-facing analytics. ### Pricing Looker pricing is enterprise-only and not publicly listed. Contracts typically start around **$30,000 per year or higher**, depending on users and features required. --- ## 6. Sisense **Best Metabase Alternative for Complex, High-Volume Data Environments** Sisense is an analytics platform built to handle large, complex, and multi-source datasets with speed. Its in-chip technology caches data in RAM and at the CPU cache level, enabling fast query performance even under heavy concurrent load. It is a solid Metabase alternative for teams where query performance and data volume are the primary concerns. ### Who Should Consider Sisense - Teams dealing with very large datasets where query speed at scale is a non-negotiable requirement - Data products that need to serve many concurrent end users without performance degradation - Organizations that need end-to-end control from data ingestion to embedded visualization within one platform - Enterprises that require fine-grained customization of the embedded experience through a composable SDK ### Key Features - Compose SDK for building fully customized embedded analytics experiences with React components - Fusion platform providing end-to-end control from data pipeline to embedded visualization - In-chip technology for high-speed query performance across very large datasets - Full white-labeling with custom themes, colors, and branding - SSO and multi-tenant deployment support - REST API and JavaScript API for programmatic access - AI-powered analytics for automated insights and anomaly detection ### Best for Teams handling large, multi-source datasets where query speed under concurrent load is the primary concern, particularly those building data products that need to serve many users at the same time. ### Pricing Sisense pricing starts at around **$21,000** per year and scales based on data volume and features. Custom enterprise pricing is negotiated per contract. --- ## 7. Qlik **Best Metabase Alternative for Associative Data Exploration** Qlik is one of the longest-standing names in analytics, known for its associative engine that allows users to explore relationships across datasets without predefined query paths. Unlike traditional filter-based BI tools, Qlik holds all data associations in memory and highlights what is related and what is excluded with every selection a user makes. Qlik Cloud Embedded brings this engine to external-facing products via REST APIs, a JavaScript API, and the Nebula.js visualization framework. ### Who Should Consider Qlik - Enterprises with complex, multi-dimensional datasets where users need to freely explore relationships rather than consume fixed dashboards - Teams already in the Qlik ecosystem looking to extend their analytics to customer-facing products - Products where exploratory analysis is a core use case rather than just dashboard consumption ### Key Features - REST and JavaScript APIs for programmatic embedding into external applications - Nebula.js for building fully custom visualization components on top of Qlik's associative engine - Associative in-memory engine for instant cross-filter interactions across large, multi-source datasets - Qlik Staige for AI-powered insights, natural language querying, and anomaly detection - Qlik Catalog for data lineage, metadata management, and governance - Row-level security and SSO for multi-tenant deployments - Active Intelligence for real-time alerting and automated action triggers ### Best for Enterprises with complex, multi-dimensional data environments where users need to explore relationships across datasets freely, particularly teams already invested in the Qlik ecosystem. ### Pricing Qlik Cloud uses capacity-based pricing. Entry plans start at around **$825/month** for a small team. Embedded analytics and enterprise deployments are priced through negotiation. --- ## 8. ThoughtSpot **Best Metabase Alternative for AI-Driven, Search-First Analytics** ThoughtSpot Embedded, marketed as ThoughtSpot Everywhere, is built around natural language search rather than pre-built dashboards. Users type questions in plain English, and ThoughtSpot's AI engine translates them into SQL, picks the most relevant chart type, and returns instant answers. It is one of the more distinct Metabase alternatives in terms of how end users actually interact with data. ### Who Should Consider ThoughtSpot - Organizations that want to give non-technical end users the ability to explore data freely without relying on pre-built dashboards - Teams where end users have highly varied, unpredictable analytical questions that cannot all be anticipated in advance - Enterprises with well-structured data models that can support natural language querying reliably - Products where self-service analytics is a core differentiator rather than a supplementary feature ### Key Features - Visual Embed SDK for native component-level embedding of charts, liveboards, or the search bar independently - ThoughtSpot Sage for GPT-style natural language querying and AI-generated insight summaries - Row-level security via user attributes and SSO for multi-tenant data isolation - Full white-labeling and theming support - SpotIQ for automated anomaly detection and trend analysis - Embeddable liveboards that respond to filters and user interactions in real time ### Best for Organizations that want to give non-technical end users natural language, self-service data exploration centered on asking questions rather than consuming pre-built dashboards. ### Pricing ThoughtSpot pricing is not publicly listed and is negotiated per contract. Enterprise plans typically start in the **tens of thousands per year**. Query-based billing can make costs harder to predict at scale. --- ## What to Consider When Choosing a Metabase Alternative for Embedded Analytics Picking the right [embedded analytics tool](/embedded-analytics-tool/) is not just a feature comparison exercise. The decision affects how fast you can ship, how much your costs grow with your customer base, and how seamlessly analytics feel inside your product. Here are the most important things to evaluate before committing. ### How native does the embedding need to feel? There is a meaningful difference between pasting an iframe into your app and embedding analytics as a true part of your product. Iframes are easy to set up but create visual inconsistencies, break on mobile, and feel bolted on to users. SDK-based embedding through React or Vue components lets dashboards inherit your app's design system, respond to your app's state, and behave like a native feature. If your customers interact with analytics frequently, the SDK approach is worth the extra setup time. ### How much control do you need over branding? Some tools offer white-labeling only on enterprise tiers. Others include it across all paid plans. If brand consistency matters to your product, verify exactly what is included at each pricing tier before evaluating features. Custom domain, logo removal, custom fonts, custom color palettes, and the ability to suppress any vendor branding are all separate considerations that vary significantly between platforms. ### Does the tool support multi-tenancy out of the box? In a SaaS product, each customer must only ever see their own data. Some tools handle this natively with built-in workspace isolation. Others require you to implement row-level security manually using filtered embed tokens. The former is significantly easier to maintain as your customer count grows. Ask specifically how tenant isolation works before signing up, not after. ### What does the pricing model look like at scale? Per-user pricing feels manageable early on but compounds quickly when you are serving customer-facing dashboards. A product with 200 customers, each with 5 users, can turn a modest monthly fee into a five-figure bill. Workspace-based or capacity-based pricing models tend to be more predictable for SaaS deployments. Always model out what the cost looks like at 50, 200, and 500 customers before choosing. ### How quickly can your team get to production? Time-to-first-embed varies dramatically between tools. Some require data modelling layers, semantic definitions, and infrastructure setup before a single dashboard can be shown. Others connect directly to your database and get you to an embedded dashboard the same day. If speed matters, prioritize tools with direct database connectivity, pre-built SDKs, and clear documentation over those that require a significant setup investment upfront. ### What is your team's technical profile? Headless and API-first platforms give maximum flexibility but require experienced developers to configure and maintain. Open-source options require DevOps capacity. If your team is lean or your developers are focused on core product work, a hosted solution with a visual builder and a well-documented SDK will let you ship faster without pulling engineering bandwidth away from other priorities. ### Does the tool grow with your analytical needs? Early on, you might need simple charts and filters. Over time, customers will ask for drill-downs, custom date ranges, ad-hoc queries, and scheduled reports. Evaluate not just what a tool does today but what headroom it gives you as your analytics requirements evolve. Migrating embedded analytics to a new platform after you have shipped to customers is a significant undertaking. --- ## Conclusion The landscape of Metabase alternatives for embedded analytics in 2026 is broad enough that there is a strong option for most team sizes, technical profiles, and product requirements. Developer-friendly platforms like Draxlr and Luzmo are built for fast SaaS deployment. Enterprise platforms like Looker, and Qlik serve teams with complex data models and governance requirements. And widely deployed options like Power BI Embedded fit naturally into Microsoft-centric organizations. The right choice depends on how your customers will interact with analytics, how much engineering time you can invest, what your data stack looks like, and how pricing behaves as your customer base grows. No single tool wins across all of those dimensions. The best approach is to pilot with your actual data and integration requirements before committing. Embed one dashboard inside your app, evaluate the developer experience, check branding controls, and test performance under realistic conditions. That process will tell you more than any feature comparison table. --- ## FAQs ### 1. What is the best Metabase alternative for embedded analytics in 2026? The best Metabase alternative depends on your specific needs. Draxlr is a strong choice for SaaS teams that want fast setup, SQL-native workflows, and full white-labeling with React and Vue SDKs. ### 2. Which Metabase alternative has the best multi-tenant support? Draxlr, Luzmo, and Sisense all offer strong multi-tenant support. Draxlr handles tenant isolation at the workspace level with row-level filtering, making each customer's data independently scoped without manual configuration per embed. Qlik and Looker also support multi-tenancy but require more engineering effort to set up and maintain tenant boundaries correctly. ### 3. Why would someone look for a Metabase alternative for embedded analytics? Most teams hit one of four walls. Metabase's core embedding features are locked behind the Pro plan at $575 per month. Even on paid plans, embeds rely on iframes that create a disjointed experience inside your app. The per-user pricing compounds quickly for SaaS products serving many customers. And multi-tenancy requires manual configuration rather than being handled out of the box. ### 4. What should I look for in a Metabase alternative for a SaaS product? The most important factors are native embedding without iframes, full white-labeling included in the plan you can afford, multi-tenant data isolation out of the box, and a pricing model that does not scale against you as your customer count grows. Beyond that, evaluate how quickly your team can get from a database connection to a live embedded dashboard, and whether the tool gives you enough room to grow as your analytics requirements evolve. ### 5. What is the best Metabase alternative for small to medium teams for embedded analytics in 2026? Draxlr is the strongest option for small to medium teams. It offers native SDK-based embedding, full white-labeling, and multi-tenant support without requiring a large engineering investment or a complex setup.

Metabase Alternatives for Non-Technical Teams in 2026

hello@draxlr.com (Draxlr) — Tue, 10 Mar 2026 00:00:00 GMT

Metabase is one of the most popular open source BI tools out there, and for good reason. It's powerful, flexible, and free to get started. But that "free" comes with hidden costs. You'll still need to handle deployment, server management, ongoing maintenance, and infrastructure expenses, which can add up quickly for teams without dedicated engineering resources. And even setting those costs aside, it's not the only option. Depending on your team's size, technical setup, and budget, another tool might be a better fit. This guide covers the **best Metabase alternatives for non-technical teams**, with a focus on ease of use, flexible querying, and self-service reporting. --- ## Why Teams Look for Metabase Alternatives Before we get into Metabase alternatives, it's worth understanding specifically where Metabase falls short for teams because the friction points are specific, not general. **Metabase does have AI.** Its Metabot feature allows users to generate SQL from natural language, fix query errors, and summarize chart results. That's genuinely useful but with important caveats: - **Metabot costs extra.** It's a paid add-on at $100/month for 500 queries, on top of your existing plan. It's not part of the default experience. - **Metabot is Cloud-only.** If your team uses the free self-hosted version, you don't get AI at all. - **The underlying interface is still query-centric.** Even with Metabot, users land in an environment built around data exploration. Non-technical users don't know what question to ask — they need the tool to guide them. - **Metabot is limited in scope.** It handles basic single-level aggregations, but complex cross-table analysis still requires SQL knowledge. - **Self-hosting has real overhead.** Setting up and maintaining Metabase on your own infrastructure requires engineering time most small teams don't have. None of this makes Metabase a bad tool. For technical teams that want a free, flexible BI platform, it's excellent. But for non-technical teams who need data to be truly self-serve, the alternatives below offer a fundamentally better experience. --- ## What to Look for in a Metabase Alternative for Non-Technical Teams - **AI built into the core experience.** Not a paid add-on or a Cloud-only feature - **Natural language querying.** Ask questions in plain English and get reliable answers without SQL - **No-code dashboard creation.** Build, share, and update without writing a query - **Minimal setup.** Cloud-hosted options with fast time-to-value, no DevOps required --- ## Quick Comparison: Metabase Alternatives for Non-Technical Teams in 2026

Tool	Natural Language Queries	Starting Price	Best For
Draxlr	Yes	$25/month	AI-powered analytics for non-technical teams
Microsoft Power BI	Yes	$24/user/month	Teams in the Microsoft ecosystem
Domo	Yes	Custom	All-in-one platform for large teams
ThoughtSpot	Yes	$50/user/month (min. 25 users, billed annually)	Enterprise data democratization
Zoho Analytics	Yes	$30/month	Teams in the Zoho ecosystem

--- ## 1. Draxlr ### Best Metabase Alternatives for AI-Powered Analytics for Non-Technical Teams **Best for:** Startups, SaaS teams, and growing businesses that want fast, self-service analytics without engineering overhead. [Draxlr](/) is an AI-powered analytics platform built for non-technical teams. Its AI assistant lets anyone on your team ask questions in plain English and get back a visualization with a plain English summary. No SQL, no query builder, no engineering support needed. ### Key Features **AI-first querying.** Type a question like "Show me revenue by region last quarter" and Draxlr generates the SQL, runs it, renders a chart, and explains the result in plain English, all automatically. Non-technical users get answers immediately without needing to understand the underlying data model. **Embeddable Dashboards.** Product and SaaS teams can embed white-label dashboards directly into their customer-facing applications, complete with row-level filtering and multi-tenant support. **Automated Alerts.** Set threshold-based alerts on your key metrics and receive notifications via Slack or email when data changes, keeping your team informed without anyone logging in to check. **Broad Database Support.** Connects to PostgreSQL, MySQL, BigQuery, Snowflake, Redshift, Supabase, ClickHouse, Databricks, and more, covering most modern data stacks without additional configuration. **Saved Queries and Dashboards.** Build and save queries as reusable reports, then organize them into dashboards that your whole team can access and explore. No rebuilding the same report from scratch every week. **Role-based Access Control.** Control who can view, edit, or share data within your workspace. Useful for teams that need to manage access across departments or clients. **Self-hosting Option.** For teams with data residency or compliance requirements, Draxlr supports self-hosted deployment alongside its cloud offering. ### Who It's For Draxlr is particularly well-suited for marketing, sales, and operations teams that need to self-serve their reporting without pulling in engineers. It's also a strong choice for SaaS founders who want to offer analytics inside their product without building a custom reporting layer. ### Pricing Draxlr starts at $25/month with a free trial available. Plans scale based on database, team size and features. ### Draxlr vs. Metabase

Feature	Draxlr	Metabase
Natural language (AI) queries	✅ Included by default	❌ Paid add-on only
Setup complexity	✅ Low (cloud-hosted)	❌ Medium-High (self-hosted)
White-label embedded dashboards	✅ All plans	❌ Paid plans only
Automated Slack / email alerts	✅ Built-in	❌ Limited
Visual Query Builder	✅	✅

--- ## 2. Microsoft Power BI ### Best Metabase Alternatives for Teams in the Microsoft Ecosystem **Best for:** Companies already using Microsoft 365, Azure, or SQL Server who want BI that integrates natively with their existing stack. Power BI is Microsoft's flagship analytics tool, and for teams already in the Microsoft orbit, it offers deep native integration with Excel, Teams, Azure Synapse, and Dynamics 365 that no competitor can match. ### Key Features - Native Microsoft integrations, Excel files become data sources in seconds - AI-powered Q&A, for natural language querying (improving rapidly) - Power BI Embedded, for embedding dashboards in applications - Scheduled reports and automated alerts ### Who It's For Power BI is the natural choice for any organization deeply embedded in Microsoft infrastructure. If your data lives in Azure SQL, SharePoint, or Excel, Power BI will feel like a natural extension of your existing workflow. ### Pricing Power BI Desktop is free to download. Advanced AI features like Copilot require the Premium Per User plan at $24/user/month. --- ## 3. Domo ### Best Metabase Alternatives for Large Teams Needing an All-in-One Platform **Best for:** Enterprise and mid-market teams that want to consolidate data, dashboards, and collaboration in a single platform. Domo is a cloud-native business intelligence platform that combines data integration, visualization, and collaboration tools under one roof. With over 1,000 data connectors, it's designed to connect every source of business data and make it accessible across the entire organization. ### Key Features - 1,000+ data source connectors, including Salesforce, Google, AWS, Snowflake, and Redshift - AI and machine learning tools for predictive analytics and automated insights - 150+ chart types for rich, customized data storytelling - App building capabilities to create custom business applications on top of your data - Mobile-first design with a polished iOS and Android experience ### Who It's For Domo works best for organizations that need a centralized data platform across multiple departments, with strong governance, real-time data access, and executive-level dashboards. ### Pricing Available on request. --- ## 4. ThoughtSpot ### Best Metabase Alternatives for Enterprise Search-First Analytics **Best for:** Mid-size and enterprise teams that want a Google-like search experience for their data, without writing SQL. ThoughtSpot pioneered the "search-first" approach to analytics — you type a question like you'd search on Google, and the platform instantly generates the most relevant chart or table. It's one of the most genuinely non-technical analytics experiences available. ### Key Features - Spotter. Conversational AI agent for natural language data exploration - SpotIQ. Automated analytics that proactively surfaces hidden insights you didn't know to ask for - SpotterViz. Build dashboards using natural language descriptions, no manual configuration needed - SpotterModel. Create semantic models without writing code - Enterprise-scale performance. Designed to handle billions of rows across large, complex data environments - Strong governance and security. Role-based access, audit logs, and fine-grained permissions built in ### Who It's For ThoughtSpot is best suited for mid-size and enterprise teams who want to democratize data access across the entire organization. It's a strong fit for data-forward companies that need self-service analytics at scale, without requiring every user to understand SQL or data modeling. ### Pricing Starts at $50/user/month (min. 25 users, billed annually) --- ## 5. Zoho Analytics ### Best Metabase Alternatives for Teams in the Zoho Ecosystem **Best for:** SMBs and mid-market companies already using Zoho CRM, Zoho Books, or other Zoho apps. Zoho Analytics is a full-featured BI platform with strong AI capabilities built in. Its "Zia" AI assistant allows users to ask questions conversationally and receive charts and summaries in response — making it one of the most mature natural language analytics experiences outside of enterprise tools. ### Key Features - 500+ data source integrations, including popular CRMs, databases, cloud storage, and spreadsheets - Zia AI assistant for natural language queries and auto-generated insights - Drag-and-drop report builder requiring no technical expertise - Automated reporting with scheduled delivery via email - Role-based access control to manage who can see or edit data ### Who It's For Zoho Analytics works best for companies already invested in the Zoho ecosystem, where native integrations eliminate much of the data wiring effort. For standalone use, it's still a capable option, though the interface can feel dense for first-time BI users. ### Pricing Plans start at $30/month for 2 users and scale up. --- ## How to Choose the Right Metabase Alternative With so many solid options, the right choice depends on a few key questions: **Do you have any technical resources available for setup?** If the answer is no, prioritize cloud-hosted tools like Draxlr or Zoho Analytics that require minimal configuration. **Where does your data live?** If it's in Microsoft products, Power BI wins on integrations and cost. If it's in a SQL database, Draxlr's direct database connectivity is a significant advantage. **Do you need AI-powered querying?** Metabase's Metabot costs an extra $100/month on top of the Starter plan for just 500 requests. Both Draxlr and ThoughtSpot offer strong natural language querying, but ThoughtSpot is geared toward enterprise teams while Draxlr is more accessible for smaller teams on a budget. **What's your budget?** Draxlr and Zoho Analytics offer affordable entry points. Power BI is cost-effective for Microsoft shops. Domo sits at the enterprise end of the spectrum. **Will you need customer-facing dashboards?** For embedded analytics in your own product, Draxlr's white-label embedding and multi-tenant support make it the most purpose-built option on this list. --- ## Which Metabase Alternative Is Right for Your Team? - Working with SQL databases and want AI included from day one → **Draxlr** - Need enterprise-grade natural language search at scale → **ThoughtSpot** - Want one platform handling your entire data stack → **Domo** - Already in the Microsoft ecosystem and want native integrations → **Power BI** - Need an all-in-one BI tool with strong data source coverage → **Zoho Analytics** --- ## Conclusion The best Metabase alternatives for non-technical teams aren't just "easier" versions of the same thing. They represent a different philosophy. Data tools should adapt to how business users think and work, not the other way around. The difference that matters most isn't which tool has AI. Most do. What matters is whether AI is central to how non-technical users interact with data from the moment they open the product, or whether it's an add-on layer sitting on top of an interface that still assumes technical knowledge. For teams where data access should be truly self-serve, that distinction is everything. Most tools on this list offer a free trial or demo. Pick the one that matches your biggest friction point and start there. --- ## FAQs ### 1. What is the best free alternative to Metabase? Metabase itself has a free open-source version, but it requires self-hosting and technical setup. Among the paid alternatives on this list, Draxlr, Zoho Analytics and Power BI offer the most affordable entry points with free trials, making them worth evaluating before committing. ### 2. Is Metabase good for non-technical teams? Metabase has a visual query builder that works well for basic questions, but more complex queries still require SQL. Its AI querying feature, Metabot, is available as an add-on at $100/month for 500 requests on top of the Starter plan which can be a barrier for teams that need it regularly. Tools like Draxlr include AI querying as part of their core offering. ### 3. What is the easiest BI tool for non-technical users? It depends on your use case. For teams that want multiple ways to query data — AI, visual builder, or raw SQL — Draxlr is one of the most flexible options. For teams already in the Microsoft ecosystem, Power BI's familiar interface lowers the learning curve significantly. Zoho Analytics is also a strong contender for SMBs that want a guided, all-in-one experience. ### 4. Which Metabase alternative is best for embedded analytics? Draxlr is the most purpose-built option for embedded, customer-facing dashboards on this list — offering white-label embedding and multi-tenant support without enterprise pricing. Domo and ThoughtSpot also support embedding but are geared toward larger organizations with bigger budgets. ### 5. Which Metabase alternative is best for non-technical teams? Draxlr is the strongest option for non-technical teams. It lets users query data in plain English, use a visual query builder, all without needing a developer involved. Zoho Analytics is also a solid pick for teams that want a guided, all-in-one experience with minimal setup. Both include AI querying as a core feature, unlike Metabase where it comes as a paid add-on.

Top Metabase Alternatives for Startups, Teams & Enterprises in 2026

hello@draxlr.com (Draxlr) — Fri, 06 Mar 2026 00:00:00 GMT

Metabase has long been a go-to business intelligence tool. It is open-source, easy to set up, and friendly enough for non-technical users to build their first dashboards. But as teams grow, data gets more complex, and business requirements evolve, many organizations find themselves hitting Metabase's ceiling. If you're evaluating **Metabase alternatives** in 2026, this guide breaks down the best options across every stage, from early-stage startups watching their budget to enterprise data teams that need governance, scale, and advanced analytics. --- ## Why Teams Start Looking for Metabase Alternatives in 2026 Metabase is a solid starting point, but it comes with limitations that tend to surface as companies scale. To understand where it falls short, we spent time reading through hundreds of user reviews on G2, Capterra, and Software Advice. These aren't dealbreakers for every team, but they're real friction points that drive people toward alternatives. Here's what the market looks like in 2026. **Limited true self-service analytics.** Business users frequently get stuck on pre-made SQL queries. Any data exploration beyond the basics typically requires a data engineer to step in, creating bottlenecks and slowing down decisions. **Performance at scale.** Metabase queries your database directly, which works fine for small datasets but leads to performance bottlenecks as data volumes grow. **Pricing that punishes growth.** Metabase's interactive embedding and advanced features like row-level security, SSO, white-labeling are gated behind Pro and Enterprise tiers. A SaaS company scaling to 1,000 users can find itself paying upward of $10,000 per month on the per-viewer model. **Limited visualization.** For teams that rely on geographic or density-based analysis, this is a hard blocker. Heat maps are a fundamental chart type in modern BI and are supported by most competitors, but Metabase has no native support for them. **The dashboard creation UI needs work.** Even users who like Metabase for querying find the dashboard builder frustrating. The UI for assembling and arranging dashboards feels underdeveloped, with limited layout flexibility, poor controls for sizing and positioning widgets, and a general lack of polish that makes building presentation-ready dashboards harder than it should be. **Integration is painful and support is limited.** Getting Metabase to play well with the rest of a modern data stack takes more effort than it should. Reviewers flag integration as "challenging" and when things break, customer support feels limited and slow to respond. **Audit logs and admin tools are paywalled.** Unless you're on the more expensive Enterprise edition, audit logs and content-copying tools are either absent or severely limited. For teams that need to track who accessed what, reproduce dashboard configurations across environments, or meet basic compliance requirements, this creates a frustrating paywall. It's the kind of feature that feels like it should be table stakes and is, on most competing platforms. **The open-source version comes with a developer dependency.** Self-hosting Metabase is free, but it's not actually free of cost. Running the open-source version requires ongoing developer involvement for setup, maintenance, upgrades, and debugging. Teams without dedicated engineering resources often find the "free" option isn't really free once they factor in the time it takes to keep it running. This is a significant consideration for startups and small teams evaluating Metabase against cloud-hosted alternatives. --- ## Best Metabase Alternatives in 2026 at a Glance

Tool	Best For	Open Source	AI capabilities	Pricing Starts At
Draxlr	Startups and teams wanting fast, AI-powered SQL analytics	No	Strong	$25/month
Apache Superset	Data teams wanting open-source power	Yes	Limited	self-hosted
Redash	SQL-first lightweight teams	Yes	Moderate	Custom (enterprise)
Sigma Computing	No-code analytics on spreadsheet interface	No	Limited	Custom
Power BI	Microsoft-heavy organizations	No	Moderate	$14/user/month
Tableau	Advanced visualization and enterprise BI	No	Moderate	$15/user/month
ThoughtSpot	AI and search-driven analytics	No	Strong	Custom
Looker	Large enterprises with complex data models	No	Moderate	Custom (enterprise)

--- ## Top Metabase Alternatives in 2026 for Startups ### 1. Draxlr If you're looking for the closest Metabase alternative that actually fixes Metabase's biggest pain points without adding complexity or ballooning your costs, Draxlr is the strongest contender in 2026. Draxlr is a next-generation BI tool built for teams that live in SQL databases. It connects directly to PostgreSQL, MySQL, Redshift, BigQuery, Databricks, and more, and gives every type of user a way to query and explore data. Non-technical users get a visual query builder, analysts and engineers get a full SQL editor, and an AI-powered Text-to-SQL layer converts plain English questions into working queries instantly. **Key strengths:** - Three flexible query modes: Visual builder, AI Text-to-SQL, and raw SQL editor - Connect and start building dashboards in minutes with no DevOps overhead - Embedded analytics for sharing dashboards inside your SaaS product with customers - Alerts via Email and Slack when data crosses defined thresholds - Self-hosted option available for teams that need full data control - Pricing starting at $25/month with no per-seat surprises as your team grows - 7-day free trial with no credit card required - Chat support available for all plans **Where it falls short:** Draxlr is optimized for relational SQL databases, so teams primarily working with NoSQL or time-series data may find it less suited to their stack. **Best for:** Startups, SaaS companies, and growing teams that want Metabase's ease of use, combined with AI-assisted querying, [affordable embedded analytics](/affordable-embedded-analytics-tool/), and pricing that scales predictably without blowing up at 50 users. ### 2. Apache Superset Apache Superset is the most popular open-source Metabase alternative, with over 70,000 GitHub stars and adoption from companies like Airbnb, Dropbox, and Lyft. It offers a no-code visualization builder alongside a powerful SQL IDE, making it workable for both technical and non-technical users. **Key strengths:** - 40+ chart types, cross-filters, and drill-to-detail - Supports PostgreSQL, BigQuery, Snowflake, MySQL, Redshift, Databricks, and many more - Row-level security and semantic layer for custom dimensions - Completely free when self-hosted **Where it falls short:** Superset has a steeper learning curve than Metabase and heavier infrastructure requirements. Non-technical users will need guidance to get started, and the initial setup is more involved. **Best for:** Startups and growing data teams that want Metabase's power without the licensing cost — and have the technical chops to manage self-hosting. ### 3. Redash Redash is a lightweight, SQL-first BI tool with over 28,000 GitHub stars. It's built around the idea of making it fast and easy to query data and build dashboards — without any frills getting in the way. **Key strengths:** - SQL query editor with autocomplete and parameterized queries - Connects to 35+ data sources including PostgreSQL, MySQL, BigQuery, Redshift, and MongoDB - Scheduled queries, alerts, and simple dashboard sharing - Free and self-hostable **Where it falls short:** Redash has fewer visualization types than Metabase, a less polished UI, and limited support for non-technical users who don't want to write SQL. **Best for:** Technical startups and engineering teams that think in SQL and want a simple, fast tool to query and share results — without the overhead of a full BI platform. --- ## Best Metabase Alternatives in 2026 for Growing Teams ### 4. Sigma Computing Sigma Computing takes an unusual approach to BI: it presents data through a spreadsheet-style interface that sits directly on top of your cloud data warehouse. There's no data extracts, no ETL, and no need for SQL — users interact with live warehouse data using familiar spreadsheet logic. **Key strengths:** - No-code exploration with powerful Excel-like formulas - Runs natively on Snowflake, BigQuery, Redshift, and Databricks - Write-back capabilities to your data warehouse - Strong performance on complex queries **Where it falls short:** Sigma has an opinionated UI with limited chart customization options. Dashboards embedded into external apps still feel foreign, similar to Metabase's limitations. **Best for:** Teams making the jump from spreadsheets to BI, or analysts who want SQL-level power without writing SQL. ### 5. Power BI Microsoft Power BI is one of the most widely adopted BI platforms in the world, and for good reason. It combines broad data connectivity, a powerful proprietary analytics language (DAX), and tight integration with the Microsoft 365 ecosystem. **Key strengths:** - Deep integration with Excel, Azure, Teams, and the broader Microsoft stack - Strong self-service analytics with drag-and-drop dashboards - Robust governance, workspace controls, and role-based security - Clear embedded analytics patterns for both "app-owns-data" and "user-owns-data" scenarios - Pro plan at ~$14/user/month makes it affordable at team scale **Where it falls short:** DAX can be challenging to learn, and Power BI's workflow isn't designed for version control — making it harder to manage changes and maintain analytics logic in engineering-style workflows. It's also not ideal for non-Microsoft operating systems like macOS or Linux. **Best for:** Teams already invested in the Microsoft ecosystem that need enterprise-grade analytics with mature governance and embedding options. --- ## Best Metabase Alternatives in 2026 for Enterprises ### 6. Tableau Tableau is the enterprise standard for data visualization. If you need highly customized, pixel-perfect visuals — complex geospatial analytics, intricate calculation logic, or advanced chart types — Tableau delivers what no other tool comes close to matching. **Key strengths:** - Massive visualization library with virtually unlimited customization - Strong embedded analytics capabilities with web authoring in-app - Mature governance, data lineage, and enterprise security - Tableau Cloud and Server options for flexible deployment **Where it falls short:** Tableau is expensive and has a steeper learning curve than Metabase. For teams that don't need its advanced visualization depth, it's often overkill. **Best for:** Enterprise data teams and analytics-heavy organizations that need best-in-class visualization, advanced reporting, and a mature ecosystem. ### 7. ThoughtSpot ThoughtSpot takes a fundamentally different approach to BI: instead of building dashboards, users search for insights using natural language. The platform's AI engine translates questions like "revenue by region last quarter" into instant answers and visualizations. **Key strengths:** - AI and search-driven analytics reduce dependency on SQL expertise - Modern SDKs for low-code embedded analytics - Works natively with cloud data warehouses - SpotIQ AI engine proactively surfaces anomalies and insights **Where it falls short:** ThoughtSpot's search-first philosophy doesn't suit all workflows, particularly teams that rely on traditional semantic modeling or need deeply customized dashboards. **Best for:** Enterprises looking to democratize analytics across the business, or product teams that want to expose AI-driven insights to end users without building a custom analytics layer. ### 8. Looker (Google Cloud) Looker, now part of Google Cloud, takes a code-first approach to BI built around LookML — a modeling language that defines metrics, dimensions, and relationships in a centralized semantic layer. Every report and dashboard is built on top of this layer, ensuring consistency across the organization. **Key strengths:** - Centralized semantic layer eliminates conflicting metrics across teams - Git-based version control for all analytics logic - Deep integration with BigQuery and the broader Google Cloud ecosystem - Strong API and embedded analytics capabilities **Where it falls short:** Looker requires significant investment in LookML modeling before teams see value. It's one of the more expensive options on the market, making it most suitable for large organizations with dedicated analytics engineering resources. **Best for:** Large enterprises with mature data stacks that need a governed, centralized source of truth across many teams and use cases. --- ## How to Choose Your Metabase Alternative There's no universal "best" BI tool. The right choice depends on your team's size, technical depth, use case, and where you're headed in the next 12–18 months. Before shortlisting any tool, work through these questions. Your answers will narrow the field quickly. ### Setup & Hosting - Do you need a **self-hosted** solution for data privacy or compliance reasons or are you comfortable with a cloud-hosted SaaS tool? - Do you have a developer available to manage infrastructure, upgrades, and maintenance? If not, open-source self-hosted tools (Superset, Redash) carry a hidden cost. - How fast do you need to be up and running? Some tools take hours to set up others take weeks of modeling work before they deliver value. ### Who Will Use It - Will your dashboards be used primarily by **technical users** (analysts, engineers) or **non-technical users** (marketing, ops, finance, leadership)? - Do your business users need to explore data on their own or are they happy consuming pre-built dashboards built by the data team? - Is there a need for **natural language or AI querying** so non-SQL users can ask their own questions without waiting on an analyst? ### Data & Analytics Requirements - What databases and data sources do you need to connect to? Make sure your shortlisted tool supports all of them natively. - Do you need **advanced chart types** like heat maps, funnels, cohort analysis, geospatial maps or will standard bar, line, and pie charts cover most use cases? - Do you need to **blend data from multiple sources** in a single dashboard, or are your queries contained to one database? - Is **real-time or near-real-time data** a requirement, or is a daily refresh sufficient? ### Embedded Analytics - Do you need to **embed dashboards inside your product** for your customers to see or is this purely an internal tool? - If embedding, how much customization do you need? White-labeling, custom themes, row-level permissions per customer? - Does the tool support embedding via a proper SDK or API — or only via iframes, which limit your flexibility? ### Governance & Security - Do you need **row-level security** — different users seeing different subsets of data based on their role or account? - Are **audit logs** a compliance requirement? (These are paywalled in Metabase unless you're on Enterprise.) - Do you need SSO, SAML, or SCIM for user management at scale? ### Pricing & Scale - Are you pricing per seat and do you have a clear sense of how many users will need access? Per-seat models can get expensive fast as teams grow. - Does the tool charge extra for features like embedding, white-labeling, or SSO or are these included? - What's the realistic total cost at 2x your current user count? Model this before committing. ### 🤝 Support & Ecosystem - How active is the community or vendor support? For open-source tools, is there a large enough community to get help when you're stuck? - Does the tool integrate with the rest of your stack — dbt, Fivetran, Slack alerts, your data warehouse? - Is the product being actively developed? Check the changelog or GitHub commit history — a tool that hasn't shipped meaningful updates in 6+ months is a risk. --- ## Conclusion Metabase remains a genuinely useful tool, especially for technical teams that want a quick, low-cost way to connect to a database and share dashboards internally. But in 2026, the market has moved to AI-assisted querying, warehouse-native analytics, and better self-serve experiences by raising the bar considerably. Whether you're a startup looking to replace a free tier you've outgrown, a product team needing cleaner embedded analytics, or an enterprise standardizing on governed BI, there's a Metabase alternative on this list that fits your context. The best move is to identify your top two or three requirements, shortlist two tools, and run a real-world pilot with your actual data before committing. --- ## FAQs ### 1. Is Metabase really free? Metabase's open-source version is free to self-host, but it comes with a developer dependency where someone needs to set it up, maintain it, and handle upgrades. The cloud-hosted version starts at $500/month. Advanced features like SSO, audit logs, and white-labeling require the Enterprise plan. ### 2. What is the best free Metabase alternative? Draxlr is the best Metabase alternative for teams looking for a free trial to get started. It offers a 7-day free trial with no credit card required, giving you full access to connect your database, build dashboards, and explore AI-powered querying before committing. For teams that need a completely free self-hosted option, Apache Superset and Redash are strong open-source picks, though both require developer involvement to set up and maintain. ### 3. Which Metabase alternative is best for non-technical users? Draxlr and Sigma Computing are the strongest picks. Draxlr offers a visual query builder and AI-powered Text-to-SQL so business users can explore data without writing a single line of SQL. Sigma Computing uses a familiar spreadsheet-style interface that feels natural for Excel-trained users. ### 4. Can I embed dashboards from Metabase alternatives into my product? Yes. Several tools support embedded analytics better than Metabase. Draxlr offers embedded dashboards with customer-facing sharing. Looker and ThoughtSpot provide full SDKs for white-labeled embedding. Metabase's iframe-based embedding is functional but limited in customization without an expensive Enterprise plan. ### 5. Which Metabase alternative is best for startups? Draxlr is the top pick for startups. It's fast to set up, starts at $25/month, supports AI querying, and scales without per-seat pricing surprises. For startups with strong technical teams and tight budgets, Apache Superset offers comparable features for free when self-hosted. ### 6. Is Metabase good for large enterprises? Metabase can work for enterprises on the managed Enterprise plan, but most large organizations outgrow it. Tools like Looker, Tableau, and Power BI offer stronger governance, centralized semantic layers, mature role-based access controls, and the enterprise support contracts that large organizations typically require.

Best Sisense Alternatives in 2026

hello@draxlr.com (Draxlr) — Tue, 03 Mar 2026 00:00:00 GMT

If you're evaluating Sisense alternatives, you're not alone. Sisense is a serious analytics platform, purpose built for embedded analytics, complex data modeling, and enterprise scale. But serious comes at a cost. Pricing is entirely custom and opaque, the implementation timeline is measured in weeks or months, and many teams find themselves over-paying for features they don't fully use. If Sisense was on your shortlist or you're already a customer looking to explore other options, this guide covers the best Sisense alternatives in 2026, with honest coverage of features, pricing, and who each tool actually serves well. --- ## Why Teams Look for Sisense Alternatives **Steep learning curve.** The Elasticube data modeling environment is one of the most commonly raised friction points. Data modeling in Sisense is powerful but cumbersome, and data sources often require ongoing manual maintenance. Teams without a dedicated BI engineer regularly struggle to get full value from the platform, and onboarding new analysts takes significantly longer than with more modern tools. **Reliability issues with the cache layer.** Elasticube build failures are a recurring operational headache. When builds fail, errors are difficult to diagnose, and some cube builds can trigger server errors that take down the environment entirely. For teams running production analytics that stakeholders depend on daily, this kind of unpredictability is a serious concern. **Limited flexibility for dashboard customization.** Basic formatting tasks that feel standard in other BI tools often require JavaScript workarounds in Sisense. Teams that need fine grained control over dashboard presentation frequently hit a ceiling and find themselves working around the platform rather than with it. **Slow performance on larger datasets.** As data volumes grow, Sisense can become noticeably sluggish. Charts can take a long time to refresh or fail to load entirely. Elasticubes become unstable when pushed beyond a certain size, and optimizing for performance typically requires infrastructure investment that wasn't initially planned for. **Expensive and non-transparent pricing.** Unlike most competitors, Sisense doesn't publish pricing publicly. Every engagement goes through a sales process, which makes it difficult to budget, benchmark, or compare options quickly. For teams that aren't large enterprises with a dedicated embedded BI budget, the cost and the process of finding out that cost can both be blockers. **Complex implementation.** Getting Sisense production ready, especially for embedded use cases, requires significant technical resources and time. Many small teams simply don't have the runway for that. **Overkill for many use cases.** Sisense is designed for enterprise scale, complex data environments. If your needs are more straightforward such as dashboards, SQL querying, and team reporting, Sisense's complexity becomes friction rather than value. **Support quality at lower tiers.** Smaller customers often report slower response times and less dedicated support compared to enterprise accounts. --- ## Quick Comparison: Best Sisense Alternatives in 2026

Tool	Strengths	Ideal For	Free Trial
Draxlr	AI querying, white label embedding, transparent pricing, fast setup	SaaS teams needing embedded analytics without enterprise complexity	Yes
Tableau	Best-in-class visualizations, wide data connector support	Analyst teams prioritizing rich, interactive dashboards	Yes
Power BI	Deep Microsoft integration, affordable per-user pricing	Organizations already using Microsoft 365 or Azure	Yes
Looker	Governed semantic layer, strong Google Cloud integration	Large enterprises needing consistent business logic across reports	No
Domo	1,000+ connectors, strong collaboration and storytelling tools	Business users needing self-service without IT dependency	Yes
Qlik Sense	Associative engine, surfaces hidden data relationships	Enterprise teams exploring complex, multi-source datasets	Yes
ThoughtSpot	Search-first AI analytics, natural language queries	Organizations wanting Google-like search as their analytics interface	Yes
Luzmo	Purpose-built SaaS embedding, multi tenancy by default	SaaS companies embedding analytics natively inside their product	Yes

--- ## 1. Draxlr **Best Sisense Alternative for Self-Serve SQL Based Analytics** [Draxlr](https://www.draxlr.com/) is a modern BI tool built around SQL databases with a strong focus on embedded analytics and ease of use. Where Sisense requires a complex implementation to get embedded analytics running, Draxlr provides React and Vue SDKs that let developers ship analytics inside their application in hours. The platform connects to 10+ SQL databases including PostgreSQL, MySQL, BigQuery, Databricks, Snowflake, Redshift, Supabase, ClickHouse, and more and offers a complete analytics layer with AI powered Text-to-SQL querying, visual dashboard builder, drill downs, pivot tables, alerts, and automated reporting. What genuinely differentiates Draxlr is its accessibility. Non technical users can build dashboards using the visual query builder or by asking questions in plain English via the AI assistant. Technical users get full SQL access with version control and complex joins. Both user types can work in the same platform. ### Key Features - AI powered Text-to-SQL for natural language queries - Connects to PostgreSQL, MySQL, BigQuery, Databricks, Snowflake, Redshift, Supabase, ClickHouse, SQL Server, and more - Visual query builder with nested grouping, aggregation, virtual columns, and filters - Complete visualization suite with charts, pivot tables, drill downs, drill throughs, and compute cards - Embedded analytics via React and Vue SDKs - White label BI with full branding control, custom domain, and multi tenancy - Self hosted deployment on your own infrastructure - Automated alerts on data thresholds and anomalies - Scheduled PDF exports and email report delivery - 7 day free trial, no credit card required ### Pricing - Lite at $25/month covers 1 database and 1 user - Premium at $75/month covers 2 databases, 20 users, and 100 AI credits - Power at $125/month covers 5 databases, 50 users, and 300 AI credits - Enterprise and self hosted plans are custom with SSO, audit logs, and custom AI models ### Best for SaaS companies, product teams, and data driven businesses that need embedded analytics without enterprise complexity or pricing. ### Why Draxlr over Sisense Draxlr is faster to implement, more transparent in pricing, and better suited for teams that don't need enterprise scale complexity. For SaaS companies embedding analytics into their product, Draxlr's SDK approach and white label capabilities match Sisense's core value proposition at a fraction of the cost and complexity. Connect your Database --- ## 2. Tableau **Best Sisense Alternative for Visualization First Analytics Teams** Tableau's visualization capabilities remain industry leading. Its drag and drop interface lets analysts build complex, interactive dashboards without code, and it supports a wide range of data connections. Tableau also has strong embedded analytics capabilities via Tableau Embedded Analytics. The limitations are per seat pricing that can get expensive at scale, and that building complex, governed data models requires significant setup. ### Pricing Starts at $15/user/month ### Best for Analytics teams that prioritize sophisticated, interactive data visualization and you have a dedicated analyst team working with diverse data sources. --- ## 3. Power BI **Best Sisense Alternative for Microsoft Stack Organizations** Power BI is the most widely deployed BI tool globally and offers strong visualization, a deep connector library, and tight Microsoft integration. Its per user pricing is affordable at small scale, though it gets complex with Premium capacity licensing for larger deployments. It also requires DAX expertise for advanced data modeling. ### Pricing Starts at $14/user/month, paid yearly ### Best for Organizations already using Microsoft 365, Azure, or Excel and you have technical users for data model development. --- ## 4. Looker **Best Sisense Alternative for Governed Semantic Layer Analytics** Looker's strength is its LookML based semantic layer, which enforces business logic consistently across all reports. It's deeply integrated with Google Cloud and BigQuery. The significant barriers are its $60,000/year starting price, no free trial, and the LookML expertise required to make it work. ### Pricing Starts at $60,000/year ### Best for Large enterprises that need a rigorous, organization wide semantic layer for consistent business definitions. --- ## 5. Domo **Best Sisense Alternative for Business User Self Service** Domo is built for business teams with strong data storytelling features, an app store of 1,000+ pre built connectors, and collaboration tools built in. It's more accessible than Sisense for non technical users. The downside is custom pricing that isn't publicly listed and an interface that some technical users find limiting. ### Pricing Custom, not published. Contact sales. ### Best for Organizations where business users need to self serve data without IT dependency. --- ## 6. Qlik Sense **Best Sisense Alternative for Associative Data Exploration** Qlik's associative engine is genuinely differentiated. It surfaces relationships in your data as you filter, helping analysts discover insights that traditional BI tools miss. It's enterprise scale, supports complex governance, and has strong embedding capabilities via its APIs. ### Pricing Business plan at around $300/month, Enterprise pricing is custom ### Best for Enterprise teams exploring complex, multi source datasets where relationships between data are as important as the data itself. --- ## 7. ThoughtSpot **Best Sisense Alternative for AI First Search Based Analytics** ThoughtSpot pioneered search based analytics. Users type questions in natural language and get charts and insights. Its AI engine, Sage, handles complex questions and surfaces automated insights. It's powerful for business user adoption but expensive and better suited for large teams. ### Pricing Enterprise pricing is custom. Team plan available with limited features. ### Best for Organizations that want natural language query as their primary analytics interface. --- ## 8. Luzmo **Best Sisense Alternative for SaaS Embedded Analytics** Luzmo is purpose built for embedded analytics in SaaS products. Unlike general purpose BI tools that added embedding as a feature, Luzmo is designed from the ground up to deliver analytics that live inside your product with full white labeling, multi tenancy, and row level security built in by default. ### Pricing Starts at $995/month for the Basic plan with 100 monthly active viewers, Pro at $2,050/month, and Elite at $3,100/month. Free trial available. ### Best for SaaS companies that need to embed interactive analytics natively inside their product for end users. --- ## How to Choose the Right Sisense Alternative **Choose Draxlr** if you need embedded analytics or a white label BI solution and want transparent pricing, fast setup, and a SQL native platform your whole team can use. It covers the core Sisense value proposition of embedded analytics, multi tenancy, and self hosting without the enterprise complexity. **Choose Tableau** if visualization sophistication is your priority and you have analyst resources to build and maintain reports. **Choose Power BI** if you're Microsoft first and want a widely supported, affordable per seat BI tool. **Choose Looker** if you're a large Google Cloud enterprise that needs governed, semantic layer analytics. **Choose Domo** if your primary audience is business users who need self service without SQL. **Choose Qlik Sense** if you need associative analytics for complex data exploration in an enterprise context. **Choose ThoughtSpot** if natural language, AI first analytics for business users is your primary use case. **Choose Luzmo** if you're a SaaS company that needs analytics embedded natively inside your product, with white labeling and multi tenancy as core requirements. --- ## Conclusion Sisense earns its reputation in complex, enterprise scale embedded analytics. But for teams that don't need that level of complexity, or can't justify the cost and implementation timeline, there are compelling alternatives. Draxlr is the most well rounded Sisense alternative for SQL driven teams. It delivers embedded analytics, white labeling, self hosting, and an AI powered query interface with transparent pricing and fast setup. If you're building analytics into a SaaS product or running internal dashboards, it's worth starting a free 7 day trial at [draxlr.com](https://draxlr.com). --- ## FAQs ### 1. What is the most affordable Sisense alternative? Draxlr is the most affordable option, starting at $25/month with transparent pricing. Power BI starts at $14/user/month and is a strong budget pick for Microsoft users. Both offer far more predictable costs than Sisense's custom enterprise pricing. ### 2. Is Sisense good for small businesses? Not typically. Sisense is designed for enterprise scale and pricing reflects that. Small businesses are better served by tools with transparent, flat monthly pricing and faster setup times. ### 3. Which Sisense alternative is best for embedded analytics? Tools with React and Vue SDKs and white label BI options are purpose built for embedded analytics. Draxlr, Luzmo and Qlik also have mature embedding capabilities for enterprise use cases. ### 4. Which Sisense alternative is best for small businesses? Draxlr is the strongest fit for small businesses. It starts at $25/month, requires no dedicated BI engineer, and can be up and running in hours. Unlike Sisense, pricing is transparent and there's no lengthy sales process. ### 5. Which Sisense alternative has natural language query features? Draxlr and ThoughtSpot are the strongest options. Draxlr includes AI powered Text-to-SQL that lets users ask questions in plain English and get results instantly. ThoughtSpot is purpose built around search based analytics with its Sage AI engine. Domo also offers natural language query capabilities for business users.

Best Power BI Alternatives in 2026

hello@draxlr.com (Draxlr) — Mon, 02 Mar 2026 00:00:00 GMT

If you're evaluating Power BI alternatives, you're not alone. Power BI is one of the most popular business intelligence tools in the world but popular doesn't always mean the right fit. Many teams run into the same set of frustrations. The desktop first experience creates friction for collaboration, the data modeling layer requires DAX expertise that most business users don't have, and the Pro license can become expensive at scale. Add in Microsoft's cloud dependency and complex licensing tiers, and it's no surprise that plenty of teams start looking for something better. If you want something simpler, more SQL native, better for embedded analytics, or just less tightly tied to Microsoft's ecosystem, this guide covers the best Power BI alternatives available in 2026. --- ## Why Teams Switch from Power BI **Complex data modeling.** Power BI's DAX (Data Analysis Expressions) language is powerful but difficult. This is the most common complaint across G2, Capterra, and Gartner Peer Insights. DAX looks deceptively like Excel formulas, but it's an entirely different beast. Non-technical users can view dashboards, but building anything meaningful requires DAX expertise that most teams don't have in-house. **Performance degrades with large datasets.** Across all major review platforms, slow performance with large data volumes is one of the top cited limitations. Users frequently report that reports become sluggish as dataset sizes grow, and optimizing for performance requires additional effort, often pushing teams toward Azure infrastructure investments they hadn't planned for. **Desktop first workflow.** Power BI Desktop is where most development happens, which means a Windows only workflow and a file sharing model that slows down collaboration. Reviewers on G2 and Capterra consistently flag this as a practical frustration. Mac users are limited to the web version, which has significantly fewer features than the desktop app. Teams that want to collaborate in real time or iterate together face friction, since reports are typically saved as .pbix files and passed around manually rather than worked on in a shared, browser based environment. **Confusing licensing tiers.** Beyond the Free vs Pro split, Power BI has Premium Per User, Premium Per Capacity, and Fabric licensing, each unlocking a different set of features. Users consistently describe the licensing structure as confusing and difficult to budget for, with advanced features like AI Copilot, paginated reports, and higher refresh rates locked behind premium tiers. **Built for Microsoft, awkward outside it.** Power BI works best when you're already in the Microsoft ecosystem including Azure, Excel, Teams, and SharePoint. Users note that connecting to non-Microsoft data sources often requires additional connectors or workarounds. Teams not on Microsoft infrastructure frequently feel like they're fighting the tool's design rather than working with it. **Refresh limitations.** The free version limits dataset refreshes to 8 times per day, and Pro users are capped at 48 refreshes per day. For teams that need live or near real-time data, this is a serious constraint. Users note that unlocking higher refresh rates or true streaming requires upgrading to Premium or investing in Azure infrastructure. What starts as a straightforward data refresh quickly becomes a licensing decision, and for many teams, an unexpected cost. **Sharing requires paid licenses.** Power BI Desktop is free, but the moment you want to share a dashboard with a colleague, everyone needs a Pro license at $14/user/month. Collaboration and sharing, arguably the most basic BI use case, sit behind a paywall. Multiple Capterra reviewers flagged this as a frustrating constraint, especially for smaller teams. --- ## Best Power BI Alternatives in 2026 at a Glance

Tool	Starting Price	Free Trial	SQL-Native	No-Code Friendly	Embedded Analytics
Draxlr	$25/month	✅ 7 days	✅	✅ (AI)	✅
Tableau	$15/user/month	✅	Partial	✅	✅
Looker	$60,000/year	❌	✅	❌	✅
Sisense	Custom	✅	✅	Partial	✅
Domo	Custom	✅	Partial	✅	✅
Luzmo	$995/month	✅	✅	✅	✅
Qlik Sense	$300/month	✅	Partial	Partial	✅

--- ## 1. Draxlr ### Best Power BI Alternative for SQL Teams [Draxlr](https://www.draxlr.com/) is a SQL-native AI-powered BI platform built for teams that want to move fast without depending on engineers for every report. It connects directly to your database including PostgreSQL, MySQL, BigQuery, Databricks, Snowflake, Redshift, and more and lets users build dashboards using a visual query builder, a direct SQL editor, or an AI-powered Text-to-SQL feature that converts plain English questions into working SQL queries. Where Power BI requires DAX expertise and a Windows desktop app, Draxlr is entirely browser-based and gives non-technical users real self-service capability. Its AI query assistant handles the complexity that would otherwise require a data engineer. ![Draxlr](/images/blogs/_tools/draxlr.png) ### Key Features: - AI-powered Text-to-SQL, ask questions in plain English and get charts and dashboards - Connects to 10+ SQL databases including PostgreSQL, MySQL, BigQuery, Databricks, Snowflake, Redshift, ClickHouse, Supabase, MariaDB, SQL Server, and more - Visual query builder with filters, joins, nested grouping, and aggregation - Rich visualization library with charts, pivot tables, drill-downs, and compute cards - Embedded analytics with React and Vue SDKs to ship analytics inside your product - Self-hosted deployment for data privacy and compliance - White-label BI for agencies and SaaS products - Automated alerts, scheduled PDF exports, and email summaries - 7-day free trial, no credit card required ### Pricing - Lite at $25/month covers 1 database and 1 user - Premium at $75/month covers 2 databases and 10 users - Power at $125/month covers 5 databases and 30 users - Enterprise and self-hosted plans available on request ### Best for SaaS companies, data teams, and startups that work with SQL databases and want a modern, AI-powered BI tool with no Microsoft dependency. ### Why it beats Power BI No Windows dependency, no DAX to learn, no Microsoft lock-in. Draxlr is browser-based, works on any stack, and gives non-technical users genuine self-service through AI. For SQL-driven teams, it's simpler, faster, and more cost-predictable. --- ## 2. Tableau **Best Power BI Alternative for Data analysts who need the most powerful, expressive visualization layer available.** Tableau has long been the benchmark for data visualization depth. Its drag-and-drop interface lets analysts build sophisticated, interactive charts that are hard to replicate elsewhere. Tableau connects to a wide range of data sources, handles large datasets, and has a rich marketplace of extensions. The trade-off is cost. Tableau Creator licenses run $75/user/month, which gets expensive for larger teams. And while it's excellent for analytics, it's not ideal for embedding into customer-facing products. ### Pricing Viewer at $15/user/month, Explorer at $42/user/month, Creator at $75/user/month ### Best if Visualization sophistication is your top priority and you have a dedicated analyst team. --- ## 3. Looker **Best for Large enterprises needing a rigorous, governed semantic layer shared across the organization.** Looker's LookML semantic layer is its defining feature. It lets data teams define business logic once and have it applied consistently across all reports. It's deeply integrated with BigQuery and the Google Cloud ecosystem. The downside is significant though, as pricing starts at $60,000/year, there's no free trial, and LookML requires developer resources. ### Pricing Starts at $60,000/year ### Best if You're a large enterprise on Google Cloud with dedicated data engineering resources. --- ## 4. Sisense **Best for SaaS companies building analytics into their product for customers.** Sisense specializes in embedded analytics and is designed to handle complex, multi-source datasets efficiently. Its in-chip technology compresses large data volumes for fast queries, and its JavaScript SDK makes embedding flexible. Like Looker, it's enterprise-priced and has a steeper learning curve. ### Pricing Custom pricing at enterprise tier ### Best if You're a SaaS company needing a battle-tested embedded analytics platform and have enterprise budget. --- ## 5. Domo **Best for Business-heavy teams that want polished dashboards and strong collaboration without IT dependency.** Domo is designed for business users first. It has strong data storytelling features, an app store of pre-built connectors, and features like Domo Everywhere for embedding. It's more accessible than Power BI for non-technical users, but pricing is enterprise-level and not transparent and you'll need to go through sales. ### Pricing Custom pricing, reported starting around $300/user/year ### Best if Business teams want to self-serve without IT and your organization is willing to pay premium for polish. --- ## 6. Luzmo **Best for SaaS companies that need to embed interactive analytics natively inside their product for end users.** Luzmo is purpose-built for embedded analytics in SaaS products. Unlike general-purpose BI tools that added embedding as a feature, Luzmo is designed from the ground up to deliver analytics that live inside your product — with full white-labeling, multi-tenancy, and row-level security built in by default. ### Pricing: Starts at $995/month (Basic — 100 monthly active viewers); Pro at $2,050/month; Elite at $3,100/month. Free trial available. ### Best if You're a SaaS company that needs analytics embedded natively inside your product, with white-labeling and multi-tenancy as core requirements. --- ## 7. Qlik Sense **Best for Enterprise teams that need to explore complex relationships across large, siloed datasets.** Qlik's associative engine lets analysts freely navigate data relationships without predefined hierarchies. It highlights what's related as you filter, which is genuinely powerful for discovery analytics. The downside is complexity and cost since Qlik is an enterprise platform with enterprise pricing. ### Pricing Business plan starts around $300/month and Enterprise requires a custom quote ### Best if You're in a large enterprise environment with complex, multi-source data needs and dedicated BI resources. --- ## Power BI Migration Checklist: How to Switch to a Power BI Alternative Choosing a Power BI alternative is only half the work. Migrating cleanly without disrupting your team is the other half. This checklist walks you through every step, whether you're moving to Draxlr, Tableau, Looker, or any other Power BI alternative on this list. ### Step 1. Audit Everything You Have in Power BI Before evaluating any Power BI alternative, document your current setup so nothing gets lost in the move. - List every active dashboard and report, and flag ones that haven't been opened in 90+ days as candidates for retirement - Map all data sources including SQL databases, Excel files, Azure connectors, and third-party APIs - Identify reports that rely heavily on complex DAX measures, as these will need the most attention when rebuilding in a new tool - Document who uses what, how often, and for which business decisions ### Step 2. Build Your Requirements List What frustrated you about Power BI becomes your requirements list for the Power BI alternative. Use your audit to define what the new BI tool must do better. - Do business users need to build their own reports, or just view dashboards? - Does your Power BI alternative need to support embedded analytics inside a product or client portal? - Is self-hosting, on-premises deployment, or data residency a compliance requirement? - Which databases and data warehouses need native connectors? - Do you need automated alerts, scheduled PDF reports, or email summaries? ### Step 3. Trial Your Shortlisted Power BI Alternatives Don't commit without testing. Pick 2 to 3 Power BI alternatives and run a structured trial using real data. - Connect your primary data source in each tool during the trial - Rebuild one of your most-used Power BI dashboards from scratch, as this is the most honest performance test - Have both a technical and a non-technical user test the experience independently - Score each Power BI alternative on setup time, query performance, learning curve, and support responsiveness ### Step 4. Migrate Your Data Sources Once you've selected your Power BI alternative, data connections come first. - Connect all SQL databases, cloud data warehouses, and flat file sources to the new platform - Run parallel queries in both tools and validate that results match before decommissioning anything - Recreate calculated fields, custom KPIs, and business metrics that were defined in DAX, using your new tool's SQL editor, visual query builder, or formula layer - Confirm that refresh schedules are working and data is updating at the expected frequency ### Step 5. Rebuild Your Most-Used Dashboards First Migrate by priority rather than by volume. The goal is to get your team working in the new Power BI alternative as quickly as possible. - Identify the 20% of dashboards that 80% of your team actually opens and start there - Use this as a chance to consolidate, since many teams discover during migration that they have redundant reports that can be merged or retired - Share rebuilt dashboards with stakeholders for sign-off before turning off the Power BI versions - If your new tool has an AI query assistant like Draxlr's Text-to-SQL, use it to rebuild reports faster by writing queries in plain English instead of recreating DAX logic manually - Some tools like Draxlr also offer professional services where their team builds your dashboards for you as a one-time fee project, which can significantly reduce migration time if you'd rather not rebuild everything in-house ### Step 6. Onboard Your Team A Power BI alternative only delivers value if your team adopts it. Adoption doesn't happen automatically. - Run a short onboarding session covering the core workflows including connecting data, building queries, creating dashboards, and sharing reports - Identify 1 to 2 internal champions per team who can answer questions and encourage usage - Create a short internal reference doc with your most common dashboards, shared queries, and data source connections - Set a clear cutover date, which is the day the team stops using Power BI and works exclusively in the new tool ### Step 7. Cancel Power BI Licenses and Clean Up Once the migration is complete and the team is fully operational in the new Power BI alternative, wrap up the Power BI side. - Cancel Pro and Premium licenses for users who have fully transitioned, as this is often an immediate and significant cost saving - Archive Power BI report files for reference rather than deleting immediately - Remove Power BI Desktop from company devices if it's no longer needed - Document the new BI stack in your internal wiki for future team onboarding --- ## Conclusion Power BI is a capable tool that works well for organizations deeply invested in the Microsoft ecosystem. But it's not the right fit for every team, especially those that want browser first collaboration, SQL native workflows, or a tool that doesn't require DAX expertise. Draxlr is the most compelling Power BI alternative for SQL-driven teams. It's fast to set up, genuinely accessible for non-technical users, and covers the full range from internal dashboards to customer-facing embedded analytics without Microsoft dependency or complex licensing. Start a free 7-day trial at [draxlr.com](https://draxlr.com). --- ## FAQs ### 1. What is the best Power BI alternative? The best Power BI alternative depends on your team's needs. SQL-native teams benefit most from tools that offer direct database connectivity and AI-assisted querying. Microsoft-heavy organizations may find alternatives that integrate with existing infrastructure more practical. The easiest way to find the right fit is to trial a shortlist using real data before committing. ### 2. Is Tableau better than Power BI? Tableau has a superior visualization layer and is generally better for deep, explorative analytics. Power BI wins on Microsoft ecosystem integration and per-seat cost at lower team sizes. For teams not on Microsoft infrastructure, Tableau is generally the stronger choice, though SQL-native tools are worth evaluating if your team works directly with databases. ### 3. What is the cheapest Power BI alternative? Open source tools that can be self-hosted are the most affordable at zero licensing cost, though they require engineering capacity to maintain. Commercial alternatives with AI features and managed hosting start as low as $25 to $30 per month with flat pricing that covers multiple users, making the per-user cost competitive even for small teams. ### 4. Can I export dashboards from Power BI to another tool? Not natively. BI tools don't have cross-platform dashboard export. However, since most tools connect to the same underlying databases, migration typically involves reconnecting your data source and rebuilding key reports rather than converting files. Visual query builders and AI query assistants in modern BI tools can significantly speed up the rebuild process. ### 5. Is there a Power BI alternative that works on Mac? Yes. Most modern Power BI alternatives are fully browser-based and run natively on any operating system including Mac, Linux, or Windows, with no virtualization or workarounds required. ### 6. What is the cheapest Power BI alternative for a small team? Several alternatives offer flat monthly pricing that covers multiple users under a single fee, making the effective per-user cost significantly lower than Power BI Pro at $14 per user per month. Some tools also offer free trials so you can validate the fit before committing to any spend. ### 7. Can non-technical users actually use Power BI alternatives? Most modern BI alternatives are specifically built to remove the technical barrier that Power BI's DAX language creates. Features like natural language querying, drag-and-drop dashboard builders, and AI-assisted report creation allow business users to build and explore dashboards independently without writing formulas, modeling data, or relying on a data engineer for every report request. ### 8. How hard is it to switch from Power BI to another tool? For most teams, the migration itself is straightforward. Since BI tools connect directly to your underlying databases, switching primarily involves reconnecting data sources and rebuilding dashboards rather than converting or exporting proprietary files. The main effort is translating DAX-based calculated fields into your new tool's query layer. A small team with a handful of dashboards can typically complete a migration in one to two weeks. ### 9. Is Power BI good for embedded analytics? This is widely considered one of Power BI's weakest areas. Embedding dashboards into a SaaS product or client-facing portal requires Azure capacity licensing that is complex to configure and difficult to price predictably. Teams building embedded analytics into their own products typically find purpose-built alternatives significantly easier to implement, more flexible to brand, and more cost-predictable at scale.

Best Looker Alternatives in 2026

hello@draxlr.com (Draxlr) — Sat, 28 Feb 2026 00:00:00 GMT

If you're evaluating Looker alternatives, you're not alone. Looker is a powerful business intelligence platform but it comes with serious trade-offs. Pricing starts at $60,000 per year, there's no free trial, and making the most of it requires engineers who know LookML, Google's proprietary modeling language. For many teams, those trade-offs simply don't make sense anymore. Whether you're a startup watching costs, a mid-size team tired of depending on engineers for every report, or an enterprise evaluating your BI stack, there are strong Looker alternatives worth knowing about. This guide breaks down the seven best options in 2026 what they do well, where they fall short, and which teams each one fits best. --- ## Why Teams Look for Looker Alternatives Before diving into the list, it helps to understand why Looker loses deals: **Cost.** At $60,000+ per year for the base license, plus $400/year per dashboard viewer, Looker is inaccessible for most small-to-mid-size businesses. Costs can escalate quickly as your team grows. **LookML dependency.** Looker's semantic layer is built on LookML, a proprietary modeling language. Non-technical users can't self-serve without a developer building views and explores first. **Google Cloud lock-in.** Since Google's acquisition, Looker has increasingly pushed users toward BigQuery and the Google Cloud ecosystem, which isn't ideal for teams on other stacks. **No free trial.** Most teams can't even test Looker before committing to enterprise negotiations. --- ## Best Looker Alternatives in 2026 at a Glance

Tool	Starting Price	Free Trial	SQL-Native	Embedded Analytics
Draxlr	$25/month	Yes	✅	✅
Tableau	$15/user/month	Yes	Partial	✅
Power BI	$14/user/month, paid yearly	Yes	Partial	Limited
Sisense	Custom	Yes	✅	✅
Qlik Sense	$300/month	Yes	Partial	✅
Zoho Analytics	$30/month	Yes	Partial	Limited
Redash	Free	Yes	✅	Limited

--- ## 1. Draxlr ### AI-powered self-serve Looker Alternative for SQL Teams [Draxlr](https://www.draxlr.com/) is an AI-powered BI tool built around your SQL database. You connect your database like PostgreSQL, MySQL, BigQuery, Databricks, Snowflake, Redshift, and more and immediately start building dashboards using a visual query builder, a plain SQL editor, or Draxlr's AI-powered Text-to-SQL feature that lets you ask questions in plain English. What makes Draxlr stand out as a Looker alternative is its combination of depth and accessibility. Technical users get full SQL control, version history, and complex joins. Non-technical users get an intuitive drag-and-drop interface and AI-generated queries. Both can work in the same tool without friction. ### Key Features - Connects to 10+ SQL databases including PostgreSQL, MySQL, BigQuery, Databricks, Snowflake, Redshift, Supabase, ClickHouse, and more - AI-powered Text-to-SQL: write queries using plain English - Visual query builder with nested grouping, aggregation, and virtual columns - Beautiful dashboards with charts, pivot tables, drill-downs, and drill-throughs - Embedded analytics with React and Vue SDKs for shipping analytics inside your product - Self-hosted deployment option for full data control - White-label BI for agencies and SaaS products - Automated alerts, PDF exports, and email summaries - 7-day free trial with no credit card required ### Pricing - Lite: $25/month (1 database, 1 user, 10 AI credits) - Premium: $75/month (2 databases, 10 users, 100 AI credits) - Power: $125/month (5 databases, 30 users, 300 AI credits) - Self-hosted and enterprise plans available ### Why Draxlr beats Looker Looker costs 40–60x more per year, requires LookML expertise, and has no free trial. Draxlr gives you SQL-native analytics, AI assistance, embedded capabilities, and self-hosting—for a fraction of the price. Teams are typically up and running within hours, not weeks. ### Best for Startups, SaaS companies, and data teams that want powerful SQL-based analytics without the enterprise price tag. --- ## 2. Tableau ### Best Looker alternative for Advanced Data Visualization Tableau is the gold standard for complex, interactive data visualization. Its drag-and-drop interface is intuitive for analysts, it handles large datasets well, and Tableau Public is a strong community resource. However, Tableau Creator licenses start at $75/user/month, and its embedding and governance features don't match enterprise BI platforms like Looker at scale. ### Key features - Drag-and-drop visualization builder with 20+ chart types and a VizQL engine for code-free visual querying - Tableau Prep for visual data cleaning, reshaping, and pipeline building before analysis - Live and extract connection modes to balance real-time freshness with query performance - Level of Detail (LOD) expressions and calculated fields for complex, multi-granularity analytics - 80+ native data connectors with support for ODBC/JDBC and Tableau Embedded Analytics for third-party app integration ### Pricing Viewer $15/user/month, Explorer $42/user/month, Creator $75/user/month ### Best for Data analysts and scientists who need the most powerful visualization capabilities available. --- ## 3. Power BI ### Best Looker Alternative for Microsoft Ecosystem Teams Microsoft's Power BI is the most widely adopted BI tool in the world, and for good reason. It integrates tightly with Excel, Azure, and Microsoft Teams, has an enormous library of connectors, and offers impressive visualization capabilities. The desktop version is free, and the Pro plan comes in at $10/user/month. The catch? Power BI's data model can be complex for non-technical users, the mobile experience lags behind competitors, and large datasets often require Azure infrastructure investment to perform well. ### Key features - Multiple native data connectors including Azure, Salesforce, Google Analytics, and SharePoint - DAX language for building custom calculations, measures, and advanced data models - Power Query for data transformation, cleaning, and shaping before it reaches dashboards - Interactive visualizations including charts, maps, KPI cards, matrix tables, and a custom visuals marketplace - Row-level security and integration with Microsoft Teams, SharePoint, and Excel for access control and collaboration ### Pricing Starts at $14/user/month, paid yearly ### Best for Your team lives in Microsoft products and you have some technical capacity to build and maintain data models. --- ## 4. Sisense ### Best Looker Alternative for Embedded Analytics at Scale Sisense is an end-to-end analytics platform designed around embedded analytics and complex data models. It uses an in-chip technology to handle large datasets efficiently and offers a flexible JavaScript SDK for embedding. The downside is pricing—Sisense is enterprise-tier and requires custom quotes—and the platform has a steeper learning curve than most alternatives. ### Key features - Elasticube in-chip analytics engine for fast queries across large, multi-source datasets - JavaScript SDK and REST APIs for embedding analytics into external applications - SSO and multi-tenancy support for white-label and multi-client deployments - 100+ data connectors covering databases, cloud applications, and flat files - Row-level and widget-level security for granular data access control ### Pricing Custom pricing (enterprise). Contact sales. ### Best for SaaS companies that need to embed analytics inside their product for customers. --- ## 5. Qlik Sense ### Best Looker alternative for Associative Data Exploration Qlik Sense uses an associative engine that highlights relationships between data points as you filter, making it ideal for exploratory analysis. It's highly scalable, supports governed data environments, and offers a polished embedding SDK. Like Looker, it's enterprise-priced and complex to set up. ### Key Features - Associative analytics engine that surfaces data relationships across all fields as users apply filters - Insight Advisor for natural language search and AI-generated chart recommendations - Drag-and-drop visual analytics builder with smart chart type suggestions - Role-based governance, data lineage tracking, and enterprise access controls - REST API and mashup SDK for embedding Qlik visualizations in external applications, with multi-cloud and on-premises deployment support ### Pricing Business plan starts around $300/month; Enterprise requires custom quote ### Best for Enterprise teams that need to explore relationships across large, complex datasets. --- ## 6. Zoho Analytics ### Best Looker Alternative for SMBs on a Budget Zoho Analytics offers drag-and-drop dashboards, AI-powered analytics via its Zia assistant, and deep integration with the Zoho ecosystem (CRM, Desk, Projects, etc.). It's priced very accessibly and can be up and running quickly. It does struggle with very large datasets and the visualizations aren't as polished as Tableau or Draxlr. ### Key Features - Drag-and-drop report and dashboard builder with 50+ chart types - Zia AI assistant for natural language querying and automated insight generation - 500+ data connectors including Zoho apps, Salesforce, Google Ads, Shopify, and QuickBooks - Data blending to combine and analyze data from multiple sources in a single report - Row-level and column-level security with scheduled email delivery of reports and dashboards ### Pricing Starts at $30/month for 2 users ### Best for Small businesses needing an affordable, easy-to-use BI tool. --- ## 7. Redash ### Best Looker Alternative for Open-Source Option Redash is an open-source BI tool that connects to virtually any SQL database and lets teams write queries, visualize results, and share dashboards. It's free to self-host and popular in engineering-heavy organizations. The trade-offs are real though: there's no AI assistance, visualizations are limited, and self-hosting requires DevOps capacity. ### Key Features - SQL editor with autocomplete, query snippets, and support for parameterized queries - 35+ data source connectors including PostgreSQL, MySQL, BigQuery, Redshift, MongoDB, and Elasticsearch - Basic visualization types including line, bar, pie, scatter, cohort, and table views - Query scheduling for automated data refresh and threshold-based alerting - Self-hostable via Docker with user management and group-level data source permissions ### Pricing Free (open source), cloud plan available ### Best For Strong engineering resources, prioritize cost-cutting, and need a no-frills querying and sharing tool. --- ## How to Choose the Right Looker Alternative **Choose Draxlr** if you want a full-featured SQL BI tool with AI assistance, great customer support, embedded analytics, and self-hosting—at a price your team can actually afford. It's the best fit for startups, SaaS companies, and any team that works directly with SQL databases. **Choose Tableau** if data visualization quality is your top priority and you're willing to pay per-seat. **Choose Power BI** if your organization is deeply embedded in the Microsoft ecosystem and you have technical users who can build data models. **Choose Sisense** if you're building a SaaS product and need a proven embedded analytics platform with enterprise support. **Choose Qlik Sense** if your enterprise needs advanced associative analytics with complex data governance. **Choose Zoho Analytics** if you're a small business already using Zoho products and want affordability over depth. **Choose Redash** if you're engineering-first, cost-sensitive, and don't need AI features or polished dashboards. --- ## Conclusion Looker remains a strong platform for enterprises that need governed, semantic-layer analytics at scale. But for most teams, the combination of high cost, LookML complexity, and Google Cloud dependency makes it hard to justify — especially when capable alternatives exist at a fraction of the price. The right Looker alternative depends on your team's priorities. If SQL flexibility and self-hosting matter most, look for tools built around direct database connectivity. If you're deep in the Microsoft or Google ecosystem, native integrations will save you significant setup time. If embedded analytics for a SaaS product is the goal, prioritize platforms with mature SDKs and white-labeling support. Most tools on this list offer a free trial or free tier, so the cost of evaluating your options is low. Shortlist two or three based on your stack and team size, run a quick proof of concept, and choose the one that your team will actually use. --- ## FAQs ### What are the best Looker alternatives in 2026? The best Looker alternatives in 2026 are BI platforms that offer SQL-native querying, self-service dashboards, AI-assisted analytics, and flexible pricing without requiring a proprietary modeling language or an enterprise contract. ### Why are teams switching from Looker to alternatives? Teams switch from Looker primarily because of cost ($60,000+/year), the dependency on LookML expertise, Google Cloud lock-in, and a lack of free trial. Many teams find that alternative tools offer comparable or better capabilities at a fraction of the price. ### What is the cheapest Looker alternative? Open source BI tools offer the most affordable entry point — they can be self-hosted at no cost. For commercial alternatives with AI features, embedded analytics, and managed hosting, plans start as low as $25–30/month depending on the platform. ### Which Looker alternatives work without LookML or a semantic layer? Most Looker alternatives do not require LookML or any proprietary modeling layer. Many tools let you connect your database and start querying immediately using a visual query builder, natural language AI, or raw SQL with no upfront modeling work. ### Which Looker alternative is best for non-technical users? The best Looker alternatives for non-technical users offer AI-powered Text-to-SQL, drag-and-drop query builders, and guided dashboard creation so business users can get answers from data without writing SQL or depending on a data team. ### Which Looker alternatives support embedded analytics? Several Looker alternatives support embedded analytics, offering JavaScript SDKs, React and Vue components, white-labeling, domain restrictions, and multi-tenant configurations. These features are especially useful for SaaS companies that need to ship analytics inside their product. ### Can small businesses use a Looker alternative? Yes. Several Looker alternatives are designed specifically for small businesses, with affordable pricing, minimal setup, and no requirement for dedicated data engineers. Many offer free trials so teams can evaluate before committing.

Best Basedash Alternatives in 2026

hello@draxlr.com (Draxlr) — Thu, 26 Feb 2026 00:00:00 GMT

If you're searching for the best Basedash alternative, you've come to the right place. Basedash has made a name for itself as an AI-native business intelligence platform — one that lets teams connect data sources, ask questions in plain English, and get dashboards generated in seconds without writing a single line of SQL. It's purpose-built for the AI era, and teams love it for its conversational analytics experience. But Basedash may not be the right fit for every team. Whether you need tighter SQL control, embedded analytics for your own SaaS product, more affordable self-hosting, or simpler pricing, there are strong alternatives worth considering, especially ones that also bring meaningful AI capabilities to the table. In this blog, we've compared each alternative purely through the lens of their AI analytics features, because if you're moving away from Basedash, you shouldn't have to leave AI-powered insights behind. Let's dive in. --- ## Quick Comparison: Best Basedash Alternatives in 2026

Tool	AI Feature	Best For	Pricing
Draxlr	Text-to-SQL AI, NL queries	AI-powdered, self-serve	Starts at $25 per user per month
ThoughtSpot	Agentic Spotter AI	Enterprise scale	Starts at $50/user/month (min. 25 users, billed annually)
Power BI	Copilot, Smart Narratives	Microsoft teams	Starts at $24 per user per month, paid yearly
Zoho Analytics	Ask Zia AI agent	SMBs	Starts at $145 per 15 users per month
Tableau	Tableau Agent, Pulse	Visual storytelling	Starts at $40 per user per month
Looker Studio	Gemini AI (Pro)	Google ecosystem	Custom pricing
Domo	Domo.AI, Agent Catalyst	Enterprise platforms	Custom pricing

--- ## 1. Draxlr ### Best AI Analytics for SQL-Heavy Teams & SaaS Products Draxlr is a no-code BI and analytics platform built specifically for SQL databases. It combines an intuitive drag-and-drop query builder with a powerful AI SQL assistant, and also has a raw query editor, making it easy for both technical and non-technical users to extract insights from their data fast.

### AI Analytics Capabilities Draxlr's standout AI feature is its **Text-to-SQL engine**. You type a plain English question like "Show me top customers by revenue last quarter" and Draxlr translates it into an executable SQL query, runs it, and renders the result as a chart or dashboard visualization — all without touching SQL. - **Text-to-SQL:** Ask a question in plain English and get an executable SQL query, result, and chart instantly - **Auto-visualization:** every AI query renders a chart automatically - **AI result summaries:** after each query, Draxlr generates a plain English explanation of the data so non-technical teammates can understand findings without interpreting charts themselves - **Conversational follow-ups:** refine results by asking follow-up questions in the same chat session, without starting over - **Editable AI-generated SQL:** technical users can inspect, modify, and re-run the SQL the AI produces — directly in the chat, no separate editor needed - **Auto execute:** AI queries run automatically the moment a response is generated, no manual trigger needed - **Show SQL query:** the generated SQL is always visible alongside the result - **Broad database support:** PostgreSQL, MySQL, BigQuery, Databricks, Snowflake, Redshift, Supabase, ClickHouse, and more ### Why Teams Choose Draxlr Over Basedash While Basedash is designed as an AI-first BI platform with a heavier focus on agentic analytics and KPI discovery, Draxlr takes a more grounded approach giving teams AI assistance exactly where they need it (query generation and visualization) while offering deeper control over how data is explored and presented. There are a few notable gaps in Basedash that Draxlr addresses directly: **1. Limited graph customization in Basedash.** Basedash does offer some chart settings like chart type, axis selection, breakdown, and legend toggle. But it stops there. Draxlr's graph options go significantly deeper: font size, background color, individual bar colors, logarithmic scale, bar orientation, barmode (grouped vs stacked), trend lines, summary sections, grid line controls, axis label toggles, time formatting, and more. For teams that need polished, presentation-ready charts, Draxlr's level of control is in a different league. **2. Basedash filters require SQL knowledge.** Basedash does have a filter feature, but it works by injecting variables directly into the underlying SQL query meaning someone with SQL knowledge needs to set them up correctly. For non-technical viewers, this creates friction. Draxlr's dashboard filters are built for end users: you define a Name, pick a Data Type, and optionally build a dropdown list from manual values or a saved query, no SQL required. Anyone on the team can add and use filters without touching the underlying query. **3. Basedash tables are raw data dumps with no customization.** Basedash does support tables on dashboards, but you have to explicitly instruct the AI to add one, and what you get is a plain record list with no controls - no aggregation, no grouping, no joins, no summary row, no pivot. Draxlr's dashboard tables are fully configurable through a visual query builder: you can join tables, group by fields, apply aggregate functions, add filters, sort, select specific columns, create pivot tables, and edit a summary row — all without writing a single line of SQL. **4. No visual query builder — only raw SQL or AI.** In Basedash, you either write SQL manually or rely entirely on AI to generate it. There's no middle ground. Draxlr's visual query builder gives non-technical users a point-and-click interface to build queries without writing SQL, while still giving technical users full SQL access when they need it. This makes Draxlr genuinely accessible to the whole team, not just developers. **5. No multi-level drill-down on Basedash graphs.** Basedash doesn't support clicking into a data point to explore what's behind it. Draxlr's drill-down capability lets users go from a high-level chart straight into the granular records driving any number a critical feature for operational teams investigating anomalies. **6. Basedash embedding is dashboard-only with no customization.** Basedash lets you embed dashboards via a simple iframe toggle. There's no element-level embedding, no domain restrictions, no color customization, and no control over what viewers can export. Draxlr's embed settings are built for SaaS products where you can restrict allowed domains, show or hide the dashboard header, configure drill-down behavior, control exports, customize background colors, and generate embed code in HTML, React, or Vue. There's even a backend API option for embeds that shouldn't expose internal identifiers. **7. Rearranging dashboard elements in Basedash is painful.** Basedash doesn't have an intuitive drag-and-drop layout editor — repositioning charts and tables on a dashboard is cumbersome and frustrating. Draxlr lets you drag and drop any element on the dashboard freely, making it easy to organize your layout exactly how you want it without any friction. **8. Basedash starts with email-only support at $250/month.** If you need chat support on Basedash, you're paying a premium just to get timely help. Draxlr includes chat support on all plans, so every team — regardless of plan size — can get help when they need it. - Draxlr has affordable pricing starting at **~$75/month for 20 users** ### Best for Startups, SaaS companies, and growing teams who want AI-assisted SQL analytics, full control over their visualizations, and the ability to embed interactive dashboards into their products. --- ## 2. ThoughtSpot ### Best for Agentic AI Analytics at Scale ThoughtSpot is one of the most mature AI analytics platforms on the market, built around a natural language search interface called SpotIQ and an agentic AI analyst named Spotter. ### AI Analytics Capabilities ThoughtSpot's Spotter acts as an autonomous AI data agent — it doesn't just answer questions, it reasons through them, asks follow-up questions, generates summaries, and proactively suggests next steps. This is as close to a fully agentic BI experience as any tool on the market today. - Spotter: agentic AI analyst that reasons, asks follow-ups, and surfaces insights proactively - Natural language search across live Liveboards - SpotIQ: automated insight detection that flags anomalies and trends - AI-generated KPI dashboards ### How It Compares to Basedash on AI ThoughtSpot's AI is arguably more sophisticated than Basedash's for enterprise-scale deployments. However, it comes with significantly higher complexity and cost. For smaller or mid-sized teams, the learning curve and pricing can be prohibitive. If AI breadth and scale matter more than simplicity, ThoughtSpot leads. ### Best for Large enterprises needing powerful agentic AI analytics across massive datasets. --- ## 3. Microsoft Power BI ### Best AI Analytics for Microsoft-First Teams Power BI is Microsoft's flagship BI tool and has been rapidly evolving its AI capabilities with the introduction of Copilot, deeply integrated with the Microsoft 365 ecosystem. ### AI Analytics Capabilities Power BI's Copilot integration allows users to ask questions about their data in a conversational interface. The AI generates reports, builds visuals, and writes DAX expressions — all in natural language. - Copilot: conversational analytics with natural language report generation - Azure ML integration for no-code machine learning - AI-powered smart narratives and automated insights - Q&A feature for natural language queries on any dataset ### How It Compares to Basedash on AI Power BI's AI shines when you're already deep in the Microsoft ecosystem. Copilot is mature and well-integrated, but it can feel constrained outside of Microsoft-native data sources. Basedash offers a more flexible, source-agnostic AI experience. Power BI wins on enterprise governance and Office integration. ### Best for Teams already using Microsoft 365, Azure, or Excel who want AI-assisted analytics within a familiar ecosystem. --- ## 4. Zoho Analytics ### Best AI Analytics for SMBs Zoho Analytics is a cloud-based BI platform positioned for small and mid-sized businesses. Its AI capabilities are built around Ask Zia, a conversational AI analyst powered by Zoho's own large language models. ### AI Analytics Capabilities Ask Zia allows users to ask data questions conversationally, generates reports, detects anomalies, and offers predictive insights. It integrates tightly with the broader Zoho business suite. - Ask Zia Agent: conversational AI for natural language data queries - AI-powered anomaly detection and forecasting - Automated report and dashboard generation - Predictive analytics built into the platform - Supports 500+ data source integrations ### How It Compares to Basedash on AI Zoho's AI is comparable to Basedash for common business questions but tends to be more structured and guided rather than fully conversational. Where Zoho wins is affordability and its integration with Zoho CRM, Desk, and other business apps. If you live in the Zoho ecosystem, the AI experience is highly contextual. ### Best for SMBs looking for affordable AI-assisted analytics with strong integration into business applications. --- ## 5. Tableau ### Best AI Analytics for Advanced Visualizations Tableau is one of the most iconic BI tools ever built, and with Salesforce's Einstein GPT integration through Tableau Agent, it has become a serious AI analytics contender. ### AI Analytics Capabilities Tableau Agent brings conversational visualization creation to Tableau's powerful charting engine. Tableau Pulse delivers proactive AI-driven insights pushed to users without them having to ask, acting like an AI monitoring layer over your dashboards. - Tableau Agent: conversational AI for creating and modifying visualizations - Tableau Pulse: proactive AI insights that surface anomalies and trends automatically - Einstein Copilot integration for Salesforce data - Natural language queries over published data sources ### How It Compares to Basedash on AI Tableau's AI capabilities are built on top of a legacy platform, which means the experience can feel less fluid than Basedash's purpose-built AI-native approach. Complex analyses still require traditional Tableau skills. However, if visualization depth and storytelling matter more than conversational ease, Tableau is unmatched. ### Best for Teams that need rich, advanced visualizations and are willing to invest in learning the tool properly. --- ## 6. Looker Studio ### Best AI Analytics for the Google Ecosystem Looker Studio (formerly Google Data Studio) is Google's free BI platform, now featuring Gemini AI integration for Pro users. Its deep Google ecosystem integration makes it a natural choice for marketing and growth teams. ### AI Analytics Capabilities Gemini AI integration enables natural language queries, automated chart generation, and AI-assisted report building within the Google ecosystem — though it's limited to Pro users at $9/user/month. - Gemini AI integration for natural language querying (Pro only) - AI-assisted report and chart generation - Seamless integration with Google Analytics, Ads, BigQuery, and Sheets - Collaborative dashboard building with AI suggestions ### How It Compares to Basedash on AI Looker Studio's AI is competent but gated behind the paid tier and primarily shines with Google data sources. Basedash is more source-agnostic and the AI experience is native throughout rather than an add-on. For teams already in the Google ecosystem on a budget, Looker Studio is hard to beat. ### Best for Marketing and growth teams in the Google ecosystem who want free or low-cost AI-assisted reporting. --- ## 7. Domo ### Best AI Analytics for Enterprise Data Platforms Domo positions itself as a comprehensive data platform with its Domo.AI framework, featuring 1,000+ pre-built connectors and a dedicated AI infrastructure for enterprise teams. ### AI Analytics Capabilities Domo.AI features an end-to-end suite of AI tools designed for autonomous business processes. Its Agent Catalyst feature enables building and deploying custom AI data agents, while real-time data processing makes it particularly powerful for operational analytics. - Domo.AI: end-to-end AI analytics framework - Agent Catalyst: build and deploy custom AI data agents - Natural language interface for querying data and generating dashboards - AI-powered anomaly detection and predictive analytics - 1,000+ pre-built connectors with real-time data processing ### How It Compares to Basedash on AI Domo's AI is powerful but designed for enterprise scale with an enterprise price tag. Basedash offers a more accessible and faster AI analytics experience for smaller teams. Domo wins for organizations that need AI across a massive, diverse data ecosystem with strict governance requirements. ### Best for Large enterprises with complex, multi-source data environments needing end-to-end AI-powered business intelligence. --- ## Conclusion Basedash has carved out a strong position as an AI-native BI platform, and it works well for teams whose primary need is conversational, chat-driven analytics. But no single tool is right for every team. If you need more control over your visualizations, a visual query builder for non-technical users, advanced embedding for a SaaS product, or a lower price point with self-hosting included, there are solid alternatives that cover those gaps without sacrificing AI analytics capabilities. The best approach is to shortlist two or three tools based on your team's specific priorities whether that's AI depth, SQL flexibility, embedding, pricing, or ease of use and run a trial before committing. Most of the tools listed here offer a free tier or trial period, so the cost of evaluating them is low. --- ## FAQs ### 1. What are the best Basedash alternatives in 2026? The best Basedash alternatives are AI powered BI tools that offer natural language SQL generation, customizable dashboards, real time alerts, and embedding options. Many teams choose alternatives that provide more flexibility as their reporting needs grow. ### 2. Why are teams searching for Basedash alternatives? Teams search for Basedash alternatives when they need more advanced dashboard controls, better pricing, strong customization, or more. As data workflows become more operational, flexibility becomes essential. ### 3. Is there a more scalable Basedash alternative? Yes. Some Basedash alternatives are built for long term reporting workflows, offering AI generated SQL, visual query builders, and direct SQL editing in one platform. These tools scale better for growing teams. ### 4. Do Basedash alternatives support embedded analytics? Many Basedash alternatives provide secure dashboard embedding and public sharing links. This is important for SaaS companies that want to add analytics directly inside their product. ### 5. Are there self hosted Basedash alternatives? Yes. Certain Basedash alternatives offer self hosted deployment, allowing companies to run analytics within their own infrastructure for security and compliance reasons. ### 6. What should I look for in a Basedash alternative? When comparing Basedash alternatives, look for: AI-powered SQL generation, flexible query-building options, real-time alerts, dashboard embedding, and transparent pricing. ### 7. Does Basedash have a visual query builder? No. Basedash only offers raw SQL editing or AI-generated queries ### 8. Does Draxlr have AI analytics like Basedash? Yes. Draxlr includes Text-to-SQL AI, AI-generated chart and dashboard creation, AI summaries of query results, follow-up questions in a conversational chat, auto-execute, and the ability to edit and re-run AI-generated SQL — making it a strong AI analytics alternative to Basedash.

Best Grafana Alternatives in 2026

hello@draxlr.com (Draxlr) — Wed, 25 Feb 2026 00:00:00 GMT

Finding the right Grafana alternative can significantly change how your team monitors infrastructure, builds dashboards, and shares data insights across your organization. Grafana has long been the popular open source platform for DevOps and engineering teams but it's not the right tool for everyone. Whether you're struggling with its steep learning curve, limited SQL analytics, or the challenge of making dashboards accessible to non-technical stakeholders, finding the right **Grafana alternative** in 2026 has never been more important. Teams today need more than just time-series visualization. They need tools that speak the language of their entire organization from engineers to product managers to executives. The best Grafana alternatives go beyond infrastructure monitoring to offer richer business intelligence, AI-powered querying, and seamless embedded analytics inside your own products. Whether you're looking for a **simpler setup**, **deeper SQL support**, or a **self-serve analytics experience** your whole team can actually use. This guide covers the top Grafana alternatives worth considering in 2026. --- ## Why consider a Grafana alternative? Grafana excels at infrastructure monitoring and time-series visualization, but several factors lead organizations to look for Grafana alternatives. Understanding these pain points can help determine whether switching makes sense for your team. ### 1. The Learning Curve Is Too Steep for Most Teams Grafana was built by engineers, for engineers. Setting up data sources, writing PromQL or Flux queries, configuring panel transformations, and managing variables requires a level of technical expertise that most users simply don't have. In practice, this means dashboards get built by one or two people on the team and everyone else is left waiting for updates or struggling to interpret what they're looking at. For organizations that want data to be truly self-serve, Grafana's complexity becomes a bottleneck rather than an asset. ### 2. Limited SQL and relational database support Grafana was designed with infrastructure monitoring in mind. It excels at visualizing metrics that change over time — CPU usage, request latency, error rates. But most business data doesn't live in Prometheus or InfluxDB. It lives in PostgreSQL, MySQL, Redshift, or BigQuery and Grafana's support for relational databases, while technically present, is nowhere near as smooth or intuitive as its time-series experience. Teams that need to answer questions like "Which customers churned last month?" or "What's our revenue by region this quarter?" quickly find that Grafana is the wrong tool for the job. ### 3. Poor self-serve experience Non-technical users struggle to build or even interpret dashboards without engineering support. Business users such as PMs, growth teams, and customer success managers typically cannot create their own views without requesting help. This creates a constant backlog of dashboard requests that engineering teams have to service, reducing bandwidth for actual product work. ### 4. No native AI-powered querying In 2026, the expectation is that analytics tools should let users ask questions in plain English and get instant answers. Grafana has no native AI querying capability. Every insight still requires someone who knows how to write the right query in the right query language, PromQL for Prometheus, Flux for InfluxDB, raw SQL for databases. For organizations trying to democratize data access, this is a significant limitation. ### 5. Embedded analytics limitations Embedding Grafana dashboards inside your own product is cumbersome and often unsupported at scale. Grafana's embedding options are largely read-only, require users to be authenticated against Grafana itself, and offer limited multi-tenant isolation. Building a customer-facing analytics experience on top of Grafana typically demands significant custom engineering, whereas purpose-built Grafana alternatives offer embedding as a first-class feature. ### 6. Alert and notification flexibility Setting up meaningful, actionable alerts is far more involved than it should be. Grafana's alert rules require careful configuration of conditions, evaluation intervals, contact points, and notification policies. Minor mistakes lead to alert storms or missed notifications, and non-engineers rarely have the confidence to manage alerting independently. ### 7. Cost at scale Grafana Cloud pricing increases significantly with data volume and active series. Running Grafana in production especially Grafana Enterprise or a self-managed setup with high availability isn't free. Beyond licensing, there's the cost of maintaining the underlying infrastructure, managing upgrades, and dealing with plugin compatibility issues. For teams without dedicated DevOps resources, this overhead can quickly outweigh the benefits. ### 8. Lack of built-in business intelligence features Grafana is an observability tool, not a full BI platform. Teams that need pivots, aggregations, saved reports, and executive dashboards often build entirely separate Grafana alternatives alongside it. If any of these pain points sound familiar, the following **Grafana alternatives** are worth a serious look in 2026. --- ## Best Grafana alternatives in 2026 at a glance

Tool	Best use case	Ease of use	AI capabilities	Embedded analytics	SQL support
Draxlr	AI powered self-service analytics	⭐⭐⭐⭐⭐	✅ Full AI querying + natural language	✅ Yes	✅ Yes
Looker	Enterprise SQL analytics on Google Cloud	⭐⭐⭐	❌ Limited	✅ Yes	✅ Yes
Power BI	BI for Microsoft ecosystem teams	⭐⭐⭐⭐	✅ Copilot + natural language Q&A	✅ Yes	✅ Yes
Sisense	Embedded analytics in SaaS products	⭐⭐⭐	✅ Anomaly detection + insights	✅ Yes	✅ Yes
Domo	Business-wide data collaboration	⭐⭐⭐⭐	✅ Forecasting + AI insights	✅ Yes	✅ Yes
Holistics	SQL modeling for analyst-led teams	⭐⭐⭐	❌ No	✅ Yes	✅ Yes
Apache Superset	Free open-source BI	⭐⭐⭐	❌ No	✅ Yes	✅ Yes
Tableau	Enterprise data visualization	⭐⭐⭐	✅ Tableau Pulse + AI insights	✅ Yes	✅ Yes

--- ## 1. Draxlr ### Best Grafana Alternative for SQL-Based Analytics & AI Querying If you're looking for a Grafana alternative that bridges the gap between engineering and business teams, **Draxlr** is the strongest option in 2026. While Grafana is built primarily for infrastructure observability, Draxlr is purpose-built for **SQL-based analytics on relational databases** — making it far more accessible to every type of user across your organization, regardless of their technical background. ### What makes Draxlr stand out Draxlr connects directly to your existing SQL databases like PostgreSQL, MySQL, Redshift, BigQuery, Databricks, and more and meets users exactly where they are with **three flexible ways to query data**: - **Visual Query Builder** — For non-technical users who want to explore data without writing a single line of code. Filter, group, sort, and aggregate data through a clean point-and-click interface that feels intuitive from day one. - **AI-Powered Querying** — For business users who want even faster answers, Draxlr lets you ask questions in plain English and instantly generates the SQL behind the scenes. No PromQL, no manual configuration — just ask and get results. - **Raw SQL Editor** — For analysts and engineers who want full control, Draxlr provides a powerful raw SQL editor that supports complex queries, joins, subqueries, and everything else you'd expect from a serious data tool. ### Key advantages of Draxlr over Grafana - **Flexible querying for every skill level** — Visual builder, AI querying, and raw SQL all in one place means no user is left behind and no use case is out of reach. - **Built for relational databases** — Unlike Grafana, which shines with time-series data (Prometheus, InfluxDB), Draxlr is optimized for the databases where most business data actually lives. - **Dashboard sharing without friction** — Share dashboards with stakeholders via a link or embed them directly inside your product. No Grafana authentication overhead. - **Embedded analytics** — Draxlr's embedded dashboard feature lets you ship analytics inside your SaaS product without building a separate reporting layer from scratch. - **No DevOps expertise required** — Setup takes minutes, not days. Connect your database, start building dashboards, and share insights immediately. - **Cleaner alerting** — Set up threshold-based alerts on your queries and get notified via Slack, email, or webhooks without the complexity of Grafana's alerting stack. - **Role-based access control** — Control who sees what at a granular level, making it safe to share data across teams with different permission requirements. ### Who should use Draxlr Product teams, growth teams, customer success managers, data analysts, and engineers, essentially any organization where multiple types of users need regular access to database-driven insights. It's also a natural fit for SaaS companies that want to embed analytics directly into their product without a dedicated data engineering investment. ### Pricing Free trial is available. Paid plans start from $75 per month. --- ## 2. Looker ### Best Grafana Alternative for Enterprise SQL Analytics **Looker** (now part of Google Cloud) is a powerful enterprise BI platform built around a proprietary data modeling layer called **LookML**. It's one of the most sophisticated Grafana alternatives for organizations that want a governed, SQL-first analytics platform at scale. ### Key features - LookML semantic layer for consistent, reusable data models across the organization - Deep Google Cloud and BigQuery integration - Embedded analytics with strong white-labeling support - Looker Studio (formerly Data Studio) for lightweight reporting - Strong governance, version control, and access controls - Scheduled reports and data delivery workflows ### Who should use Looker Large enterprises with dedicated data engineering teams that need a governed, scalable SQL analytics platform — especially those already invested in the Google Cloud ecosystem. ### Pricing Enterprise pricing, available on request. Included with some Google Cloud contracts. --- ## 3. Power BI ### Best Grafana Alternative for Microsoft Ecosystem Teams **Power BI** is Microsoft's widely adopted business intelligence platform and one of the most recognized Grafana alternatives in the market. For organizations already running on Microsoft infrastructure — Azure, Excel, Teams, SharePoint — Power BI slots naturally into existing workflows. ### Key features - Deep integration with Microsoft 365, Azure, and Excel - Drag-and-drop report and dashboard builder - DAX (Data Analysis Expressions) for advanced calculations - Power Query for data transformation and preparation - AI-powered insights and natural language Q&A - Copilot integration for AI-assisted report building - Strong mobile experience ### Who should use Power BI Teams and enterprises already in the Microsoft ecosystem that want a well-supported BI tool with familiar, Excel-like functionality. ### Pricing Power BI Pro starts at $10/user/month. Often included in Microsoft 365 E5 plans. --- ## 4. Sisense ### Best Grafana Alternative for Embedded Analytics in SaaS Products **Sisense** is an analytics platform designed for embedding data experiences inside external-facing products. For teams whose primary goal is delivering analytics to their own customers, Sisense is one of the more mature and capable Grafana alternatives available. ### Key features - Embedded analytics with deep white-labeling support - Compose SDK for building custom analytics experiences - AI-powered insights and anomaly detection - Flexible deployment — cloud, on-premise, or hybrid - Multi-tenant architecture designed for SaaS products - API-first design for developer customization ### Who should use Sisense SaaS companies and ISVs that need to embed customized, customer-facing analytics experiences inside their own products at scale. ### Pricing Enterprise pricing, available on request. --- ## 5. Domo ### Best Grafana Alternative for Business-Wide Data Collaboration **Domo** is a cloud-based BI and data platform designed to make data accessible across an entire organization — from the C-suite to frontline teams. It combines dashboards, data pipelines, and collaboration tools in a single platform, making it a broad-purpose Grafana alternative for business-focused teams. ### Key features - 1,000+ pre-built data connectors - Business-friendly drag-and-drop dashboard builder - Domo Bricks for custom app and visualization development - Built-in data transformation with Magic ETL - Strong mobile experience for executives on the go - Collaboration features including alerts, annotations, and comments - AI-powered insights and forecasting ### Who should use Domo Mid-to-large organizations that want a single platform for data pipelines, dashboards, and business-wide collaboration — and have the budget to support it. ### Pricing Custom pricing based on users and data volume. Available on request. --- ## 6. Holistics ### Best Grafana Alternative for Data Teams Who Love SQL Modeling **Holistics** is a self-service BI platform with a SQL-first philosophy and a built-in semantic modeling layer. It has built a strong reputation among data teams — particularly in Asia-Pacific — as a well-designed Grafana alternative for analyst-led organizations that prioritize data consistency and governance. ### Key features - SQL-based semantic modeling layer - Self-service dashboard builder for non-technical users - Version-controlled data models using a Git-based workflow - Scheduled reports and email delivery - Row-level security and strong access controls - Caching layer for query performance optimization ### Who should use Holistics Data analyst teams that want a SQL-first BI platform with strong modeling, governance, and self-serve capabilities — and are willing to invest in building a semantic layer upfront. ### Pricing Starts at $100/month. Free trial available. --- ## 7. Apache Superset ### Best Free Open Source Grafana Alternative for BI **Apache Superset** is a powerful, free, open-source data exploration and visualization tool that's a strong Grafana alternative for business intelligence use cases. It supports a wide range of databases and offers an advanced chart library. ### Key features - Supports 40+ SQL databases - Rich visualization library (60+ chart types) - Role-based access control - SQL Lab for advanced querying - Active open-source community ### Who should use Apache Superset Data and engineering teams at organizations that are comfortable managing open-source infrastructure and want a free, capable BI tool for SQL-based analytics and reporting. ### Pricing Free and open-source. Managed hosting options are available through third-party providers. --- ## 8. Tableau ### Best Grafana Alternative for Enterprise Data Visualization **Tableau** is one of the most powerful and widely recognized data visualization platforms in the world. For organizations that need enterprise-grade visualizations, deep data blending, and executive-level dashboards, Tableau is a premium Grafana alternative. ### Key features - Advanced drag-and-drop visualization - Connects to virtually any data source - Tableau Prep for data transformation - Tableau Pulse for AI-driven insights - Strong governance and enterprise security ### Who should use Tableau Large enterprises and organizations with dedicated data analyst or BI teams that need advanced visualization, data storytelling, and strong governance capabilities across a wide range of data sources. ### Pricing Tableau Creator starts at $75/user/month. Viewer and Explorer licenses available at lower price points. Salesforce bundle pricing available for existing customers. --- ## Conclusion Grafana remains a capable open source tool for infrastructure monitoring and time-series visualization, but many teams need more than it offers. Whether your priority is AI-powered SQL analytics, simpler dashboards for business users, customer-facing embedded analytics, or richer BI features beyond observability, there is a Grafana alternative that fits your needs. For teams transitioning from Grafana toward business intelligence and SQL analytics, **Draxlr** delivers the strongest combination of AI-assisted querying, visual dashboard building, embedded analytics, and live database connectivity — all without the configuration complexity that makes Grafana challenging for non-technical users. We recommend trialing two or three Grafana alternatives before committing. Most platforms offer free trials or starter plans that let you test connectivity with your existing databases and evaluate the user experience firsthand. --- ## FAQs ### 1. What are the best Grafana alternatives in 2026? The best Grafana alternative depends on your team's specific needs. Key factors to consider are whether you need SQL-based analytics or infrastructure monitoring, how technical your users are, whether you need embedded analytics, and what your budget looks like. This guide covers the top options across each of these use cases. ### 2. Which Grafana alternative is best for business analytics and SQL? Look for a tool that connects natively to relational databases, supports flexible querying for users of different skill levels, and makes it easy to build and share dashboards without engineering involvement. AI-powered querying and visual query builders are strong indicators that a tool was built with business analytics in mind. ### 3. Which Grafana alternative is best for non-technical users? The best options for non-technical users offer a visual query builder or natural language querying, require no SQL knowledge to get started, and have an intuitive dashboard interface that doesn't assume a DevOps or engineering background. Easy sharing and collaboration features are also important for keeping non-technical stakeholders engaged with data. ### 4. Is Grafana free to use? Yes, Grafana's core open-source version is free to self-host. However, running Grafana in production comes with infrastructure and maintenance costs. Grafana Cloud offers a free tier with usage limits, while Grafana Enterprise adds advanced features at a significant licensing cost. ### 5. Which Grafana alternative works best for embedded analytics? For embedded analytics, look for tools with a multi-tenant architecture, white-labeling support, and a well-documented SDK or API. The key questions to ask are how much developer effort is required to embed dashboards, how customizable the end-user experience is, and how the tool handles row-level security for different customers.

Best Qlik Alternatives in 2026

hello@draxlr.com (Draxlr) — Tue, 24 Feb 2026 00:00:00 GMT

Qlik is powerful but it's not always the right fit. Whether it's the steep learning curve, high licensing costs, or the need for something more modern and lightweight, thousands of teams are searching for **Qlik alternatives** that better match how they actually work in 2026. In this blog, we've put together a definitive list of the top Qlik alternatives you can try. Let's dive in. --- ## Why Teams Look for Qlik Alternatives Qlik has long been recognized for its associative data engine and strong enterprise analytics. But in 2026, the BI landscape has shifted significantly. Teams now expect tools that are faster to set up, easier to use, and more affordable, especially as cloud-native, AI-powered platforms have raised the bar. Here are the most common reasons teams are actively searching for Qlik alternatives: - **High cost** — Qlik's licensing model can be prohibitive for small to mid-sized businesses. - **Steep learning curve** — QlikView and even Qlik Sense require significant training and technical expertise. - **Complex deployment** — Getting Qlik up and running across an organization can take weeks or months. - **Limited collaboration** — Qlik's collaboration features feel outdated compared to modern BI alternatives. - **AI capabilities** — Newer tools offer more native, intuitive AI-powered analytics out of the box. The good news? There are excellent Qlik alternatives in 2026 that address every one of these pain points. Here's what we found. --- ## Side-by-Side Comparison: The Top Qlik Alternatives in 2026 Here's how the top Qlik alternatives stack up across the most critical dimensions for modern BI teams:

Tool	Ease of Use	Pricing	AI Features	Embedded Analytics	Best For
Draxlr	⭐⭐⭐⭐⭐	💰 Affordable	✓	✓	All team sizes
Tableau	⭐⭐⭐	💰💰💰 High	✓	✓	Enterprise / Viz-heavy
Power BI	⭐⭐⭐⭐	💰💰 Mid	✓	Partial	Microsoft ecosystems
Looker	⭐⭐	💰💰💰 High	Partial	✓	Google Cloud / Enterprise
ThoughtSpot	⭐⭐⭐⭐	💰💰💰 High	✓	Partial	Search-first analytics
Sisense	⭐⭐	💰💰💰 High	✓	Partial	ISVs / Large-scale data

--- ## 1. Draxlr — Best Overall Qlik Alternative > **Top Pick — Best self-serve AI-powered Qlik alternative in 2026** **[Draxlr](https://www.draxlr.com/)** is rapidly becoming the go-to Qlik alternative for data-driven teams who want the power of enterprise analytics without the enterprise headache. Built for the modern data stack, Draxlr lets you connect your database, build dashboards, and surface insights all without needing deep BI expertise or weeks of onboarding. Where Qlik demands heavy configuration and dedicated admin resources, Draxlr gets you from data to dashboard in minutes. Its clean, intuitive interface is designed for both technical and non-technical users, making it ideal for cross-functional teams. Draxlr connects directly to your SQL databases — PostgreSQL, MySQL, Redshift, BigQuery, Databricks, and more — with zero ETL friction. Its **AI-assisted** query builder means even non-SQL users can explore data independently, dramatically reducing the load on data engineering teams. ![Draxlr](/images/blogs/_tools/draxlr.png) ### Why Draxlr Stands Out as a Qlik Alternative Unlike Qlik, which requires significant setup and dedicated admins, Draxlr is self-serve from day one. Its pricing is transparent and accessible, making it a compelling choice for startups, scale-ups, and enterprises alike. The platform supports role-based access control, real-time query execution, scheduled slack and email reports, and embeddable dashboards all features that typically come at a premium in Qlik. For teams that have struggled with Qlik's performance on large datasets, Draxlr's direct-to-warehouse architecture delivers fast query execution without data duplication or complex in-memory processing. **Pros:** - Fast, frictionless setup — live in minutes - Direct SQL database connectivity (PostgreSQL, MySQL, Redshift, BigQuery, Databricks, and more) - AI-assisted query builder - Multi-level drill-through — deep, layered data exploration in clicks - Pivot tables and advanced graphs — powerful data reshaping and visualization - Embeddable dashboards for SaaS products - Real-time query execution - Clean, intuitive UI with a low learning curve - Role-based access control and team collaboration - Summary rows — instant totals and aggregations on any table - Virtual columns — create computed fields without modifying your database - Transparent, affordable pricing - Quick customer support — fast, human responses (not just tickets) **Cons:** - Best suited for SQL-based data sources - Prioritizes core analytics capabilities without unnecessary feature overload **Best For:** Startups, scale-ups, and enterprise teams who want a modern, lightweight, and highly accessible Qlik alternative with direct database connectivity and AI-powered analytics. --- ## 2. Tableau **Best for Data Visualization** [Tableau](https://www.tableau.com/) remains one of the most recognized names in data visualization. Acquired by Salesforce, it offers rich, interactive dashboards and broad data source connectivity. Tableau is particularly well-suited for teams that need beautiful, presentation-ready charts and reports for executive stakeholders or clients. That said, Tableau comes with its own complexity and cost challenges. It's significantly more expensive than newer Qlik alternatives like Draxlr, and its data modeling capabilities can be difficult for non-technical users. For teams deeply embedded in the Salesforce ecosystem, however, it remains a strong option. **Pros:** - Industry-leading data visualization capabilities - Large community and extensive documentation - Strong Salesforce integration - Flexible deployment (cloud, on-prem, hybrid) **Cons:** - High licensing cost - Steep learning curve for advanced features - Slower performance on very large datasets - Limited self-serve experience for non-technical users **Best For:** Enterprise teams that prioritize polished, presentation-quality dashboards and are already in the Salesforce ecosystem. --- ## 3. Microsoft Power BI **Best for Microsoft Teams** [Power BI](https://www.microsoft.com/en-us/power-platform/products/power-bi) is Microsoft's flagship BI tool and a dominant force in the market. If your organization already relies on Microsoft 365, Azure, or Excel, Power BI integrates naturally and at a competitive price. It offers robust data modeling, interactive dashboards, and a wide range of connectors. The major trade-off is that Power BI works best within the Microsoft ecosystem — outside of it, you can run into integration friction. It also has a well-documented performance ceiling when dealing with very large or complex datasets without premium capacity. **Pros:** - Excellent Microsoft and Azure integration - Competitive pricing within Microsoft 365 subscriptions - Familiar Excel-like experience for business users - Large user base and a strong support community **Cons:** - Performance issues with large datasets without Premium - Vendor lock-in to the Microsoft ecosystem - Complex and confusing licensing tiers - Limited flexibility outside the Microsoft stack **Best For:** Organizations already running on Microsoft 365 or Azure that need a cost-effective BI tool with solid reporting capabilities. --- ## 4. Looker (Google) **Best for Google Cloud Users** [Looker](https://lookerstudio.google.com/overview), now under Google Cloud, is a powerful BI tool built around LookML — its proprietary data modeling language. It excels at embedded analytics and is a go-to for software companies and product teams that need to surface data insights inside their applications. Looker's main limitation is that LookML requires developer-level expertise to set up, making it not truly self-serve for most business users. It's also expensive, positioning it primarily as an enterprise play. For teams on Google Cloud, however, the integration is seamless and the real-time data access is a genuine differentiator. **Pros:** - Powerful embedded analytics capabilities - Excellent Google Cloud and BigQuery integration - Live connections to cloud data warehouses - Strong data governance and modeling via LookML **Cons:** - Requires LookML knowledge — not self-serve for business users - Very high cost — enterprise-only pricing - Heavy dependency on the Google Cloud ecosystem - Slow and complex to set up initially **Best For:** Enterprise engineering and product teams building embedded analytics on Google Cloud infrastructure. --- ## 5. ThoughtSpot **Best for AI-Powered Search Analytics** [ThoughtSpot](https://www.thoughtspot.com/) takes a unique approach to analytics by allowing users to type questions in natural language and get instant, AI-powered answers. Instead of building dashboards manually, users search their data the way they'd search Google. This makes it an excellent Qlik alternative for business users who struggle with traditional BI tools. ThoughtSpot's main limitation is cost and complexity at scale. It works best when data is well-structured, and like Looker, it targets enterprises with its pricing. For teams that need deep, exploratory analytics in a search-first format, it's a compelling option. **Pros:** - Natural language search delivers instant insights - AI-powered analytics that surface hidden trends - Strong cloud data warehouse integrations - Low friction for non-technical business users **Cons:** - High enterprise pricing - Less flexible for custom dashboard building - Requires well-structured, clean data to work effectively - Limited embedding options for SaaS products **Best For:** Enterprise business teams that want a natural language, search-first analytics experience without writing queries. --- ## 6. Sisense **Best for Large-Scale Embedded Analytics** [Sisense](https://www.sisense.com/) is a robust, developer-friendly BI platform built for organizations that need to handle large, complex datasets and embed analytics deeply into their own products. It's particularly popular among ISVs and SaaS companies that require highly customizable, white-labeled dashboards served to their end customers. Where Sisense shines is in its scalability and backend flexibility — it can ingest and process massive volumes of data from disparate sources, making it one of the more powerful Qlik alternatives for technical teams. That said, the complexity and cost that come with this power mean it's not the right fit for teams looking for a quick, self-serve solution. Setup typically requires dedicated developer involvement, and pricing firmly places it in the enterprise tier. **Pros:** - Excellent scalability for large, complex datasets - Strong embedded and white-label analytics capabilities - Flexible data modeling and custom logic support - Wide range of data source integrations **Cons:** - Requires technical expertise to set up and maintain - High enterprise pricing — not suited for smaller teams - Steeper learning curve compared to modern alternatives like Draxlr - Slower to deploy than lighter-weight BI tools **Best For:** Technical teams at ISVs and SaaS companies that need a highly scalable, deeply embeddable BI engine for large-scale data workloads. --- ## Conclusion: Which Qlik Alternative Should You Choose? The right Qlik alternative in 2026 depends on your team's size, technical capabilities, and budget. Here's a quick breakdown: **Best Overall Qlik Alternative — Draxlr** Our top recommendation for most teams. Draxlr combines ease of use, direct SQL database connectivity, AI-powered querying, and transparent pricing — everything Qlik lacks for small to mid-sized teams. Whether you're a startup analyst or an enterprise data team lead, Draxlr gets you to insights faster with far less friction. **Best for Microsoft-Heavy Organizations — Power BI** The natural choice if your team is deeply embedded in the Microsoft ecosystem. Cost-effective within Microsoft 365 subscriptions and handles most standard BI use cases well. **Best for Executive-Facing Visualizations — Tableau** The gold standard when polished, publication-quality dashboards are the primary requirement — especially for client-facing or executive reporting. **Best for Large-Scale Embedded Analytics — Sisense** Ideal for ISVs and SaaS companies that need a highly scalable, deeply embeddable BI engine and have the technical resources to implement and maintain it. --- ## FAQs ### 1. What are the best Qlik alternatives in 2026? For small to mid-sized businesses, Draxlr is the top choice. It offers a powerful feature set, AI-assisted querying, and transparent pricing without the enterprise complexity or cost that comes with Qlik, or Tableau. ### 2. Which Qlik alternative is best for AI powered analytics? The best Qlik alternative for AI powered analytics is a platform that includes Text to SQL, conversational AI chat, automated insights, and intelligent data exploration. These capabilities reduce manual reporting effort and help teams generate faster, data driven decisions without heavy technical involvement. ### 3. Which Qlik alternative is best for non-technical users? Look for platforms with natural language querying, intuitive drag-and-drop interfaces, and spreadsheet-like familiarity. Avoid tools requiring coding or complex data modeling. ### 4. Which Qlik alternative is best for small businesses? Prioritize tools with transparent, consumption-based or per-user pricing, quick implementation, and no requirement for dedicated BI administrators. Scalability as you grow is also key. ### 5. Will switching from Qlik be complicated? It depends on the complexity of your existing setup. Most modern BI platforms connect directly to your existing data sources, so you don't need to move your data. The main effort involves recreating dashboards and reports, which typically takes days to a few weeks depending on volume. ### 6. What's the cheapest Qlik alternative? Look for platforms with consumption-based or low per-user pricing models. Open-source options can reduce licensing costs further, though they may require more setup and self-hosting effort.

Best ThoughtSpot Alternatives in 2026

hello@draxlr.com (Draxlr) — Wed, 18 Feb 2026 00:00:00 GMT

If you're researching ThoughtSpot alternatives, you're likely facing a critical business decision. Maybe your organization has outgrown ThoughtSpot's constraints, discovered unexpected costs hidden in the fine print, or realized the platform doesn't align with your team's technical maturity. Whatever the reason, you're not alone because many companies evaluate ThoughtSpot alternatives every year, searching for BI tools that deliver power without the complexity and surprise billing. The analytics software market has evolved substantially since ThoughtSpot's emergence. Contemporary alternatives address gaps in ease of use, pricing transparency, and integration with modern data infrastructure. This guide examines viable alternatives for organizations with varied requirements like SaaS companies requiring [embedded analytics](/embedded-analytics-tool/), Microsoft-integrated enterprises, businesses managing complex data governance, and cost-constrained teams. --- ## Why consider a ThoughtSpot alternative? Understanding why companies search for ThoughtSpot alternatives is the first step in finding the right solution. Below, we dive into the specific reasons why your peers are making the switch and why you might be too. ### 1. Pricing surprises and cost overruns While ThoughtSpot advertises starting at $50 per user per month, real-world deployments paint a different picture. Per-query charges, data volume thresholds, and embedded analytics licensing can quickly escalate costs. Organizations often find themselves locked into $100,000+ annual contracts that expand unpredictably as usage grows. ### 2. Steep learning curve ThoughtSpot's power comes with complexity. The platform requires well-structured data models and significant IT involvement to set up properly. Business users expecting to jump in and create analyses quickly often hit friction, requiring formal training and IT handholding that slows adoption. ### 3. Limited visualization and extensibility Compared to modern alternatives like Tableau, ThoughtSpot offers fewer visualization options and limited customization capabilities. If your team needs highly tailored charts or interactive visualizations, you'll find other tools more flexible. ### 4. Embedding challenges For SaaS companies building customer-facing analytics, ThoughtSpot's iframe-based embedding doesn't feel native to applications and lacks the white-label capabilities that newer tools offer. This forces compromises between functionality and user experience. ### 5. Vendor lock-in and limited portability Migrating logic and analyses from ThoughtSpot to another platform is labor-intensive. Your "answers" don't export cleanly, meaning significant rework when switching tools. --- ## What to look for in ThoughtSpot alternatives When evaluating a ThoughtSpot alternative, consider these key criteria: - **Ease of Use & Self-Service:** Can business users create reports and dashboards without IT dependency? Can the platform handle both simple and advanced use cases? - **Transparent Pricing:** Does the vendor offer clear, predictable pricing that scales with your usage? Are there surprise costs lurking in the fine print? - **AI & Advanced Features:** Does the platform offer machine learning for automated insights? Natural language query capabilities? - **Data Integration & Flexibility:** How well does it work with your data warehouse (Snowflake, BigQuery, Redshift, etc.)? Can it connect to all your data sources? - **Visualization & Customization:** Does it offer rich chart types and the ability to customize dashboards for your brand? - **Embedded Analytics Quality:** If you're a SaaS company, how native does the embedded experience feel? Can you white-label it? - **Support & Onboarding:** Is responsive support available? Are there comprehensive training resources and documentation? - **Deployment Flexibility:** Can you deploy on-premises, in the cloud, or hybrid? What about security requirements? --- ## The best ThoughtSpot alternatives for 2026 at a glance

Platform	Best For	Ease of Use	Visualization	Pricing	Embedded Analytics
Draxlr	AI-powered self-service analytics	Excellent	Very Good	Transparent, Flexible	Excellent
Tableau	Advanced analysts, visualization	Moderate	Excellent	Per-user, Higher cost	Good
Power BI	Microsoft organizations	Very Good	Good	Per-user	Good
Looker	Complex governance needs	Moderate	Very Good	Higher, Enterprise	Very Good
Sigma	Spreadsheet-familiar teams	Excellent	Good	Usage-based, Flexible	Good
Domo	Enterprise AI needs	Moderate	Very Good	Enterprise, Higher cost	Very Good

--- ## 1. Draxlr ### Best for AI-powered self-service analytics **Why Draxlr tops our list:** Draxlr is purpose-built for organizations that want analytics without the complexity and cost of legacy platforms. It combines the self-service capabilities of ThoughtSpot with the ease of use of modern SaaS tools, all at transparent pricing that won't surprise you. **Best for:** SaaS companies, fast-growing teams, organizations prioritizing ease of use and predictable costs, companies needing embedded analytics. Connect your Database ### Key Strengths - **User-Friendly:** Non-technical users can create dashboards and reports in minutes, not days. The interface is intuitive without sacrificing power. - **Transparent, Predictable Pricing:** No surprise per-query charges or hidden costs. You know exactly what you're paying. Starts at accessible price points for small teams and scales cleanly. - **Powerful Embedded Analytics:** Native white-label embedding designed for SaaS. Your customers see your brand, not Draxlr's. Deep API access for full customization. - **AI-Driven Insights:** Natural language query capabilities help users ask questions their own way. - **Modern Data Stack Integration:** Seamless connectors to Snowflake, BigQuery, Redshift, Databricks, and many other data sources. Real-time data refresh options. - **Fast Deployment:** Get dashboards live in days, not months. No complex ETL pipelines required before you start seeing value. - **Exceptional Support:** Dedicated support teams, comprehensive documentation, and an active community. Your success is their priority. ### Considerations - Prioritizes core analytics capabilities without unnecessary feature overload. - Built around direct SQL database connectivity, allowing teams to work with live data sources. ### Pricing **Premium:** $75/month (2 databases, 20 users, 100 AI credits, chat support)
**Power:** $125/month (50 databases, 50 users, 300 AI credits, dedicated server, SSO, audit logs)
**Self-Host:** $500/month billed annually (Docker containers, custom AI models, full control)
**Free Trial:** 7 days with full access to Power plan. No credit card required.
> **Interested in Draxlr?** See why leading companies choose Draxlr for modern analytics. [Start Your Free Trial](https://app.draxlr.com/register/) --- ## 2. Tableau ### Best for Advanced Data Storytelling Tableau remains the industry standard for sophisticated data visualization and interactive reporting. If your team has strong data analysis skills and needs publication-quality visualizations, Tableau delivers. **Best for:** Data analysts, enterprises with large BI teams, organizations prioritizing stunning visualizations, companies with established data cultures. ### Key Strengths - Industry-leading visualization capabilities with unmatched interactivity - Strong community and extensive training ecosystem - Robust governance and security features for enterprises - Excellent mobile experience and dashboard sharing ### Considerations - Steep learning curve for non-technical users, requires analyst intermediaries - Higher total cost of ownership compared to alternatives - Slower time to insight due to complex data preparation workflows ### Pricing $70/month per Creator, $35/month per Explorer. Additional data source fees. Enterprise contracts start at $150K+. --- ## 3. Microsoft Power BI ### Best for Microsoft-Integrated Organizations If your organization runs on Microsoft's ecosystem (Office 365, Azure, SQL Server), Power BI offers tight integration and familiar authoring experience. It's become increasingly competitive in the mid-market segment. **Best for:** Microsoft-centric organizations, companies with Excel-heavy workflows, mid-market enterprises, teams prioritizing Microsoft ecosystem integration. ### Key Strengths - Seamless integration with Excel, Office 365, and Azure services - Competitive pricing compared to enterprise alternatives - Growing visualization library and AI capabilities (Quick Insights) - Strong adoption among business users familiar with Office ### Considerations - Limited visualization sophistication compared to Tableau - Can require significant Power Query knowledge for data prep - Embedded analytics capabilities lag behind specialized tools ### Pricing $10-30/month per user for Pro licenses. Premium capacity starts at $5,000/month. Premium for Embedded at $4,995/month. --- ## 4. Google Looker ### Best for Complex Data Governance Looker (now Google Looker) brings a developer-first approach to BI. If your team needs sophisticated data modeling and strict governance, Looker provides powerful semantic layers and role-based security. **Best for:** Organizations with strong data teams, enterprises needing complex governance, companies with existing Google Cloud investments, data-forward organizations. ### Key Strengths - Powerful semantic layer (LookML) for centralized data modeling - Enterprise-grade security, governance, and compliance features - Excellent for large organizations with complex data architectures - Strong Google Cloud integration and modern architecture ### Considerations - Steep learning curve, requires significant developer involvement - Not ideal for business user self-service analytics - Higher implementation costs and longer time to ROI ### Pricing $2,000-$8,000/month depending on user count and features. Implementation and support additional. --- ## 5. Sigma Computing ### Best for Spreadsheet-Like Familiarity Sigma bridges the gap between spreadsheets and modern BI. If your team already uses spreadsheets and wants a direct cloud alternative, Sigma provides familiar interfaces with enterprise-grade capabilities. **Best for:** Teams coming from spreadsheet-heavy workflows, organizations seeking spreadsheet familiarity with BI power, mid-market companies, business users preferring traditional table layouts. ### Key Strengths - Spreadsheet-familiar interface reduces learning curve - Flexible pricing with transparent cost structure - Works directly with modern cloud data warehouses - Good balance of ease-of-use and analytic power ### Considerations - Less sophisticated visualization options than Tableau - Smaller community and ecosystem compared to established platforms - Limited advanced analytics and AI capabilities ### Pricing Usage-based pricing. Free tier available. Typically $100-500/month depending on usage. --- ## 6. Domo ### Best for Enterprise and Complexity Domo targets enterprise customers who need advanced AI capabilities, complex integrations, and comprehensive dashboarding. It's a full-featured platform for organizations with mature data practices. **Best for:** Large enterprises, organizations needing advanced AI, companies requiring custom integrations, data-heavy organizations with complex architectures. ### Key Strengths - Advanced AI and machine learning capabilities - Extensive integration library (1,000+ connectors) - Strong enterprise features and scalability - Comprehensive data governance and security ### Considerations - High complexity requires dedicated BI teams - Steep learning curve and implementation costs - Pricing can be expensive for smaller organizations ### Pricing Enterprise pricing starting at $10K+/month. Custom quotes based on requirements. --- ## Finding the Right Alternative for Your Use Case ### For SaaS Companies (Customer-Facing Analytics) **Top Choice: Draxlr** If you're building customer-facing analytics, Draxlr stands out with its native white-label embedding, powerful customization capabilities, and API-first architecture. Your customers see your brand, your experience, and your support with Draxlr's technology working invisibly behind the scenes. The transparent pricing also makes it easy to build predictable analytics revenue models. ### For Microsoft-Heavy Organizations **Top Choice: Microsoft Power BI** If your team lives in Office 365 and Azure, Power BI offers unmatched integration. Your team can author in Excel, Power Query, and familiar Microsoft tools. The pricing is also competitive for organizations already committed to Microsoft's ecosystem. ### For Cost-Conscious Teams **Top Choice: Draxlr or Power BI** Draxlr and Power BI both offer significantly better pricing than ThoughtSpot's enterprise contracts. Choose **Draxlr** if you need transparent, flexible pricing with ease of use—starting at $75/month for small teams with no surprise costs. Choose **Power BI** if your organization already uses Microsoft services; licensing at $10-30/month per user provides strong value within the Microsoft ecosystem. Both beat ThoughtSpot's cost structure substantially while delivering superior user experiences. ### For Advanced Data Storytelling **Top Choice: Tableau** If your organization publishes sophisticated, interactive reports for consumption across the company, Tableau's visualization and storytelling capabilities remain unmatched. Your analysts can create publication-quality dashboards that rival custom applications. --- ## How to Migrate from ThoughtSpot to an Alternative ### 1. Data Migration Most modern BI platforms connect directly to your data warehouse without needing a complete export from ThoughtSpot. Your data model stays intact, and queries run against the same underlying sources. ### 2. Logic and Analysis Migration This is the most labor-intensive step. ThoughtSpot "Answers" don't export cleanly to other platforms. You'll need to manually rebuild analyses, though platforms with AI (like Draxlr) can significantly reduce this burden by learning from your database structure and suggesting common analyses. Alternatively, tools like Draxlr offer professional services to handle the entire migration for you as a one-time project fee, building all your dashboards and analyses in the new platform so your team can focus on adoption rather than rebuilding. ### 3. Training and Change Management Plan for user training, especially if switching to a platform with different authoring paradigms. Platforms like Draxlr, Sigma, and Power BI have gentler learning curves than Tableau or Looker, requiring less formal training. ### 4. Phased Implementation Strategy Don't try to migrate everything at once. Start with a pilot team, validate the new platform, then gradually migrate additional teams. This approach minimizes disruption and lets you optimize your process. ### 5. Licensing and Cost Optimization As you migrate, right-size your user tiers. Many organizations discover they can achieve more with fewer expensive Creator licenses and more affordable Viewer licenses once they switch to intuitive platforms. --- ## Conclusion: Your path forward Selecting the right ThoughtSpot alternative requires evaluating your organization's specific requirements across several dimensions. Consider your team's technical maturity, budget constraints, integration needs, and timeline for deployment. Modern BI platforms have converged on similar core capabilities such as data connectivity, visualization, and self-service authoring but differentiate through ease of use, pricing structure, AI capabilities, and specialized features like embedded analytics. Most platforms offer trial periods—use them to validate whether the platform matches your team's workflows and technical reality before committing to a long-term contract. --- ## FAQs ### 1. Can I migrate my ThoughtSpot dashboards to another platform? Direct dashboard migration isn't possible since each platform has different data models and query languages. However, some platforms support building it for you with a one-time fee project. ### 2. What are the best ThoughtSpot alternatives in 2026? The best ThoughtSpot alternative depends on your organization’s priorities, technical maturity, and budget. Options like Draxlr, Power BI, Tableau, Looker, and Sigma each cater to different needs, from AI-powered self-service analytics to enterprise visualization and governed reporting. ### 3. Which ThoughtSpot alternative is best for AI-powered analytics? AI capabilities vary across ThoughtSpot alternatives. Some embed AI for non-technical users with anomaly detection and natural language queries. Others offer advanced machine learning for data science teams. Choose based on whether you need intuitive AI for business users or sophisticated modeling for specialists. ### 4. Which ThoughtSpot alternative is best for small businesses? For small businesses, the best ThoughtSpot alternative is one that combines ease of use, affordable pricing, and minimal setup complexity. Platforms like Draxlr, Power BI, and Sigma are often considered suitable options because they offer self-service analytics, scalable pricing, and the flexibility to grow as the business expands.

Best Apache Superset Alternatives in 2026

hello@draxlr.com (Draxlr) — Sat, 14 Feb 2026 00:00:00 GMT

Finding the right Apache Superset alternative can make a significant difference in how your team builds dashboards, explores data, and delivers analytics across your organization or product. While Apache Superset is a popular open-source business intelligence tool, it may not suit every company's needs in terms of ease of deployment, user accessibility, AI capabilities, enterprise support, or long-term maintainability. Many teams start with Apache Superset for its open-source flexibility but later look for Apache Superset alternative tools that offer simpler setup, better experiences for non-technical users, stronger embedded analytics, or managed infrastructure that eliminates operational overhead. As your analytics requirements grow, having the right BI platform becomes critical to avoid engineering bottlenecks and mounting maintenance costs. If you are actively searching for a strong Apache Superset alternative in 2026, this guide will help you evaluate the best options available. --- ## Why consider an Apache Superset alternative? While Apache Superset provides a powerful open-source foundation for business intelligence and data visualization, several factors might push your organization toward exploring other options. Understanding these pain points can help you determine if switching to an Apache Superset alternative makes sense for your business. ### 1. High maintenance and operational overhead Apache Superset requires dedicated engineering resources to deploy, configure, scale, and maintain. Self-hosting means managing infrastructure, database backends, caching layers, security patches, and version upgrades. Many Apache Superset alternatives offer fully managed solutions that eliminate this operational burden entirely. ### 2. Steep learning curve for non-technical users Superset was built with data engineers and analysts in mind. Business users without SQL knowledge or technical backgrounds often find the interface overwhelming. Teams frequently explore Apache Superset alternatives that provide more intuitive drag-and-drop interfaces, visual query builders, or AI powered natural language querying. ### 3. Limited AI and automation capabilities Modern BI tools are moving beyond static dashboards. Teams often explore Apache Superset alternatives that provide AI driven query generation, text to SQL features, and automated insights to speed up dashboard creation and reduce dependency on SQL expertise. ### 4. Embedded analytics complexity While Superset supports embedding through iframes and its API, achieving production-grade embedded analytics with proper multi-tenancy, white labeling, and granular access controls requires significant custom engineering. A strong Apache Superset alternative should provide embedding capabilities without heavy development effort. ### 5. Limited alerting and monitoring Superset's built-in alerting capabilities are basic compared to commercial BI platforms. Organizations needing advanced data change alerts, threshold notifications, or Slack and email integrations often find themselves building custom solutions on top of Superset. ### 6. Enterprise support and governance gaps As an open-source project, Superset relies on community support. Organizations requiring guaranteed SLAs, dedicated support teams, compliance certifications, or advanced access controls may find the lack of formal enterprise support a significant limitation. ### 7. Scaling challenges under heavy workloads While Superset can handle large datasets, scaling it for high-concurrency environments with many concurrent users and complex queries requires careful infrastructure tuning. Many Apache Superset alternatives handle scaling automatically without manual intervention. --- ## What to look for in an Apache Superset alternative Selecting a replacement BI platform requires evaluating multiple dimensions. Here's what to prioritize when assessing alternatives: ### 1. Ease of use and accessibility This should rank high on your list. The best BI tool balances power with simplicity, enabling both technical analysts and business users to create insights without extensive training. Look for platforms with drag-and-drop interfaces, natural language query (AI) capabilities, and intuitive design. ### 2. Deployment and maintenance simplicity Determine whether the platform eliminates operational overhead. Assess whether you need to manage infrastructure, handle upgrades, or configure scaling. Fully managed cloud solutions can free your engineering team to focus on product work instead of BI maintenance. ### 3. Advanced analytical features Distinguish premium platforms from basic ones. Consider whether you need AI-driven insights, advanced charts, intelligent alerts, or more. Your future analytics roadmap should influence this decision. ### 4. Transparent, flexible pricing This prevents budgetary surprises. Compare total cost of ownership across different pricing models — per-user, per-query, or consumption-based. While Superset itself is free, factor in infrastructure, engineering time, and opportunity costs when calculating true TCO. ### 5. Embedded analytics and white labeling SaaS companies often require full branding control, granular row-level security, and advanced permission management. A strong Apache Superset alternative should provide embedding flexibility without heavy engineering effort. ### 6. Vendor stability and support quality Research the company's product roadmap and customer support reputation. Responsive support teams can make a significant difference during implementation and scaling phases. ### 7. Database connectivity and integrations These are essential for seamless operations. Your ideal Apache Superset alternative should connect effortlessly with your data warehouse, cloud storage, or any other critical systems. Evaluate the breadth of pre-built connectors and API flexibility. ### 8. Alerting and real-time monitoring Look for platforms with built-in alerting on data changes, scheduled reports, and integrations with communication tools like Slack and email. Proactive monitoring reduces the time between data changes and team awareness. --- ## Best Apache Superset alternatives in 2026 at a glance

Tool	Best use case	Ease of use	AI capabilities	Self-hosted option
Apache Superset	Open source BI for data teams	Low	None	Yes
Draxlr	AI powered self-service analytics	High	Strong	Yes
Tableau	Enterprise visualization	Medium	Moderate	Yes
Microsoft Power BI	Microsoft ecosystem analytics	Medium	Moderate	No
Looker	Data modeling and governed analytics	Low	Moderate	No
Domo	Cloud BI with data integration	Medium	Moderate	No
Sigma	Spreadsheet style cloud analytics	High	Limited	No

--- ## 1. Draxlr ### AI powered analytics built for teams moving away from complex open-source setups ![Draxlr](/images/blogs/_tools/draxlr.png) [Draxlr](https://www.draxlr.com/) stands out as a top Apache Superset alternative for teams that want the depth of SQL analytics without the DevOps headache. Where Superset demands infrastructure provisioning, Redis caching, and manual scaling, Draxlr lets you connect a database and start building dashboards in minutes. Its AI query assistant turns plain English questions into working SQL, which is a game-changer for organizations where not every stakeholder writes queries. Teams migrating from Superset consistently highlight the reduction in setup time and the ability to onboard non-technical users without training sessions. Connect your Database ### Key Features - AI powered Text-to-SQL that converts natural language into optimized queries — no SQL expertise needed - Three dashboard building modes: Visual Query Builder, AI chat, and a full raw SQL editor for power users - Direct connections to MySQL, PostgreSQL, SQL Server, ClickHouse, BigQuery, Databricks, and more — no ETL pipelines required - Embeddable dashboards with white labeling and row-level security for SaaS products - Scheduled Slack and email alerts triggered by data changes or threshold breaches - Interactive filters, drill-downs, and cross-chart filtering on live data - Zero infrastructure management — no Docker containers, no Celery workers, no Redis to maintain - Role-based access controls and team workspaces for multi-department analytics ### Pricing Draxlr uses a **consumption-based pricing** model that starts affordable for small teams and scales predictably as usage grows. There are no per-seat surprises — you pay for what you use. Startup-friendly plans cover core analytics needs, while enterprise tiers unlock dedicated support, SSO, and custom integrations. A **self-hosted deployment** option is available for teams that need on-premise control, delivering Superset-like flexibility without the ongoing maintenance cost. --- ## 2. Tableau ### Industry-standard visualization for data-heavy enterprises ![Tableau](/images/blogs/apache-superset-alternatives-2026/tableau.png) [Tableau](https://www.tableau.com/) is often the first name that comes up when organizations outgrow open-source BI tooling. Unlike Superset, which requires building visualizations through a web UI with limited chart customization, Tableau offers a desktop-class authoring experience with granular control over every visual element. Its strength lies in exploratory analytics — analysts can drag dimensions and measures onto a canvas and discover patterns through visual iteration. For teams that found Superset's charting options restrictive or ran into rendering limitations with complex datasets, Tableau provides a significant upgrade in visualization depth. ### Key Features - Desktop and web authoring with pixel-level control over chart design and layout - VizQL engine that translates visual selections into optimized database queries automatically - Native connectors to virtually every major database, warehouse, and cloud platform - Tableau Prep for visual data cleaning and transformation workflows - Built-in statistical models, trend lines, clustering, and forecasting - Tableau Server or Tableau Cloud for centralized publishing, scheduling, and governance ### Pricing Tableau licenses are sold per user across Creator, Explorer, and Viewer tiers. Creator licenses (needed for dashboard authoring) start at a premium, and costs add up quickly for large teams. Compared to Superset's zero licensing fee, the jump in spend is significant — but many teams find the trade-off worthwhile when factoring in reduced engineering hours and faster dashboard delivery. --- ## 3. Power BI ### The go-to Apache Superset alternative for Microsoft-centered teams ![Power BI](/images/blogs/apache-superset-alternatives-2026/power-bi.png) [Power BI](https://powerbi.microsoft.com/) is a natural migration path for teams running on Azure, SQL Server, or the broader Microsoft 365 stack. Where Superset requires you to wire up database connections and configure authentication manually, Power BI pulls data directly from Excel files, SharePoint lists, Azure Synapse, and Dataverse with minimal configuration. Its DAX formula language gives analysts modeling power that goes well beyond what Superset's SQL Lab offers, and the tight integration with Teams means dashboards live where your team already collaborates. ### Key Features - One-click connectivity to Azure services, SQL Server, Excel, SharePoint, and Dynamics 365 - DAX-powered data modeling with calculated columns, measures, and time intelligence functions - Natural language Q&A that lets business users type questions and receive auto-generated charts - Power BI Embedded SDK for integrating reports directly into custom web applications - Row-level security, sensitivity labels, and Microsoft Entra ID integration for enterprise governance - Paginated reports for pixel-perfect financial statements and operational printouts ### Pricing Power BI Pro starts at $10 per user per month, making it one of the most affordable commercial options. However, embedded analytics and high-concurrency workloads require Premium Per Capacity plans that start significantly higher. When comparing total cost against a self-hosted Superset instance, Power BI frequently comes out cheaper once you account for infrastructure and engineering overhead. --- ## 4. Looker ### Governed, model-driven analytics for data engineering teams ![Looker](/images/blogs/apache-superset-alternatives-2026/looker.png) [Looker](https://cloud.google.com/looker), now part of Google Cloud, takes a fundamentally different approach than Superset. Instead of writing ad-hoc SQL queries for each dashboard panel, Looker uses LookML — a version-controlled modeling language that defines metrics, dimensions, and relationships once and reuses them everywhere. This eliminates the metric inconsistency problem that plagues Superset deployments where different teams write slightly different SQL for the same KPI. For engineering-led organizations running on BigQuery or Snowflake, Looker enforces a single source of truth that Superset's freeform SQL Lab cannot match. ### Key Features - LookML semantic layer that centralizes business logic and prevents metric drift across teams - Git-integrated development workflow for version control, code review, and CI/CD on analytics definitions - Looker API and Action Hub for triggering workflows, sending data to Slack, or pushing results to external systems - Embedded analytics via iframes or the Looker Embed SDK with SSO and content filtering - Native BigQuery optimization with awareness of partitioning, clustering, and BI Engine acceleration - Granular content access, row-level permissions, and field-level security tied to user attributes ### Pricing Looker pricing is negotiated per contract and typically based on user count plus platform usage. It is positioned firmly in the mid-market to enterprise segment. Smaller teams exploring Superset alternatives may find the cost prohibitive, but organizations with complex governance needs often see the investment pay for itself through reduced data inconsistency and faster self-service adoption. --- ## 5. Domo ### All-in-one cloud platform replacing your BI stack, not just Superset ![Domo](/images/blogs/apache-superset-alternatives-2026/domo.png) [Domo](https://www.domo.com/) goes beyond replacing Superset — it aims to consolidate your entire data pipeline. Where a typical Superset setup requires separate tools for ETL (like Airflow), data warehousing, and visualization, Domo bundles data ingestion, transformation, storage, and dashboarding into a single cloud platform. This makes it particularly appealing to operations teams and executives who want real-time KPI dashboards without depending on data engineering to stitch together multiple open-source components. ### Key Features - Over 1,000 pre-built connectors for SaaS apps, databases, cloud storage, and APIs - Magic ETL — a no-code data transformation pipeline builder with drag-and-drop logic - Domo Appstore with pre-built dashboard templates for common business functions - Buzz — a built-in messaging and collaboration layer tied directly to dashboard cards - Mobile-first design with a fully featured iOS and Android app for on-the-go analytics - Domo Everywhere for embedding analytics into external applications and partner portals ### Pricing Domo uses custom contract pricing that varies by user count, data volume, and connector requirements. It is generally positioned toward mid-market and enterprise buyers. The all-in-one approach can reduce total spend versus running Superset plus Airflow plus a warehouse, but individual licensing costs are higher than most point solutions. --- ## 6. Sigma Computing ### The spreadsheet-native alternative for teams that think in rows and columns ![Sigma Computing](/images/blogs/apache-superset-alternatives-2026/sigma.png) [Sigma Computing](https://www.sigmacomputing.com/) bridges the gap between the power of a cloud data warehouse and the familiarity of a spreadsheet. While Superset requires users to write SQL or navigate a chart builder UI, Sigma presents warehouse data in a live spreadsheet grid where business users can sort, filter, pivot, and build formulas the same way they would in Excel or Google Sheets. Every action generates optimized SQL executed directly on Snowflake, BigQuery, or Databricks — no data extracts, no CSV exports, no stale snapshots. For organizations where finance, marketing, or operations teams built mission-critical spreadsheets that Superset could never replace, Sigma offers a familiar entry point into warehouse-scale analytics. ### Key Features - Live spreadsheet interface that runs queries directly on your cloud warehouse in real time - Familiar formula bar supporting Excel-compatible functions alongside warehouse-specific operations - Workbook-based collaboration with version history, comments, and shared editing - Input tables that let users write data back to the warehouse for planning and forecasting workflows - Embeddable workbooks with tenant-aware filtering for customer-facing analytics - No data movement — all computation happens at the warehouse layer, keeping data governance intact ### Pricing Sigma offers per-user pricing with tiers for viewers, contributors, and creators. Enterprise plans include advanced governance, custom branding, and priority support. For teams moving from Superset, the per-user cost is offset by the near-zero training investment — if your team knows spreadsheets, they already know Sigma. --- ## Conclusion Selecting the right business intelligence platform requires careful evaluation of your organization's specific needs, budget constraints, technical capabilities, and growth trajectory. While Apache Superset provides a powerful open-source foundation for analytics and dashboard creation, numerous alternatives offer distinct advantages — particularly around ease of use, managed infrastructure, AI capabilities, and enterprise support. The true cost of Superset often extends well beyond its zero license fee. Factor in engineering time for deployment, ongoing maintenance, scaling, and security when comparing against commercial alternatives. For many teams, a managed Apache Superset alternative delivers faster time-to-value and lower total cost of ownership. We recommend evaluating 2-3 Apache Superset alternatives through free trials before deciding. Most vendors offer implementation support and migration assistance to facilitate smooth transitions. By assessing each Apache Superset alternative against your unique requirements, you'll identify a BI solution that maximizes analytics value and supports data-driven decision-making. --- ## FAQs ### 1. What are the best Apache Superset alternatives in 2026? The best Apache Superset alternatives in 2026 include Draxlr, Tableau, Power BI, Looker, Domo, and Sigma Computing. The right choice depends on your needs such as AI powered analytics, ease of deployment, embedded dashboards, pricing scalability, and database compatibility. ### 2. Which Apache Superset alternative is best for AI powered analytics? If AI driven analytics is a priority, look for a BI platform that offers Text to SQL, AI chat, automated insights, and intelligent data exploration features to reduce manual reporting effort and eliminate the need for every user to write SQL. ### 3. Which Apache Superset alternative is best for non-technical users? Draxlr and Sigma are the easiest for non-technical users. Draxlr offers AI driven querying from natural language and a visual query builder, while Sigma provides a familiar spreadsheet interface connected to cloud warehouses. ### 4. Which Apache Superset alternative is best for small businesses? Draxlr and Power BI are ideal for small businesses with affordable pricing, quick implementation, and scalable growth models. Both eliminate the infrastructure overhead that makes Superset challenging for small teams without dedicated DevOps resources. ### 5. Is Apache Superset really free to use? Apache Superset is free to download and use as open-source software. However, the true cost includes cloud infrastructure, engineering time for setup and maintenance, security management, and ongoing upgrades. Many teams find that a managed alternative offers better ROI when total cost of ownership is considered. ### 6. Will switching from Apache Superset be complicated? Switching is straightforward with most platforms connecting to existing data sources. Since Superset connects directly to databases, you can point a new BI tool at the same databases. Migration typically takes days to weeks, with most vendors offering implementation support.

Best Luzmo Alternatives in 2026

hello@draxlr.com (Draxlr) — Fri, 13 Feb 2026 00:00:00 GMT

Finding the right Luzmo alternative can make a significant difference in how your team builds dashboards, embeds analytics, and scales data access across your organization or product. While Luzmo is a well-known business intelligence solution, it may not suit every company’s needs in terms of pricing, SQL flexibility, AI capabilities, customization depth, or long-term scalability. Many teams start with Luzmo but later look for Luzmo alternative tools that offer better control over data queries, more advanced dashboard customization, improved white labeling, or more transparent pricing. As your analytics requirements grow, having the right BI platform becomes critical to avoid engineering bottlenecks and rising costs. If you are actively searching for a strong Luzmo alternative in 2026, this guide will help you evaluate the best options available. --- ## Why consider a Luzmo alternative? While Luzmo provides a solid foundation for business intelligence and data visualization, several factors might push your organization toward exploring other options. Understanding these pain points can help you determine if switching to a Luzmo alternative makes sense for your business. ### 1. Cost Efficiency at Scale Luzmo's cost structure can become prohibitive as your user base expands or data volumes increase, making it challenging for cost-conscious teams to maintain ROI. Many Luzmo alternatives offer more transparent, scalable pricing models that grow alongside your business. ### 2. Limited customization and scalability This point often emerges as primary concerns for growing enterprises. Luzmo's architecture may struggle to handle complex, large-scale analytics requirements or highly customized workflows that align with your specific industry needs. ### 3. Feature gaps in AI-powered insights Modern BI tools are moving beyond static dashboards. Teams often explore Luzmo alternatives that provide AI driven query generation, text to SQL features, and automated insights to speed up dashboard creation. ### 4. Embedded analytics flexibility While Luzmo supports embedded dashboards, not every company needs the same level of embedding complexity. Some businesses want lighter embedding, stronger multi-tenant controls, or more customization over how dashboards appear inside their product. ### 5. White labeling SaaS companies often require full branding control, granular row-level security, and advanced permission management. A strong Luzmo alternative should provide flexibility without heavy engineering effort. ### 6. Long Term Product Alignment Your BI tool should evolve with your product roadmap. If your current analytics setup feels restrictive or adds friction to development, it may be time to consider a more scalable alternative. ### 7. User experience gaps Some organizations find Luzmo's learning curve steeper than expected, or its interface less intuitive for non-technical users. A more accessible BI platform can accelerate adoption rates across your organization. --- ## What to look for in a Luzmo alternative Selecting a replacement BI platform requires evaluating multiple dimensions. Here's what to prioritize when assessing alternatives: ### 1. Ease of use and accessibility This should rank high on your list. The best BI tool balances power with simplicity, making both technical analysts and business users to create insights without extensive training. Look for platforms with drag-and-drop interfaces, natural language query(AI) capabilities, and intuitive design. ### 2. Scalability and performance Determine whether the platform can grow with your ambitions. Assess how it handles increasing data volumes, concurrent users, and complex queries. Real-time performance under load is non-negotiable for modern analytics. ### 3. Advanced analytical features Distinguish premium platforms from basic ones. Consider whether you need AI-driven insights, advanced charts, intelligent alerts, or more. Your future analytics roadmap should influence this decision. ### 4. Transparent, flexible pricing This prevents budgetary surprises. Compare total cost of ownership across different pricing models—per-user, per-query, or consumption-based. Ensure the cost structure aligns with your growth projections. ### 5. Multi tenant and security controls Granular permissions, data filtering per user, and strong access controls are essential for customer facing analytics. ### 6. Vendor stability and support quality Research the company's product roadmap, and customer support reputation. Responsive support teams can make a significant difference during implementation and scaling phases. ### 7. Mobile and collaborative features This enables distributed teams to access insights anywhere. Look for mobile apps, real-time collaboration tools, and sharing capabilities that support modern work environments. ### 8. Robust integration capabilities These are essential for seamless operations. Your ideal Luzmo alternative should connect effortlessly with your data warehouse, cloud storage, or any other critical systems. Evaluate the breadth of pre-built connectors and API flexibility. --- ## Best Luzmo alternatives in 2026 at a glance

Tool	Best use case	Ease of use	AI capabilities	Embedded analytics
Luzmo	Customer facing dashboards	Medium	Basic	Yes
Draxlr	AI powered self-service analytics	High	Strong	Yes
Tableau	Enterprise visualization	Medium	Moderate	Yes
Microsoft Power BI	Microsoft ecosystem analytics	Medium	Moderate	Yes
Looker	Data modeling and governed analytics	Low	Moderate	Yes
Domo	Cloud BI with data integration	Medium	Moderate	Yes
Sigma	Spreadsheet style cloud analytics	High	Limited	Yes

--- ## 1. Draxlr ### AI powered analytics built for teams without dedicated data analysts ![Draxlr](/images/blogs/_tools/draxlr.png) [Draxlr](https://www.draxlr.com/) is an AI powered Luzmo alternative focused on delivering intuitive analytics capabilities with minimal learning curve. The tool combines powerful data processing with user-friendly interface design, making advanced analytics accessible to business users without technical backgrounds. Draxlr emphasizes rapid deployment and quick time-to-value, helping organizations extract insights from their data within days rather than weeks. Connect your Database ### Key Features - Multiple ways to build dashboards - Visual Query Builder, AI assisted, and raw SQL editor - Powerful visualization engine with multiple chart types and customization options - Full SQL control without restrictions for complex joins, aggregations, and performance tuning - Live database connections without data duplication or warehouse migration - Interactive dashboards with filters and drill through capabilities - Secure embedded analytics serving both internal BI reporting and customer-facing embedded analytics - Predictable, scalable pricing that grows with teams without unexpected cost spikes - Lightweight setup enabling quick database connection and dashboard building - Advanced calculated fields and complex analytical operations ### Pricing Draxlr offers **transparent pricing** tiers based on features and usage. The tool offers entry-level plans suitable for small teams and organizations just beginning their analytics journey. Mid-market pricing tiers support growing data volumes and expanding user bases. Enterprise plans include dedicated support, custom integrations, and advanced security features. **self hosted** options are also available for teams requiring full infrastructure control. --- ## 2. Tableau ### Scalable enterprise analytics with advanced visualization [Tableau](https://www.tableau.com/) is a leading enterprise analytics Luzmo alternative known for advanced data visualization and powerful reporting capabilities. It is widely used as a Luzmo alternative by large organizations for deep analytical exploration. ### Key Features - Advanced visualization engine - Drag and drop dashboard creation - Data blending and modeling - Forecasting and predictive analytics - Enterprise grade security ### Pricing Tableau follows a user based licensing model with separate pricing tiers for creators, explorers, and viewers. Enterprise deployments can become expensive at scale, especially for large teams. --- ## 3. Power BI ### Best Luzmo alternative for Microsoft ecosystem users [Power BI](https://powerbi.microsoft.com/) is Microsoft’s business intelligence solution, widely adopted by organizations using the Microsoft ecosystem. It offers strong integration with Excel, Azure, and other Microsoft tools. ### Key Features - Deep integration with Microsoft products - Drag and drop report builder - AI powered visuals and forecasting - Power BI Embedded for application integration - Strong governance and security controls ### Pricing Power BI offers affordable entry level pricing, but enterprise and embedded use cases may require premium capacity plans, which significantly increase costs. --- ## 4. Looker ### The API-First tool for modern data organizations [Looker](https://cloud.google.com/looker), part of Google Cloud, focuses on governed analytics and centralized data modeling. It is often chosen by organizations that need strong semantic modeling on top of their data warehouse. ### Key Features - LookML data modeling layer - Centralized data governance - Embedded analytics support - Cloud warehouse integration - Enterprise security controls ### Pricing Looker uses a custom pricing model based on platform usage and data warehouse scale. It is typically positioned for mid size to enterprise organizations and may not be cost effective for smaller teams. --- ## 5. Domo ### Modern dashboard building tool offering cloud native dashboards [Domo](https://www.domo.com/) is a cloud based Luzmo alternative designed for executive reporting and operational dashboards. It emphasizes real time data connectivity and collaboration features. ### Key Features - Cloud native dashboarding - Data connectors for multiple sources - AI driven insights and alerts - Collaboration and sharing tools - Mobile optimized dashboards ### Pricing Domo offers custom pricing, often positioned toward mid market and enterprise customers. Costs vary based on number of users and data connectors. --- ## 6. Sigma Computing ### Spreadsheet style designed for warehouse native analytics. [Sigma Computing](https://www.sigmacomputing.com/) is a cloud analytics Luzmo alternative known for its spreadsheet style interface built directly on modern cloud data warehouses. It appeals to business users familiar with Excel like workflows. ### Key Features - Spreadsheet style data exploration - Direct warehouse querying - Collaborative dashboards - Cloud data warehouse integration - No data extraction required ### Pricing Sigma typically offers per user pricing with enterprise plans for larger teams. Pricing scales with usage and warehouse connectivity requirements. --- ## Conclusion Selecting the right business intelligence platform requires careful evaluation of your organization's specific needs, budget constraints, technical capabilities, and growth trajectory. While Luzmo provides a solid foundation for analytics and dashboard creation, numerous alternatives offer distinct advantages depending on your priorities. We recommend evaluating 2-3 Luzmo alternatives through free trials before deciding. Most vendors offer implementation support and migration assistance to facilitate smooth transitions. By assessing each Luzmo alternative against your unique requirements, you'll identify a BI solution that maximizes analytics value and supports data-driven decision-making. --- ## FAQs ### 1. What are the best Luzmo alternatives in 2026? The best Luzmo alternatives in 2026 include Draxlr, Tableau, Power BI, Looker, Domo, and Sigma Computing. The right choice depends on your needs such as AI powered analytics, SQL flexibility, embedded dashboards, pricing scalability, and database compatibility. ### 2. Which Luzmo alternative is best for AI powered analytics? If AI driven analytics is a priority, look for a BI platform that offers Text to SQL, AI chat, automated insights, and intelligent data exploration features to reduce manual reporting effort. ### 3. Which Luzmo alternative is best for non-technical users Draxlr and Sigma are the easiest for non-technical users. Draxlr offers AI driven querying from natural language, while Sigma provides a familiar spreadsheet interface connected to cloud warehouses. ### 4. Which Luzmo alternative is best for small businesses? Draxlr and Power BI are ideal for small businesses with affordable pricing, quick implementation, and scalable growth models. ### 5. Will switching from Luzmo be complicated? Switching is straightforward with most platforms connecting to existing data sources, typically taking days to weeks with professional migration support available. ### 6. What's the cheapest Luzmo alternative? Draxlr (consumption-based) and Power BI (from $10/user/month) offer the most cost-effective alternatives with better ROI.

Best Zoho Analytics Alternatives in 2026

hello@draxlr.com (Draxlr) — Tue, 10 Feb 2026 00:00:00 GMT

The best Zoho Analytics alternatives in 2026 are Draxlr, Tableau, Microsoft Power BI, Looker, Domo, and Sigma. Each targets a different type of team, and the right choice depends on your data stack, budget, and whether you need features like live SQL database access, AI-assisted querying, or [embedded analytics](/embedded-analytics-tool/) Zoho Analytics works well for teams fully invested in the Zoho ecosystem, but many organizations are moving to dedicated BI tools that offer more power and fewer restrictions. Common reasons teams look for Zoho Analytics alternatives: - Need to connect directly to live SQL databases instead of importing or syncing data - Want AI assisted querying that goes beyond basic natural language search - Require embeddable dashboards at a reasonable cost without Zoho platform lock-in - Need a tool that non-technical team members can use independently - Want better support for real-time alerting on database changes **Quick picks by use case** - **AI-powered self-service analytics**: Draxlr - **Enterprise visualization**: Tableau - **Microsoft 365 integration**: Power BI - **Governed metrics and data modeling**: Looker - **Built-in data integration across cloud apps**: Domo - **Spreadsheet-style analytics on cloud warehouses**: Sigma Here is a full comparison of the top Zoho Analytics alternatives in 2026. ## Quick answer If you want the short version, Draxlr is the best Zoho Analytics alternative for teams that need live SQL database access, AI-assisted querying, embedded dashboards, and self-hosting. Tableau is better for advanced enterprise visualization, Power BI is best for Microsoft-centric teams, Looker is strongest for governed warehouse analytics, Domo is best for multi-source data integration, and Sigma is a strong fit for spreadsheet-style analysis on cloud warehouses. ## Comparison table

Tool	Best use case	Ease of use	AI capabilities	Embedded analytics	Live database connection
Zoho Analytics	Zoho ecosystem reporting	Medium	Basic	Yes	Limited
Draxlr	AI powered self-service analytics	High	Strong	Yes	Yes
Tableau	Enterprise visualization	Medium	Moderate	Yes	Yes
Microsoft Power BI	Microsoft ecosystem analytics	Medium	Moderate	Yes	Yes
Looker	Data modeling and governed analytics	Low	Moderate	Yes	Yes
Domo	Cloud BI with data integration	Medium	Moderate	Yes	Yes
Sigma	Spreadsheet style cloud analytics	High	Limited	Yes	Yes

--- ## 1. Draxlr ### AI powered analytics built for teams without dedicated data analysts Draxlr is the strongest Zoho Analytics alternative for teams that want AI assisted self-service analytics without depending on the Zoho ecosystem. - AI SQL generation from plain English questions, no SQL or formula knowledge needed - Visual query builder with support for filters, joins, and aggregations - Connects directly to live SQL databases including PostgreSQL, MySQL, SQL Server, ClickHouse, BigQuery, Databricks, and more - Real-time dashboards that stay in sync with your production data - Slack and email alerts when key metrics cross thresholds - Public sharing and embeddable dashboards with white-label support - Clean, minimal interface designed for founders, product managers, and business users **Why teams switch from Zoho Analytics to Draxlr** - No data syncing or import delays. Queries run directly on your database - AI assistance that actually writes accurate SQL, not just keyword search - Simpler and faster setup with no ecosystem lock-in - Affordable embedded analytics for customer-facing dashboards **Best for** - Startups and SaaS teams - Product, growth, and operations teams - Teams without a dedicated BI or data team - Companies that want to embed analytics inside their product ![Draxlr](/images/blogs/_tools/draxlr.png) --- ## 2. Tableau ### Enterprise grade visualization and analytics platform Tableau remains the industry standard for advanced data visualization and is a strong Zoho Analytics alternative for enterprises that need rich, interactive dashboards. - Industry leading charting and visualization capabilities - Connects to hundreds of data sources including databases, warehouses, and files - Strong governance, security, and enterprise administration features - Excellent for executive reporting and data storytelling - Large community with thousands of templates and resources **Watch outs** - Steeper learning curve compared to Zoho Analytics - Significantly higher cost, especially for larger teams - Requires more technical expertise to build and maintain dashboards - Can be overkill for simple internal reporting **Best for** - Large enterprises with dedicated BI teams - Organizations that need advanced, interactive visualizations - Teams producing board-level and executive reporting ![Tableau](/images/blogs/zoho-analytics-alternatives-2026/tableau.png) --- ## 3. Microsoft Power BI ### Best Zoho Analytics alternative for Microsoft ecosystem users Power BI is a natural Zoho Analytics alternative for teams that rely on Microsoft 365, Azure, and Excel. It offers deep integrations across the Microsoft stack with strong self-service and governance features. - Deep integration with Excel, Azure, Teams, SharePoint, and Dynamics 365 - Drag and drop report builder with a wide library of visualizations - Natural language Q and A for basic data exploration - Strong row-level security and workspace governance - Copilot AI features for summarization and report generation **Watch outs** - Premium features like paginated reports and larger datasets require Pro or Premium licensing - Data modeling with DAX can be complex for beginners - Embedding outside the Microsoft ecosystem requires extra setup - Less suited for teams using non-Microsoft infrastructure **Best for** - Organizations already using Microsoft 365 and Azure - Finance, HR, and operations reporting - Teams that want tight integration with Excel workflows ![PowerBI](/images/blogs/zoho-analytics-alternatives-2026/power-bi.png) --- ## 4. Looker ### Data modeling first BI for governed, scalable analytics Looker, now part of Google Cloud, is a strong alternative to Zoho Analytics for organizations that prioritize consistent data definitions and governed self-service analytics. - LookML modeling layer ensures consistent metrics across the organization - Runs live queries against cloud data warehouses like BigQuery, Snowflake, and Redshift - Powerful embedded analytics with APIs and SDKs - Strong governance, permissions, and version control for data models - Tight integration with the Google Cloud ecosystem **Watch outs** - Requires engineering effort to set up and maintain LookML models - Higher learning curve than Zoho Analytics or other visual BI tools - Pricing is enterprise-level and not transparent - Less accessible for non-technical users without pre-built explores **Best for** - Data teams that want a single source of truth for metrics - Organizations using Google Cloud and BigQuery - Companies that need governed, scalable embedded analytics ![Looker](/images/blogs/zoho-analytics-alternatives-2026/looker.png) --- ## 5. Domo ### Cloud BI platform with built-in data integration Domo is a strong Zoho Analytics alternative for organizations that want a single cloud platform combining data integration, visualization, and app building without managing separate ETL tools. - Cloud-native platform with built-in ETL and over 1,000 pre-built data connectors - Drag and drop dashboard builder with a large library of chart types - Real-time data pipelines that pull from databases, APIs, spreadsheets, and cloud apps - App builder for creating custom data-driven workflows and tools - Role-based access controls and enterprise governance features **Watch outs** - Pricing is not transparent and can scale quickly for larger teams - The platform can feel overwhelming due to the breadth of features - Less flexibility for teams that prefer SQL-first or open source workflows - Embedded analytics requires higher-tier plans **Best for** - Mid-size to large organizations that need data integration and BI in one platform - Operations and marketing teams that pull data from many cloud sources - Companies that want pre-built connectors instead of writing custom queries ![Domo](/images/blogs/zoho-analytics-alternatives-2026/domo.png) --- ## 6. Sigma ### Spreadsheet style BI for cloud data warehouse teams Sigma is a great Zoho Analytics alternative for teams that want the familiarity of spreadsheets combined with the power of live cloud warehouse queries. - Spreadsheet-like interface that business users adopt quickly - Live queries on BigQuery, Snowflake, Databricks, and Redshift without data imports - Built-in collaboration, versioning, and governance features - Strong support for ad-hoc data exploration and analysis - Good balance between self-service access and data team control **Watch outs** - Pricing is higher than Zoho Analytics, especially at scale - Requires a cloud data warehouse as the data source - Embedded analytics support is less mature than some competitors - Less suitable for teams working primarily with traditional SQL databases **Best for** - Teams already using cloud data warehouses - Business analysts who prefer spreadsheet-based workflows - Organizations that want governed self-service analytics without SQL ![Sigma](/images/blogs/zoho-analytics-alternatives-2026/sigma.png) --- ## Why teams move away from Zoho Analytics in 2026 Zoho Analytics works well within the Zoho ecosystem, but teams often outgrow it for several reasons: - **Data connectivity**: Zoho Analytics relies heavily on data imports and connectors. Teams with SQL databases prefer tools that query data live without syncing delays - **AI limitations**: Zoho's Zia assistant handles basic natural language queries, but it falls short compared to AI tools that generate full SQL queries from plain English - **Ecosystem lock-in**: Zoho Analytics works best alongside Zoho CRM, Zoho Desk, and other Zoho products. Teams using mixed or non-Zoho stacks often find it limiting - **Embedding costs and flexibility**: Embedding Zoho Analytics dashboards outside of Zoho apps requires workarounds and additional licensing that may not scale well - **Customization**: Advanced customization for dashboards, alerts, and workflows is more restricted compared to dedicated BI platforms --- ## How to choose the right Zoho Analytics alternative Before picking a replacement, narrow down your options based on these key factors. **Where does your data live?** If you work with SQL databases like PostgreSQL or MySQL, choose a tool with native live database connections. Draxlr connects directly to production databases without syncing or imports. If your data lives in a cloud warehouse like Snowflake or BigQuery, Looker or Sigma are purpose-built for that environment. If you pull from dozens of SaaS apps, Domo's 1,000-plus connectors reduce integration work significantly. **Who will build and use dashboards?** Non-technical teams benefit most from Draxlr's AI querying, where you ask a question in plain English and get accurate SQL results, or Sigma's familiar spreadsheet interface. Technical teams may prefer Tableau's visualization depth or Looker's LookML modeling layer. **What is your budget?** Power BI has the lowest entry cost among enterprise options. Draxlr and Sigma offer transparent per-user pricing that scales predictably. Tableau, Looker, and Domo are enterprise-priced and typically require a sales conversation. **Do you need embedded analytics?** Draxlr and Looker are the strongest choices for embedding dashboards inside your own product. Draxlr offers affordable white-label embedding; Looker provides enterprise-grade APIs and SDKs for deeper customization. **Do you need self-hosting?** Most tools in this list are cloud-only. Draxlr supports self-hosted deployment for teams with strict data residency or compliance requirements. --- ## Final recommendation: which Zoho Analytics alternative should you choose - **Draxlr**: best for teams that want AI-driven, self-service analytics with live SQL database access and no ecosystem lock-in - **Tableau**: best for enterprises with dedicated BI teams that need advanced visualizations and a large community of templates - **Power BI**: best for organizations already using Microsoft 365 and Azure who want the tightest ecosystem integration - **Looker**: best for data teams that need governed, consistent metrics and are already on Google Cloud or a major data warehouse - **Domo**: best for mid-size teams that pull data from many cloud sources and want data integration and BI in a single platform - **Sigma**: best for business analysts who think in spreadsheets but need the scale of a cloud data warehouse For most growing teams replacing Zoho Analytics, Draxlr offers the fastest path to self-service analytics without the complexity of enterprise BI tools or the restrictions of the Zoho ecosystem. --- ## FAQs ### What is the best Zoho Analytics alternative in 2026 The best alternative depends on your team and use case. Draxlr is ideal for AI assisted self-service analytics with live database connections. Tableau and Power BI suit enterprise environments. Looker works well for governed data modeling, and Domo is a strong option for teams that need built-in data integration. ### Is Zoho Analytics still worth using in 2026 Yes, if your team is fully invested in the Zoho ecosystem. Zoho Analytics integrates well with Zoho CRM, Desk, and other Zoho products. However, teams that use external databases or need stronger AI capabilities often find dedicated BI tools more effective. ### Which Zoho Analytics alternative is best for non-technical users Draxlr and Sigma are the easiest for non-technical users. Draxlr offers AI driven querying from natural language, while Sigma provides a familiar spreadsheet interface connected to cloud warehouses. ### Which alternative is best for teams with many cloud data sources Domo is a great choice for teams that pull data from many cloud apps, APIs, and databases. It includes over 1,000 pre-built connectors and built-in ETL, so you can consolidate data without separate integration tools. ### Which Zoho Analytics alternative is best for embedded dashboards Draxlr and Looker are commonly chosen for embedded analytics. Draxlr offers affordable, white-label embeddable dashboards. Looker provides enterprise grade embedded analytics with APIs and SDKs for deeper customization. ### Which tool is best for teams that need live database connections Draxlr, Domo, and Looker all connect directly to live databases. Draxlr is the easiest to set up and includes AI assisted querying. Domo offers broad connectivity with built-in data pipelines. Looker is best for organizations that want a governed modeling layer on top of their warehouse. ### How does Zoho Analytics pricing compare to alternatives Zoho Analytics starts at around $30 per month for two users with limited data rows. Power BI starts at approximately $10 per user per month for Pro. Draxlr offers competitive per-user pricing that includes live database connections and AI features from the base plan. Tableau and Looker are significantly more expensive and designed for enterprise buyers. Domo pricing is customized and not publicly listed. ### Does Zoho Analytics have a free plan Zoho Analytics offers a free plan limited to two users and 10,000 rows. It is suitable for evaluation but not for production use. Most Zoho Analytics alternatives do not offer a permanent free tier, though Power BI Desktop is free for local report building. ### Can I use a Zoho Analytics alternative with my existing SQL database Yes. Draxlr, Tableau, Power BI, Looker, and Domo all connect to common SQL databases including PostgreSQL, MySQL, SQL Server, and others. Draxlr is specifically designed around live SQL queries and supports direct connections without data imports or scheduled syncing. ### Is there an open source alternative to Zoho Analytics The most widely used open source BI alternatives are Metabase and Apache Superset. Both support direct SQL database connections and dashboard building. If open source or self-hosting with full control is a requirement, these are worth evaluating alongside the commercial options in this list.

How to embed a MSSQL dashboard in your app

hello@draxlr.com (Draxlr) — Tue, 27 Jan 2026 00:00:00 GMT

Your users need to see their data insights. Not in a separate tool, not in another tab but right there in your application where they're already working. That's where MSSQL embedded dashboards come in. The problem is that embedding a **Microsoft SQL Server** dashboard has traditionally required serious engineering effort. You're looking at weeks of development, complex infrastructure setup, and ongoing maintenance. Most teams either skip embedded dashboards altogether (and lose a competitive edge) or commit months to building something that becomes technical debt as their database scales. But there's a better way. Whether you're building a SaaS product, an internal analytics tool, or a client reporting platform, the approach is the same. This guide walks you through the complete process of embedding a MSSQL dashboard in your application. --- ## What is Embedded MSSQL Dashboard? An embedded MSSQL dashboard is a visualization interface that pulls data directly from your **Microsoft SQL Server** database and displays it within your application. Rather than forcing users to access a separate tool or portal, the dashboard lives inside your app seamlessly integrated with your existing workflows. Embedded dashboards aren't new, but the tooling has evolved dramatically. Five years ago, embedding dashboards required: - Building from scratch with custom code - Managing your own servers and infrastructure - Handling security, scaling, and performance yourself - Months of development time Today, modern solutions abstract away this complexity. You can connect your MSSQL database, design a dashboard, and embed it in your application in hours instead of months. The infrastructure is managed for you. Security is built-in. Performance is handled automatically. This shift has made embedded dashboards accessible to teams of any size, not just large enterprises with dedicated BI engineers. --- ## Why Embed Microsoft SQL Server Dashboards? Embedded dashboards solve a fundamental problem: your users need data insights, but they shouldn't have to leave your application to get them. Every context switch between your app and an external dashboard tool breaks focus and kills productivity. ### 1. Better In App Experience Users can view key metrics and trends without switching tools or contexts. Dashboards load inside the same interface they already use, making analytics feel like a natural part of the product instead of a separate destination. ### 2. Real Time Access to SQL Server Data When dashboards are connected directly to Microsoft SQL Server, users are always looking at current data. There is no waiting for scheduled reports or outdated exports. This makes it easier to respond to changes as they happen. ### 3. Competitive Differentiation Embedded analytics is increasingly an expectation in SaaS and application businesses. Offering built-in dashboards enhances product value, increases engagement, and keeps users from supplementing your offering with external tools. ### 4. Cost and Resource Efficiency Building custom dashboards requires significant engineering resources and ongoing maintenance. Embedded dashboards, done right, reduce both infrastructure overhead and external subscription costs. ### 5. Increased Product Adoption and Retention Users who regularly access their data gain deeper insights into their business. This insight drives engagement, they interact with your product more frequently and derive more value from it. Higher engagement typically means lower churn and increased customer lifetime value. ### 6. Flexibility and Control Embedded dashboards give you complete control over the user experience. You customize appearance to match your branding, control metric visibility by user role, and enforce security policies directly. Users experience a native product experience, not a third-party tool bolted on the side. ### 7. Scalability Without Complexity Modern embedded dashboard solutions are built to handle growth. As your user base scales or your data volume increases, the dashboard scales with it, without requiring extensive infrastructure management from your team. --- ## Ways to Embed MSSQL Dashboards When you decide to embed a dashboard, you have two fundamental approaches: build it yourself or use an embedding tool. Each has trade-offs worth understanding. ### Option 1: Build Custom You write code to create the dashboard from the ground up. Your team develops the frontend interface, writes SQL queries, builds the visualization layer, handles authentication, manages the database connection, and maintains everything. **What's involved:** - **Frontend development:** Build the dashboard UI using React, Vue, Angular, or vanilla JavaScript - **Backend API:** Create endpoints to query your MSSQL database - **Query optimization:** Write efficient SQL to ensure performance - **Authentication:** Implement user login and permission logic - **Visualization library:** Choose and integrate charting libraries (D3.js, Chart.js, Recharts) - **Real-time updates:** Build mechanisms for live data refresh - **Error handling:** Account for failed queries, connection issues, timeouts - **Scaling:** Design the architecture to handle growth - **Maintenance:** Keep dependencies updated, fix bugs, monitor performance **Timeline:** 6-12 months of development, depending on complexity **Who should consider this:** - Organizations with unlimited engineering budgets - Teams with dedicated full-stack developers - Products requiring highly specialized customization **Trade-offs:** - **Pros:** Complete control, fully custom, no vendor dependency - **Cons:** Time-intensive, expensive, requires specialized expertise, ongoing maintenance burden, security complexity, scalability challenges ### Option 2: Use an Embedded Analytics Tool You connect your MSSQL database to a dedicated platform, design your dashboard through a UI, and embed it in your application. The tool handles infrastructure, security, and maintenance. **What's involved:** - **Connect database:** Add your MSSQL credentials securely to the platform - **Design dashboard:** Use drag-and-drop builder or use AI assisted queries to create visualizations - **Configure access:** Set permissions and user roles - **Generate embed code:** Copy one line of code to integrate into your app - **Deploy:** Push to production **Timeline:** 1-2 hours to full deployment **Who should consider this:** - SaaS companies wanting fast time-to-market - Teams without dedicated BI engineers - Organizations wanting to reduce engineering overhead **Trade-offs:** - **Pros:** Fast setup, minimal code, built-in security, no infrastructure management, vendor handles updates and scaling, cost-effective - **Cons:** Less customization than fully custom, depends on vendor platform, limited to platform features For most teams, an [embedded analytics tool](/embedded-analytics-tool/) is the right choice. It gets dashboards in front of users faster, costs less, and requires fewer engineering resources. --- ## How to Embed a MSSQL Dashboard with Draxlr Setting up an embedded dashboard with Draxlr takes minutes. Here's the complete process. ### Step 1: Create Your Draxlr Account Sign up for a Draxlr account. You'll get instant access to the dashboard builder and all features. ### Step 2: Connect Your MSSQL Database Start by connecting your MSSQL database using your database credentials or by pasting an existing MSSQL connection string from your application or hosting provider using the 'Import from URL' option. If your MSSQL database restricts access by IP address, you can add the Draxlr IP shown during setup to allow the connection. For databases that must remain private, SSH connections are supported, enabling Draxlr to query your MSSQL data securely. Once connected, Draxlr automatically reads your schema and makes your tables available for building dashboards. Your credentials are encrypted and never exposed. Connect your Database ### Step 3: Build Your Dashboard In Draxlr, dashboards are built on top of saved queries. You can create those queries in three different ways, depending on how much control or flexibility you need. **Visual Query Builder** Choose your table, select columns, add filters, and apply aggregations using a point and click interface. The visual query builder is ideal for straightforward queries and common reporting needs, and it does not require any SQL knowledge. **AI Query Builder** Describe the data you want in plain language, such as “total revenue by product category for the last 12 months” or “top 10 customers by order count.” Draxlr translates the request into SQL automatically. You can then select a visualization and adjust it as needed. **Raw SQL** For more complex use cases, you can write SQL directly. This option gives you full control over joins, transformations, and advanced logic when the query goes beyond what a visual builder can express. Once your query is ready, turning it into a dashboard widget is straightforward: 1. **Select a visualization type** such as a charts, tables, metrics, gauges, geographic visualizations 2. **Configure the display** including labels, axes, number formatting, and colors 3. **Save the query** to add it to your dashboard Use this same flow to build out the rest of your dashboard, adding each metric or chart as needed. ### Step 4: Define Data Access and Filtering Rules Before embedding, configure how data should be scoped for each user. Draxlr allows you to pass identifiers and/or custom values from your application and apply them dynamically at query time. You can configure: - Row-level filters to restrict MSSQL data per user or organization - Parameter mapping to inject values from your backend - Dashboard permissions to control who can access each embed This ensures that every embedded dashboard loads with the correct MSSQL data, without requiring custom filtering logic in your frontend or backend. ### Step 5: Customize the Embedded Dashboard Experience Draxlr gives you extensive control over how the embedded MSSQL dashboard appears inside your app. From the Embed settings page, you can: - Show or hide headers and UI controls - Enable or disable export options - Adjust background colors and layout behavior - Control interactions such as drill-downs and tooltips - Match your brand colors - Choose light or dark theme These settings allow you to match the embedded dashboard to your application's design and user experience before embedding it. ### Step 6: Configure Dashboard-Level Filters If your dashboard includes filters, configure them directly in the Embed settings. You can choose which filters are exposed, define operators such as BETWEEN or IN, and set default values. Once enabled, filters become part of the embedded experience, allowing users to refine data without leaving your application. ### Step 7: Embed the Dashboard Into Your Application After configuration is complete, copy the generated embed code. Draxlr provides multiple embed options: - HTML - React - Vue For more advanced use cases, you can enable backend API mode to inject parameters securely from your server. Paste the embed code into your application, and the MSSQL dashboard loads instantly with live, user-specific data. --- ## A typical MSSQL dashboard embed in Draxlr looks like

--- ## Real-World Use Cases: SaaS Companies with Analytics ### Case Study 1: Customer Success Platform **Company Profile:** SaaS platform helping support teams manage customer interactions **Challenge:** Support managers needed real-time visibility into team performance metrics without leaving their workspace **Dashboard Metrics:** - Average response time by agent - Customer satisfaction scores - Ticket volume and resolution rates - Queue length and wait times - First-response resolution rate ### Case Study 2: E-commerce Analytics Platform **Company Profile:** E-commerce SaaS platform for inventory and order management **Challenge:** Store managers needed to see sales performance, inventory levels, and customer metrics in one place without leaving the platform **Dashboard Metrics:** - Daily revenue and order count - Product performance rankings - Inventory levels and stock alerts - Customer acquisition cost - Conversion rates - Profit margins by product ### Case Study 3: HR Management Software **Company Profile:** HR platform for workforce management and analytics **Challenge:** HR teams needed to see employee metrics, hiring progress, and turnover data within the platform **Dashboard Metrics:** - Headcount by department and location - Open positions and time-to-hire - Turnover rate and trends - Compensation benchmarks - Hiring pipeline status - Employee engagement scores --- ## Conclusion Embedded dashboards have become essential for modern applications. Users expect insights without leaving your platform. The question isn't whether to embed dashboards but it's how to do it efficiently. The traditional approach—building custom dashboards is expensive, time-consuming, and creates ongoing maintenance burden. It ties up engineering resources that should be focused on core product development. Most teams can't afford it, and those that do often regret the decision. Draxlr changes the equation. You get production-ready embedded dashboards without months of development, without managing infrastructure, without security complexity. Your MSSQL data becomes an asset you can showcase to users immediately. The difference is dramatic. What would take 4 months and $100,000+ in custom development takes 2 hours and costs a fraction of that. Your users get better insights. Your team ships faster. Your costs drop. Your product becomes more competitive. **Ready to get started?** **[Start Free Trial](https://app.draxlr.com/register)** - No credit card required. 14 days full access to all features. --- ## FAQs ### 1. Can I embed a MSSQL dashboard in a customer-facing application? Yes. You can embed MSSQL dashboards directly into customer-facing applications using a BI or embedded analytics tool, allowing users to view charts, tables, and metrics inside your app instead of in a separate analytics tool. ### 2. Do I need to write SQL to embed a dashboard? Not necessarily. While SQL gives you full control, dashboards can also be built using visual query builders or AI-generated queries. You can mix visual queries, AI SQL, and raw SQL within the same dashboard. ### 3. Can I customize how dashboards look? Yes. You can customize colors, fonts, logos, layouts, and themes. Dashboards can match your brand exactly. ### 4. Can embedded dashboards use live database data? Yes. Most embedded analytics tools query your database directly or on a schedule, so dashboards reflect live or near real-time data instead of static exports. ### 5. Can embedded analytics match my product’s branding? Most embedded analytics tools allow customization of colors, layout, and visibility of UI elements so dashboards feel native to your application rather than a third-party add-on.

How to embed a MySQL dashboard in your app

hello@draxlr.com (Draxlr) — Thu, 22 Jan 2026 00:00:00 GMT

Embedding a MySQL dashboard directly into your app is one of the fastest ways to turn raw database data into actionable insights for your users without forcing them to switch tools. Whether you are building a SaaS product, an internal admin panel, or a customer-facing analytics experience, an embedded MySQL dashboard lets you visualize metrics, track trends, and monitor performance in real time, all inside your existing application. In this guide, we will walk through the practical and scalable way to embed a MySQL dashboard. --- ## Why Embedded MySQL Dashboards Matter for Customer-Facing Analytics Customer-facing analytics, also known as embedded or in-app analytics, means displaying dashboards and reports directly inside your application using data from your MySQL database. Instead of sending users to external tools or offline exports, analytics becomes part of the product experience. ### What embedded MySQL dashboards enable - Show real-time charts, tables, and key metrics inside your app - Surface live MySQL data without manual exports or reports - Keep users in context without switching tools or interfaces ### Why embedding analytics improves the user experience - Users access insights exactly where they already work - Metrics update in real time, not through delayed spreadsheets - Trends and performance are easier to understand in context - Faster, more confident decision-making ### How embedded dashboards help product teams - Turn raw SQL data into a built-in product feature - Avoid building and maintaining custom analytics from scratch - Control access to data at the user or role level - Reduce engineering effort while keeping flexibility in visualization ### The long-term impact on your product - Build trust through transparent, real-time data access - Increase engagement and retention with meaningful insights - Make analytics a core part of the application, not an add-on --- ## What to Consider When Choosing How to Embed a MySQL Dashboard There is no single best way to embed a MySQL dashboard into an application. The right approach depends on how tightly analytics needs to integrate with your product, how quickly you need to ship, and how much flexibility you want over time. Rather than thinking only in terms of tools or implementation details, most teams evaluate their options across a few practical dimensions. Below are some of the most common questions teams ask when choosing an approach for embedded MySQL analytics. ### 1. Product experience and visual consistency Embedded analytics should feel like a natural extension of your application. If dashboards look or behave differently from the rest of the product, users immediately notice. Teams often prioritize approaches that allow analytics to follow the same layout patterns, design system, and interaction models as the rest of the app. ### 2. Flexibility in how data is presented Different products require different ways of showing data. Some teams need simple charts, while others require highly customized views, filters, or user-specific metrics. An embedding approach should not limit how data can be queried, visualized, or scoped per user. ### 3. Performance and reliability at scale Customer-facing dashboards are held to higher performance standards than internal reports. Users expect pages to load quickly and data to feel responsive, even when dashboards are backed by large MySQL tables. Any approach that introduces noticeable latency can quickly become a bottleneck. ### 4. Speed of development and iteration Analytics requirements change frequently. Teams value approaches that allow them to launch dashboards quickly and adapt them over time without repeated engineering effort. Faster iteration often matters more than perfect customization on day one. ### 5. Ongoing engineering effort Building dashboards directly on top of MySQL can provide maximum control, but it also increases maintenance costs. Many teams look for approaches that reduce the amount of custom code required, especially for common features like filtering, permissions, and updates. ### 6. Ability to grow with future needs What starts as a simple dashboard often evolves into something more complex. Teams consider whether their chosen approach can support future needs such as role-based access, additional data sources, alerts, or more advanced analytics without requiring a full rebuild. ### 7. Total cost over time Cost is not limited to upfront tooling or licensing. It also includes development time, infrastructure, and long-term maintenance. Understanding the full cost helps teams choose an approach that remains sustainable as usage grows. --- ## Approaches to Embed a MySQL Dashboard in Your App When embedding a MySQL dashboard into your application, you can choose between two approaches. The right option depends on how much customization you want, how quickly you need to ship, and how much engineering effort you are willing to invest. ### 1. Use a BI tool that supports embedding and pre-embed customization One common approach is to use a BI or [embedded analytics tool](/embedded-analytics-tool/) like Draxlr where most of the customization happens while building the dashboard, before it is embedded into your app. With tools like Draxlr, you first connect your MySQL database, build dashboards, and configure how data should be looked, queried, filtered, and displayed. This includes defining charts, tables, layouts, color scheme, filters, access rules, and visual settings upfront. Once the dashboard is ready, it can be embedded into your application using an iframe or a frontend SDK. Because the dashboard is already fully configured, the embedded version behaves like a ready-made feature inside your app, without requiring additional analytics logic in your frontend or backend. This approach works well for teams that want: - Faster time to market - Minimal custom analytics code - Strong control over dashboard behavior and appearance before embedding - The flexibility to update dashboards without redeploying their application ### 2. Build custom dashboards Another approach is to build dashboards entirely in-house by querying MySQL directly and rendering charts. This gives you complete control over the user experience and data flow, but it also requires significantly more development effort. Teams must handle SQL queries, performance optimization, permissions, filtering logic, visualization, and ongoing maintenance themselves. Custom-built dashboards are typically chosen when analytics requirements are highly specialized or when teams already have dedicated resources to maintain a custom analytics layer. In the next section, we’ll walk through how to build and embed a MySQL dashboard the easiest way using a BI tool called Draxlr, focusing on the customization options available before embedding. --- ## How to Build and Embed a MySQL Dashboard with Draxlr Draxlr lets you create fully customizable MySQL dashboards and embed them into your application without building analytics from scratch. Before anything is embedded, you can **configure how the dashboard looks, behaves, and filters** data so it fits naturally into your product. ### 1. Connect your MySQL database Start by connecting your MySQL database using your database credentials or by pasting an existing MySQL connection string from your application or hosting provider using the 'Import from URL' option. If your MySQL database restricts access by IP address, you can add the Draxlr IP shown during setup to allow the connection. And for databases that must remain private, SSH connections are supported, enabling Draxlr to query your MySQL data securely. Once connected, Draxlr automatically reads your schema and makes your tables available for building dashboards. --- ### 2. Build your dashboard for MySQL data using flexible query options

After connecting MySQL, you can start building your dashboard using the approach that best fits your workflow. Draxlr supports three ways to work with your data: - **Visual Query Builder** to create queries through clicks without writing SQL - **AI SQL** to generate queries from plain-text prompts - **Raw SQL** for full control over complex queries You can mix these query types within the same dashboard. Charts, tables, and metrics update instantly as you make changes, and layouts can be rearranged using drag-and-drop. Every element is powered directly by live MySQL data. All dashboard structure and queries are saved automatically, so any future changes to queries, filters, or layout are reflected across all embedded instances. --- ### 3. Define data access and filtering rules Before embedding, configure how data should be scoped for each user. Draxlr allows you to pass identifiers and/or custom values from your application and apply them dynamically at query time. You can configure: - Row-level filters to restrict MySQL data per user or organization - Parameter mapping to inject values from your backend - Dashboard permissions to control who can access each embed This ensures that every embedded dashboard loads with the correct MySQL data, without requiring custom filtering logic in your frontend or backend. --- ### 4. Customize the embedded dashboard experience Draxlr gives you extensive control over how the embedded MySQL dashboard appears inside your app. From the Embed settings page, you can: - Show or hide headers and UI controls - Enable or disable export options - Adjust background colors and layout behavior - Control interactions such as drill-downs and tooltips These settings allow you to match the embedded dashboard to your application’s design and user experience before embedding it. --- ### 5. Configure dashboard-level filters If your dashboard includes filters, configure them directly in the Embed settings. You can choose which filters are exposed, define operators such as `BETWEEN` or `IN`, and set default values. Once enabled, filters become part of the embedded experience, allowing users to refine data without leaving your application. --- ### 6. Embed the dashboard into your application After configuration is complete, copy the generated embed code. Draxlr provides multiple embed options: - HTML embed - React component - Vue component For more advanced use cases, you can enable backend API mode to inject parameters securely from your server. Paste the embed code into your application, and the MySQL dashboard loads instantly with live, user-specific data. --- ## A typical MySQL dashboard embed in Draxlr looks like

--- ## Conclusion Embedding a MySQL dashboard into your application is most effective when analytics is treated as part of the product, not an add-on. By building and customizing dashboards before embedding, you ensure users see relevant data in a way that feels natural inside your app. Using a tool like Draxlr removes the need to build and maintain analytics from scratch, while still giving you control over queries, filters, and access rules. As your product evolves, embedded MySQL dashboards can be updated and extended without redeploying your application, making analytics easier to manage and scale over time. --- ## FAQs ### 1. Can I embed a MySQL dashboard in a customer-facing application? Yes. You can embed MySQL dashboards directly into customer-facing applications using a BI or embedded analytics tool, allowing users to view charts, tables, and metrics inside your app instead of in a separate analytics tool. ### 2. Do I need to write SQL to embed a MySQL dashboard? Not necessarily. While SQL gives you full control, dashboards can also be built using visual query builders or AI-generated queries. You can mix visual queries, AI SQL, and raw SQL within the same dashboard. ### 3. Can each user see only their own MySQL data? Yes. Embedded dashboards can be configured to filter data based on user-specific identifiers such as user ID or organization ID. These values are passed from your application and applied automatically, so each user sees only the data they are allowed to access. ### 4. Can I customize the dashboard before embedding it? Yes. Dashboards can be fully customized before embedding, including charts, layouts, filters, access rules, and visual settings. Once embedded, the dashboard behaves like a ready-made feature inside your application. ### 5. Can I update embedded dashboards without redeploying my app? Yes. Since dashboards are managed outside your application code, updates to queries, filters, or layouts do not require a redeploy. The embedded dashboard reflects changes automatically.

Best Metabase Alternatives in 2026

hello@draxlr.com (Draxlr) — Fri, 16 Jan 2026 00:00:00 GMT

Metabase is widely used for SQL-based analytics, but in 2026 many teams are looking for more flexible, AI-assisted, and business-friendly BI tools. Modern teams want: - Faster insights without heavy SQL dependency - Better dashboards for non-technical users - Dashboard embedding at affordable rates (Metabase interactive embedding starts at $575 / month) - AI assistance for querying and summarizing data If Metabase feels limiting for your current needs, here are the best Metabase alternatives in 2026, compared side by side. ## Comparison table

Tool	Best use case	Ease of use	AI capabilities	Embedded analytics
Metabase	SQL driven analytics	Medium	Limited	Yes
Draxlr	AI powered self service analytics	High	Strong	Yes
Tableau	Enterprise dashboards	Medium	Moderate	Yes
Microsoft Power BI	Microsoft ecosystem analytics	Medium	Moderate	Yes
Qlik	Governed associative analytics	Medium	Moderate	Yes
Sisense	Embedded analytics	Medium	Moderate	Yes
Sigma	Spreadsheet style cloud analytics	High	Limited	Yes

--- ## 1. Draxlr ### AI powered alternative to Metabase for non technical teams - AI assisted SQL generation from natural language questions - Visual query builder for filters, joins, and aggregations - Real time dashboards connected to live databases - Slack and email alerts when metrics change - Public sharing and embeddable dashboards for products - Clean UI designed for founders, PMs, and business users **Best for** - Startups and SaaS teams - Product and growth teams - Teams without dedicated data analysts - Teams that want analytics embedded inside their product ![Draxlr](/images/blogs/_tools/draxlr.png) --- ## 2. Tableau ### Enterprise business intelligence platform for advanced visualization - Industry leading visualization and interactivity - Strong governance and enterprise controls - Works across many data sources and large datasets - Great for executive dashboards and reporting **Watch outs** - Steeper learning curve - Higher cost at scale - Can be heavy for simple reporting **Best for** - Large enterprises - Dedicated BI teams - Advanced reporting and data storytelling ![Tableau](/images/blogs/metabase-alternatives-2026/tableau.png) --- ## 3. Microsoft Power BI ### Best Metabase alternative for Microsoft ecosystem users - Deep integration with Excel, Azure, Teams, and Office 365 - Drag and drop dashboards and reports - Natural language Q and A features for basic insights - Strong security and governance options for larger orgs **Watch outs** - Advanced capabilities may require premium licensing - Data modeling can feel complex for smaller teams - Less flexible for product embedded analytics **Best for** - Organizations already using Microsoft tools - Finance and operations reporting - Structured governance environments ![PowerBI](/images/blogs/metabase-alternatives-2026/power-bi.png) --- ## 4. Qlik ### Governed Business Intelligence with Associative Analytics - Associative data model that lets users explore data from any angle without breaking context - Strong support for governed self-service analytics - Handles complex, multi-source enterprise datasets well - Advanced data preparation and transformation capabilities - Built-in security, permissions, and audit controls **Watch outs** - Steeper learning curve for new users - Higher cost compared to lightweight BI tools - Less intuitive for teams looking for quick, ad-hoc insights **Best for** - Large enterprises - Regulated industries like finance, healthcare, and manufacturing - Teams that need strong governance and controlled analytics at scale ![PowerBI](/images/blogs/metabase-alternatives-2026/qliksense.png) --- ## 5. Sisense ### Embedded Analytics for Customer-Facing Products - Powerful embedded analytics using APIs and SDKs - Designed for customer-facing dashboards and reports - Scales well for external and multi-tenant analytics use cases - Supports a wide range of data sources and cloud warehouses - Flexible theming and white-labeling options **Watch outs** - Setup and customization require engineering effort - More complex than internal-only BI tools - Pricing can be high for smaller teams **Best for** - SaaS companies offering analytics to customers - Product teams building data-driven features - Organizations that need white-labeled or embedded dashboards ![PowerBI](/images/blogs/metabase-alternatives-2026/sisense.png) --- ## 6. Sigma ### Spreadsheet style BI for cloud data warehouses - Spreadsheet like interface that business teams adopt quickly - Live queries on cloud warehouses - Governance features like lineage and permissions - Good balance between self service and analyst depth **Watch outs** - Higher pricing - Best for warehouse first organizations - Less focus on embedded analytics **Best for** - Cloud warehouse users - Analytics teams partnering with business users - Teams that prefer spreadsheet workflows ![Sigma](/images/blogs/metabase-alternatives-2026/sigma.png) --- ## FAQs ### What is the best Metabase alternative in 2026 The best alternative depends on your needs. Draxlr works well for AI assisted self service analytics. Tableau and Power BI are strong enterprise options. Qlik and Sisense fit governed and embedded analytics use cases. ### Is Metabase still a good BI tool in 2026 Yes. Metabase remains useful for SQL friendly teams, but many organizations move on when they need stronger AI support or easier access for non technical users. ### Which Metabase alternative is best for non technical users Draxlr and Sigma are generally easier for non technical users. Draxlr emphasizes AI driven insights, while Sigma offers a spreadsheet style interface. ### Are there enterprise grade alternatives to Metabase Yes. Tableau, Power BI, Qlik, and Sisense are all enterprise grade BI platforms used at scale. ### Which BI tool works best for embedded analytics Sisense and Draxlr are commonly used for embedded analytics inside customer facing applications.

How to build Supabase Dashboard from Supabase data

hello@draxlr.com (Draxlr) — Mon, 05 Jan 2026 00:00:00 GMT

If you need to build Supabase dashboard and want to avoid weeks of custom coding, this guide is for you. Learn how to build Supabase dashboard that keeps your metrics accessible to everyone without relying on engineering team every time. **But first, What is Supabase?** [Supabase](https://supabase.com/) is an open-source Firebase alternative that lets you create Postgres databases with the option to perform CRUD operations on the database. You can build a Supabase dashboard to visualize and monitor your data in real time. Supabase also provides these features: - APIs to access your data and perform queries. - Authentication: Give access to your data to users with row-level permission. - Write custom code that can be scaled. - Store images or videos. - Integrate with machine learning platforms. --- ## How to build Supabase dashboard from Supabase data > [Draxlr](https://supabase.com/partners/integrations/draxlr) is a no-code BI tool that helps you build dashboards, get data alerts, and embed dashboards on customer-facing sites. > Draxlr is a technology partner with Supabase to allow Supabase users to build Supabase dashboards and get data alerts. **Learn more here: [https://supabase.com/partners/integrations/draxlr](https://supabase.com/partners/integrations/draxlr)** Here is a quick video walkthrough of how to build Supabase dashboard from **Supabase data** in **Draxlr**: Ok, let's get started. --- ## Connecting Supabase with Draxlr Supabase allows you to manage multiple organizations, each containing multiple projects with their own Postgres databases. Draxlr enables you to connect to a Supabase project database and gain valuable insights from your data. 1. Login into Draxlr and navigate to the [Add Database page](https://app.draxlr.com/add-database). 2. Select **Supabase** from the available database options. ![Draxlr Select Supabase](/images/blogs/how-to-build-dashboards-from-supabase-data/images/select-supabase.png) 3. Click on **Connect Supabase** and select the organization that hosts your database. ![Draxlr Select Supabase Organization](/images/blogs/how-to-build-dashboards-from-supabase-data/images/select_organization.png) 4. Select the **project** from the list of projects within the organization. ![Draxlr Select Supabase Project](/images/blogs/how-to-build-dashboards-from-supabase-data/images/select_project.png) 5. Draxlr will automatically fetch database credentials from Supabase, except for the **database password**, which you need to enter manually. ![Draxlr Set Supabase Password](/images/blogs/how-to-build-dashboards-from-supabase-data/images/set_password.png) Enter the database password manually, and click **Next**. 6. Draxlr will sync the table schema and guide you through creating a simple bar chart as part of the onboarding process. ![Draxlr Onboarding Graph](/images/blogs/how-to-build-dashboards-from-supabase-data/images/create_onboarding_query.png) Select the table, then specify the x-axis and y-axis values. 7. Draxlr will generate your first graph query, add it to your dashboard, and [set up an email alert](https://www.draxlr.com/blogs/how-to-set-email-alerts-when-your-sql-data-changes/) to notify you whenever the graph value changes. ![Draxlr onboarding completed](/images/blogs/how-to-build-dashboards-from-supabase-data/images/onboarding_completed.png) --- ## How to build queries and dashboard from Supabase data? You can build queries and leverage them to create Supabase dashboard and set up alerts. There are 3 ways to create a query in Draxlr: 1. **Query Builder** – A no-code feature for generating queries. 2. **Raw SQL** – Write and execute custom SQL queries. 3. **AI-powered SQL Generation** – Convert [plain-English prompts into SQL queries](https://www.draxlr.com/blogs/ai-sql-tool/). ### Example: Query Builder 1. You can use Draxlr's no-code query builder to generate data using **Filters, Joins, Aggregate, Sort, and Select** options. 2. Once you have the desired data result, you can **visualise** it using multiple graph options like bar, pie, line, area, and more. ![Draxlr Supabase Graph Options](/images/blogs/how-to-build-dashboards-from-supabase-data/images/query_builder.png) 3. After you have completed the Graph setup, you can save the query and: - Add it to a Dashboard - Create integrations (Email/Slack Alerts, APIs, Google Sheets export). 4. **And that's it; your dashboard is ready!** ![Draxlr Supabase - Dashboard View](/images/blogs/how-to-build-dashboards-from-supabase-data/images/dashboard-view.png) 5. You can **share the dashboard** with your team and **embed the dashboard** in your customer-facing application. --- ## Key metrics to track in your Supabase dashboard A well-designed Supabase dashboard helps you monitor the health and growth of your product at a glance. To make the most of your data, build Supabase dashboard that focus on tracking metrics that offer clarity, drive decisions, and surface trends early. Some KPIs are below: #### 1. Total records or transactions Tracks the volume of data or actions over time. **Example:** Total orders placed this month, total appointments booked, or total invoices generated. #### 2. Growth rate by week or month or quarter Measures the speed of growth across time periods. **Example:** Monthly growth in active users. #### 3. Average value Helps track trends using average numbers. **Example:** Average revenue per customer is 4500 rupees or average delivery time is 42 minutes. #### 4. Conversion rate Measures how many users move from one step to another. **Example:** Out of 500 visitors, 40 signed up for a demo. #### 5. Active and inactive counts Shows how many entities are currently in use versus idle. **Example:** 4200 active users this week and 1800 inactive users. --- With Draxlr, you can turn raw Supabase data into actionable insights, enhancing your decision-making and workflow automation. Connect your Supabase Database \- **Vivek**

Best Supabase Analytics Tools for Dashboards and Reporting

hello@draxlr.com (Draxlr) — Fri, 26 Dec 2025 00:00:00 GMT

**Supabase analytics tools** are designed to turn raw data stored in your Supabase database into clear, actionable dashboards and reports. They help you visualize data using charts, tables, and graphs so you can spot trends, track performance, and understand what is really happening inside your application. Think of Supabase analytics tools as data translators. [Supabase](https://supabase.com) is a powerful, open source backend built on PostgreSQL, but once your app starts growing, analyzing data directly from tables and SQL queries becomes time consuming. Analytics tools step in to organize that data and present it in a way that is easy to explore and share. Whether you are monitoring user signups, tracking revenue, analyzing feature usage, or building internal dashboards, a [Supabase dashboard tool](https://www.draxlr.com/blogs/how-to-build-dashboards-from-supabase-data/) helps simplify complex data into meaningful insights. Instead of stitching together scripts or exporting data repeatedly, these tools give you a faster and more reliable way to make data driven decisions as your product scales. In this article, we break down the most reliable Supabase analytics tools to consider for 2026. --- ## What are Supabase analytics tools? Supabase analytics tools help you analyze and visualize data stored in your Supabase database without relying on raw SQL queries for every insight. These tools connect directly to Supabase’s PostgreSQL database and transform rows of data into dashboards, charts, tables, and reports that are easy to understand and share. Think of them as a bridge between your database and decision making. While Supabase is excellent for building and running applications, Supabase analytics tools focus on answering questions like how users behave, which features are being used, or how key metrics change over time. Instead of manually querying tables or exporting data, Supabase analytics tools present this information in a structured and visual format. A Supabase dashboard tool typically allows you to filter data, drill into specific records, and track metrics in real time or near real time. This makes it easier for founders, product managers, and operations teams to monitor performance without deep technical effort. Whether you are tracking signups, revenue, engagement, or internal operations, Supabase analytics tools simplify complex datasets and turn them into insights you can act on. --- ## Why your business needs Supabase analytics tools As your product grows, so does the amount of data flowing through your Supabase database. User activity, feature usage, revenue events, and operational metrics all live in tables, but raw data alone does not create clarity. Without proper analytics, teams spend time writing ad hoc queries, exporting data, or relying on gut feeling instead of evidence. Supabase analytics tools help turn database data into shared understanding. Instead of insights living with a few engineers, analytics dashboards give product, growth, and operations teams a consistent view of what is happening across the business. This makes decisions more proactive, aligned, and measurable. **The core benefits of Supabase analytics tools are:** **Visibility:** Dashboards bring key metrics like signups, engagement, revenue, and system activity into one place. Teams no longer need to chase SQL queries or spreadsheets to understand performance. Everyone works from the same numbers and definitions. **Speed:** With analytics in place, answering questions becomes faster. Teams can explore data, apply filters, and track trends without waiting for custom queries or manual reports. This shortens feedback loops and helps teams act while insights are still relevant. **Better decision making:** Historical trends and real time views help teams understand what is changing and why. Whether it is a drop in activation, a spike in usage, or a steady revenue trend, analytics provide context instead of isolated data points. --- ## How to choose a Supabase analytics tool Choosing the right Supabase analytics tool can have a big impact on how effectively you turn data into insights. While many tools can connect to Supabase, the right one should match your team’s workflow, technical comfort level, and long term analytics needs. Here are some key factors to consider when evaluating different Supabase analytics tools: ### 1. Cost and budget Make sure the pricing fits your budget, including both upfront costs and ongoing subscriptions. Some Supabase analytics tools offer flexible plans as your usage grows, which can be helpful for small and mid size teams. ### 2. Easy to adopt across teams Choose a Supabase analytics tool that different teams can start using quickly without heavy training or setup. The right Supabase dashboard tool should make it easy for engineers, product managers, and operations teams to build, view, and update dashboards without creating bottlenecks or relying on a single owner. ### 3. Dashboard and reporting features Evaluate the range of dashboards, charts, tables, and filters the Supabase analytics tool supports. Features like customization, scheduled reports, and alerts can make a big difference in how you monitor and act on your Supabase data. ### 4. Access control and row level security As teams grow, data access gets messy fast. Prioritize a Supabase dashboard tool that can enforce row level security rules, restrict sensitive tables, and control what each team or customer can see. ### 5. Embedding and external sharing If you share analytics with clients or other departments, embedding becomes important. A strong Supabase analytics tool should support secure public links, password or pin protection, and embeddable dashboards. ### 6. Support and onboarding When multiple teams depend on analytics, support quality matters. Prefer tools with clear documentation, fast response times, and onboarding help so you are not blocked during rollout. --- ## The best Supabase analytics tools to consider in 2026 Here’s a breakdown of the top Supabase analytics tools worth exploring in 2026 and beyond. ### 1. Draxlr [Draxlr](https://www.draxlr.com/) is a SQL first analytics and dashboarding tool designed to work directly with databases like Supabase. It focuses on making analytics accessible without sacrificing flexibility, allowing teams to build dashboards using visual queries, AI assisted SQL, or raw SQL. #### Use case Draxlr is best suited for teams that want native Supabase analytics without building custom dashboards or maintaining complex BI setups. It works well for founders, product teams, and mid size teams who need dashboards, alerts, and embeddable analytics directly on Supabase data. #### Key features - Native Supabase and PostgreSQL support - Visual query builder, AI chat, and Raw SQL queries - Dashboards with filters and drill downs - Slack and email alerts from Supabase data - Secure sharing and embeddable dashboards #### Pricing Draxlr's paid plan starts at $75/month , with a free trial available to explore dashboards and analytics before upgrading. #### What users are saying > Draxlr offers a clean, modern interface and makes it incredibly easy to build dashboards — even for non-technical users. I especially like the visual quality and flexibility of the available graph types. It's intuitive, fast to set up, and has excellent usability from the start. The ability to securely share dashboards via link is a game-changer for our client reporting. — [Review from a SaaS Founder on G2](https://www.g2.com/products/draxlr/reviews/draxlr-review-11185807) --- ### 2. Tableau Tableau is a well known business intelligence platform focused on powerful visual analytics. It can connect to Supabase’s PostgreSQL database, though setup and ongoing maintenance may require more effort compared to Supabase focused tools. #### Use case Tableau is commonly used by large teams that need advanced visualizations and detailed analytics on Supabase data as part of a broader enterprise BI setup. #### Key features - Advanced data visualizations - Support for large and complex datasets - Wide range of connectors and integrations - Enterprise grade analytics workflows #### Pricing Tableau follows enterprise focused pricing, with costs varying based on user roles and deployment needs. A limited trial is available. #### What users are saying > What I like best is Tableau’s design philosophy — it emphasizes data exploration and visual storytelling over just rigid reporting. --- ### 3. Sigma Computing [Sigma Computing](https://www.sigmacomputing.com/) combines spreadsheet style workflows with cloud data analytics. It allows teams to explore data visually, though it is typically used in more enterprise oriented setups. #### Use case Sigma Computing is suited for business teams that prefer spreadsheet like analytics while working with Supabase data in a cloud environment. #### Key features - Spreadsheet like interface for analytics - Cloud based querying and collaboration - Strong support for large datasets - Business user friendly exploration #### Pricing Sigma Computing offers enterprise focused pricing, with plans tailored to larger teams and organizations. #### What users are saying > Sigma is awesome at making data accessible and editable for non technical users, requiring minimal tech background to use effectively. --- ### 4. Apache Superset [Apache Superset](https://superset.apache.org) is an open source analytics and visualization platform. It can be connected to Supabase’s PostgreSQL database and customized extensively, but requires technical setup and ongoing maintenance. #### Use case Apache Superset is a good option for teams that want fully self hosted Supabase analytics and are comfortable managing infrastructure themselves. #### Key features - Open source and self hosted - Wide range of chart types - SQL based exploration - Customizable dashboards #### Pricing Apache Superset is free to use, with costs coming from hosting, maintenance, and operational overhead. #### What users are saying > We can combine data from different systems for thorough analysis because the platform supports an extensive amount of data sources, including databases like Postgres. --- ### 5. Luzmo [Luzmo](https://www.luzmo.com) focuses on embedded analytics, allowing teams to present dashboards inside applications. It supports Supabase data through database connections and APIs. #### Use case Luzmo is commonly used by SaaS companies that want to embed Supabase analytics dashboards directly into their products. #### Key features - Embedded dashboards for SaaS products - Customizable visualizations - Multi tenant analytics support - Developer friendly embedding options #### Pricing Luzmo offers paid plans based on usage and embedding requirements, with a free trial available. #### What users are saying > Luzmo works well for embedding dashboards into applications and offers good customization options, though it is more focused on customer facing analytics than internal reporting. --- ## Conclusion The Supabase analytics tools covered in this guide serve different needs, from simple reporting to enterprise grade analysis and embedded dashboards. The right choice depends on your team size, technical resources, and how closely analytics needs to integrate with your day to day workflow. If you want a Supabase analytics tool that is SQL first, easy to adopt across teams, and built for dashboards, alerts, and sharing, you can try **Draxlr**. It connects directly to Supabase and helps you move from raw tables to decision ready dashboards without heavy setup. Connect your Database --- ## FAQs ### 1. What are Supabase analytics tools? Supabase analytics tools connect to your Supabase database and help you turn raw tables into dashboards, charts, and reports so you can track metrics and trends. ### 2. Does Supabase have built in analytics? Supabase includes basic database management and monitoring tools, but most teams use external analytics tools for dashboards, reporting, and sharing insights across teams. ### 3. How do I build dashboards from Supabase data? You can connect a BI or analytics tool to Supabase’s PostgreSQL database, then build charts and tables from SQL queries or a visual query builder, and combine them into dashboards. ### 4. Do I need SQL for Supabase analytics? Not always. Many tools offer visual query builders and templates, but SQL is helpful for custom metrics, joins, and advanced reporting as your product grows. ### 5. Which Supabase analytics tool is best for mid size teams? Mid size teams usually benefit from tools like Draxlr that support team access control, reusable dashboards, filters, scheduled reports or alerts, and the ability to scale as data volume increases.

How to send Stripe MRR and Gross revenue alerts to Slack for free

hello@draxlr.com (Draxlr) — Tue, 23 Dec 2025 00:00:00 GMT

If you are a SaaS founder, Stripe is usually a daily check. You open it, refresh, scan the numbers, and move on. Instead of doing that manually, it is easier to get a short daily Slack message that tells you: 1. What is the gross revenue 2. What is our current MRR This guide shows the easiest and free way to do that using Revenue Tracker, a free tool built by Draxlr that sends your Stripe MRR and gross revenue straight into the Slack channel you choose. --- ## MRR vs Gross Revenue: Quick Definitions ### 1. What is Monthly Recurring Revenue (MRR)? MRR is your predictable recurring subscription revenue normalized to a monthly number. Stripe describes MRR as a way to measure predictable recurring income and to forecast and track growth trends. If your business is **subscription based**, MRR is the heartbeat that shows whether you are building a stable base. ### 2. What is Gross Revenue? Gross revenue is the total amount collected from successful payments in a given time period. A daily alert turns that into one number for yesterday, so you can track revenue day by day. MRR is your recurring baseline. Gross revenue is your daily total. A simple example: - Yesterday you receive a few large annual payments, so gross revenue for the day goes up. - But MRR may stay mostly the same, because those annual payments are spread across months when converted into a monthly recurring number. That is why it helps to see both. One shows what came in yesterday, the other shows your steady recurring baseline. --- ## Why send Stripe MRR and revenue alerts to Slack Slack is where you as a SaaS founder already spend most of your day, so it is a convenient place to get a daily revenue check. Instead of opening Stripe, clicking around, and repeating the same steps, you get one short message that tells you what you need. Benefits: - You see your numbers without switching tabs - The update is consistent, so you do not forget to check - You can scroll back and compare days quickly Keep it as a summary. One message a day is enough. --- ## How to send Stripe notifications to Slack Before we get to the free setup, here are the common options you can consider. ### Option 1: The Stripe app for Slack Slack has an official Stripe app that can post updates for events like charges and subscriptions. This is useful if you want event notifications. The downside: It is not designed to be a clean daily digest of MRR and gross revenue. Event streams can become noisy fast. ### Option 2: Automation tools like Zapier Automation tools can connect Stripe triggers to Slack messages. For example, posting to Slack when a new Stripe customer is created. The downside: Most setups become event based, not summary based. Also, free tiers often limit how many automations you can run or how frequently they run. ### Option 3: Build your own Stripe webhook to Slack Stripe webhooks are powerful and flexible, and many teams build custom pipelines for internal notifications. The downside: You need to host an endpoint, handle retries, verify signatures, and maintain the integration over time. It works, but it is not the “free and simple” path most founders want. ### Option 4: Use Revenue Tracker for free Revenue Tracker is a free tool built by Draxlr that sends your Stripe MRR and gross revenue straight into Slack. What it does: - You connect Stripe and Slack once - It calculates your previous day’s MRR and gross revenue - It sends a simple daily summary to the Slack channel you selected Try Revenue Tracker It is intentionally not a heavy analytics product. The goal is a reliable daily revenue heartbeat in Slack. Important trust details from the product FAQ: - It only reads data from your Stripe account to generate alerts - It does not modify your Stripe data or settings - It is completely free for sending daily MRR and gross revenue alerts to Slack --- ## How to set up Revenue Tracker to get Stripe MRR and gross revenue alerts to Slack ### Step 1: Sign up On the [Revenue Tracker page](https://www.revenuetracker.co/), you can start the setup to send Stripe MRR and gross revenue updates to Slack. Sign Up to Revenue Tracker ### Step 2: Connect Stripe ![Connect Stripe](/images/blogs/stripe-mrr-gross-revenue-alerts-slack/images/1.png) Link your Stripe account so Revenue Tracker can compute daily MRR and gross revenue. This is read only access for generating alerts, not for changing anything in Stripe. ### Step 3: Connect Slack Choose the Slack workspace where you want the daily message to be present. ### Step 4: Select a Slack channel for the alerts ![Home page](/images/blogs/stripe-mrr-gross-revenue-alerts-slack/images/2.png) Pick a Slack channel that you personally check every day, so the update is easy to notice without changing your routine. Many founders use a private channel or a dedicated channel like `revenue`. Revenue Tracker will send the daily summary to the channel you select. --- ## What the daily Slack message looks like ![Slack message](/images/blogs/stripe-mrr-gross-revenue-alerts-slack/images/3.png) Revenue Tracker is designed to be easy to scan. The Slack message shows: - Today’s Gross Revenue with a percentage change - Monthly Recurring Revenue (MRR) with a percentage change This formatting works well for founders because: 1. You get the number immediately 2. You see whether it is up or down at a glance 3. You can scroll back and compare recent days without opening Stripe --- ## Best practices to get real value from daily revenue alerts ### 1. Choose the right channel A good default is a channel that includes founders and whoever owns growth and retention. If the channel is too broad, people mute it.
If the channel is too private, it does not build team alignment. ### 2. Create a tiny daily ritual A simple routine that works: 1. Check the message 2. If there is a spike or dip, add a one line note in a thread That is enough to build context over time. ### 3. Keep the alert summary focused Daily digests work because they avoid alert fatigue. If you also need per payment events, use the Slack Stripe app for that, and keep your daily digest separate. ### 4. Use MRR and gross revenue together to diagnose faster If gross revenue drops but MRR stays stable: - Look for refunds, payment failures, or one time purchase dips If MRR drops: - Look for churn, downgrades, or cancellations MRR is tied to your active subscription base, so it is an early signal for retention work. --- ## Conclusion If your goal is simple, daily visibility without extra cost, Revenue Tracker built by Draxlr is the cleanest path to get revenue updates. Once it is set up, you get the revenue update every day, in the place you already work. --- ## FAQs ### 1. What does Revenue Tracker actually do? It sends a daily Slack message with your previous day’s MRR and gross revenue from Stripe. ### 2. Is this only for Stripe? Yes, it is for Stripe currently. ### 3. Do I need a data warehouse or a BI tool? No. Revenue Tracker plugs into billing data directly and focuses on alerts, not dashboards. ### 4. Does this modify anything in my Stripe account? No. It only reads Stripe data to generate alerts and does not change your data or settings.

How to build Dashboards from MariaDB data

hello@draxlr.com (Draxlr) — Thu, 04 Dec 2025 00:00:00 GMT

**MariaDB** is great for shipping product fast. But once users start signing up, you’ll eventually need a clear way to track what’s happening like signups, usage, revenue, orders, errors, subscriptions, anything that matters to your SaaS. That’s the real gap not the data itself, but the lack of a clear, accessible way to **build MariaDB dashboards** that everyone on your team can read and understand without touching SQL. What you need is a simple, repeatable path from raw MariaDB tables to decision-ready dashboards. And that’s exactly what this guide gives you. --- ## What you need before you start building MariaDB dashboards Before you start building MariaDB dashboards, it’s worth taking a moment to make sure your MariaDB setup is ready for analytical workloads. These small adjustments help dashboards load faster, keep queries consistent, and prevent unnecessary load on your production environment. A bit of preparation upfront makes it much easier to build MariaDB dashboards that stay accurate as your data grows. Your quick setup checklist: - **Use a read replica or reporting instance**
Offload dashboard queries from your primary database to avoid performance impact on live users. - **Index frequently filtered columns**
Fields like dates, user IDs, statuses, and product identifiers should have indexes to keep analytical queries fast. - **Standardize time zones across tables**
Using a consistent time zone (preferably UTC) prevents mismatched results when visualizing trends over time. - **Ensure primary keys are consistent and reliable**
Clean, unique, predictable primary keys make joins easier and dashboards more stable. - **Review table naming and relationships**
Consistent naming helps both your team and your SQL dashboard tool understand the schema more intuitively. You don’t need a full data warehouse to **build MariaDB dashboards**, but preparing your database with these basics will save you significant effort later especially as dashboards become a core part of how your team reads data. --- ## Choose the right SQL dashboard tool for building MariaDB dashboards Once your database is ready, the most important decision is selecting the SQL dashboard tool that will sit on top of MariaDB. The right tool determines how quickly you can build MariaDB dashboards, how easily your team can explore data, and how much ongoing engineering effort you’re committing to. To build MariaDB dashboards efficiently, you need a SQL dashboard tool that works with your schema as it is, respects SQL logic, and keeps your reporting fast and maintainable. ### What to look for in a MariaDB dashboard building tool: - **Native MariaDB connectivity**
The tool should connect directly using secure credentials with no complicated middleware or sync processes. - **Support for SQL-first workflows**
MariaDB data is often best accessed through SQL. A tool that supports writing, editing, and previewing SQL gives you full control without workarounds. - **Multiple ways to build queries**
Teams need flexibility. Look for: - A **Visual Query Builder** for non-technical users - **AI text-to-SQL** for quick exploration - A **full SQL editor** for deeper analysis - **Fast, flexible visualization options**
Line charts, bar charts, KPIs, tables, and filters should be easy to create and rearrange. - **Secure sharing and access control**
The tool should let you share dashboards without exposing your database or allowing unintended access. - **Embedding capabilities**
If you ever want customer-facing dashboards, embedding should be available. A SQL dashboard tool that offers these capabilities will give you a clean, scalable foundation for building MariaDB dashboards your entire team can rely on. --- ## Build dashboards from MariaDB data using Draxlr Once you’re ready to analyze your data, Draxlr gives you a complete workflow starting from connecting your MariaDB instance, all the way to building, sharing, and automating dashboards. Everything lives in one place, making it easy to build MariaDB dashboards without maintaining any reporting infrastructure yourself. ### 1. Connect your MariaDB database Add your database credentials (host, port, username, password, and database name) to connect. Draxlr instantly detects your tables and schema, so you can start exploring data without any additional setup. ![MariaDB data selector](/images/blogs/how-to-build-dashboards-from-mariadb-data/images/database-selector.png) ### 2. Create queries using the method that suits you Draxlr offers three flexible ways to build queries, depending on your comfort with SQL: - **Visual Query Builder** for selecting tables, joining data, filtering, and aggregating with few clicks. No SQL needed. - **AI Chat (text to SQL)** to describe your requirement in plain English and let Draxlr generate the SQL. - **Full SQL Editor** for maximum control when you want to fine-tune advanced queries. Every query you save becomes a reusable building block for future dashboards.

### 3. Convert your queries into dashboards Turn your saved queries into visual insights by mapping them to: - Line, bar, donut charts, and more advanced charts - KPI widgets and number blocks - Tables for detailed views - Time-series charts for trends and comparisons You can freely arrange widgets to design dashboards that match your team’s workflow. ### 4. Add filters and drilldowns for deeper exploration Draxlr makes dashboards interactive by allowing you to: - Add global filters like date range, region, or customer segment - Drill into charts to view the underlying query results - Enable dynamic filtering without modifying the SQL This makes dashboards explorable rather than static snapshots. ### 5. Share and Embed insights securely Draxlr ensures dashboards can be shared without exposing your database: - Invite teammates with controlled access - Share private dashboard links - Embed dashboards inside your SaaS product with full white-label options Everyone gets access to insights, while your data stays protected. ### 6. Automate alerts and scheduled reports Set up email or Slack summaries and KPI-based alerts so your team gets updates automatically. This ensures key metrics are monitored even when no one is checking the dashboard. With Draxlr, you go from a raw MariaDB connection to fully interactive dashboards in just a few steps—fast, simple, and designed for real-world teams.

--- ## Common mistakes to avoid when building MariaDB dashboards When teams start building dashboards from MariaDB, the challenges usually aren’t technical they come from small decisions that slowly create inconsistencies or performance bottlenecks. Keeping these mistakes in check ensures your dashboards stay clean, dependable, and easy to manage. - **Treating every dashboard as a standalone project** When each dashboard uses its own version of a metric, teams end up with conflicting numbers. Centralizing logic in reusable queries prevents this fragmentation. - **Connecting with accounts that have overly broad permissions** Dashboards don’t need write access or full admin rights. Using a minimal, read-only user protects your production environment and reduces risk. - **Building dashboards before agreeing on metric definitions** If “active user,” “MRR,” or “conversion” mean different things to different teams, no dashboard can fix that. Define key metrics before visualizing them. - **Ignoring how data will grow over time** A query that works today may not scale when the table has 50 million rows. Designing with growth in mind (proper filters, date ranges, pagination) keeps dashboards fast long-term. - **Hardcoding time filters, statuses, or segments inside SQL** This forces you to edit queries every time business logic changes. Using dynamic filters in your BI tool keeps dashboards flexible and reduces maintenance. These are the types of issues that quietly undermine analytics workflows. Avoiding them makes it far easier to build MariaDB dashboards that stay accurate, scalable, and consistent as your organization grows. --- ## Conclusion Building dashboards from MariaDB doesn’t need a complex data pipeline or a dedicated analytics team. With a few preparations, the right SQL dashboard tool, and a simple workflow, you can turn your existing tables into interactive dashboards your team actually understands. From connecting your database to creating reusable queries, adding filters, sharing insights, and automating updates, the entire process becomes straightforward once everything lives in one place. If your goal is to **build MariaDB dashboards** that are live, consistent, and easy for everyone to explore, Draxlr gives you a clean path from raw tables to decision-ready insights without adding engineering overhead or maintaining internal reporting tools. Connect your MariaDB Database --- ## FAQs ### 1. Do I need a data warehouse to build MariaDB dashboards? No. You can build MariaDB dashboards directly on your live database as long as you use a read-only user or a read replica. A data warehouse is only necessary when your dataset becomes too large or your reporting logic becomes heavily aggregated. ### 2. Can non-technical users build dashboards from MariaDB data? Yes. Tools like Draxlr provide visual query builders, filters, and drag-and-drop dashboards so users can explore data without writing SQL. ### 3. How do I keep my MariaDB dashboards accurate over time? Define metrics clearly, reuse saved queries instead of duplicating SQL, and standardize timestamps across tables. These small steps prevent inconsistencies as your data grows. ### 4. Can I embed MariaDB dashboards inside my product or internal tools? If your SQL dashboard tool supports embedding, you can publish dashboards directly inside your SaaS app or admin panel while keeping your database secure. ### 5. What’s the easiest way to start building MariaDB dashboards? Prepare your database (indexes, read-only access), connect it to a SQL dashboard tool like Draxlr, create reusable queries, and turn them into interactive dashboards with filters and [drilldowns](/features/drill-down/).

How to build dashboards from Neon database

hello@draxlr.com (Draxlr) — Wed, 03 Dec 2025 00:00:00 GMT

Neon database has quickly become a favorite among developers who want a modern, scalable, serverless Postgres database without the overhead of managing infrastructure. With features like branching, autoscaling, and storage optimization, Neon offers a frictionless way to run Postgres in the cloud. But once your application is storing data in Neon, the next logical step is turning that data into insights - and that’s where **Draxlr** comes in. **Draxlr** allows you to visualize your Neon Postgres data, build dashboards, monitor key metrics, and share insights with your team - all without maintaining your own analytics stack. Whether your team is technical or not, Draxlr turns your Neon into a powerful decision-making engine. In this guide, we’ll walk you through how to connect your Neon database to Draxlr and start building dashboards in minutes. ## Why Use Draxlr with Neon database? Neon database provides a cloud-native Postgres experience that’s built for development speed and reliability. Pairing it with Draxlr gives you: 1. **Zero-config analytics on serverless Postgres**: Draxlr connects directly to your Neon Postgres instance - no middleware, no pipelines. Start exploring your data instantly. 2. **No-code & SQL-friendly workflow**: Non-technical users can explore data visually using Draxlr’s query builder. Developers can write custom SQL for complex analytics. 3. **Dashboards that auto-update**: Every dashboard refreshes automatically based on live data. 4. **Alerts & monitoring**: Set up Slack or email alerts to stay informed about metric changes, anomalies, or scheduled reports. ## Step-by-Step Guide to Integration ### Retrieve your Neon Connection Details 1. Go to [Neon](https://console.neon.tech/) dashboard and select the project you want to connect to Draxlr. 2. On the right side, click on the **Connect** button to view your database connection details. 3. Choose **Connection string** method from the list and copy the connection string. It will look something like this: ``` postgresql://:@.aws.neon.tech/neondb?sslmode=require&channel_binding=require ``` ![Get Neon credentials](/images/blogs/how-to-build-dashboards-from-neon-database/images/connection-info.png) ### Connecting to Draxlr Once you have your Neon credentials ready, you can proceed to configure Draxlr: 1. If you’re new to Draxlr, sign up for an account, or log in if you already have one. 2. New users will land directly on the database connection page. If you already have an account, open the navbar and choose **Select database > + Connect a Database**. Pick `Neon` as your database type and continue. 3. Enter your Neon database information (host, port, username, password, and database name) into the connection form, then click Next. 4. Choose the schemas you want to connect. If you only have one schema, it will be selected automatically. Click Next to proceed. ### Exploring your data You can build queries and use them to create Neon dashboards and set up alerts. There are 3 ways to create a query in Draxlr: 1. **Query Builder**: A no-code feature for generating queries. 2. **Raw SQL**: Write and execute custom SQL queries. 3. **AI-powered SQL Generation**: You can use Draxlr's AI assistant to generate SQL queries based on natural language prompts. #### Create query using Query Builder 1. Go to the **Browse Data** option from the navbar. 2. Click a table from the left-side panel. 3. This will open the Query Builder interface for that table. 4. Use **Filter**, **Join**, **Aggregate**, **Sort**, **Select (columns)**, and **Virtual Column** to shape your data and reach your desired result. ![Creating query](/images/blogs/how-to-build-dashboards-from-neon-database/images/browse-data.png) ### Data Visualisation and Dashboard Turn your raw Neon data into meaningful visuals with Draxlr’s dashboarding features. 1. After running a query, whether through the no-code builder or SQL editor, click the **Graph** button to begin visualising your data. Draxlr supports multiple visualisation types including bar charts, line charts, pie charts, number cards, and more. 2. Once you finalise your visualisation, click **Save query**. Then, use **Add to Dashboard** to place your visualisation onto a custom dashboard. ![Visualising your data](/images/blogs/how-to-build-dashboards-from-neon-database/images/visualization.png) Dashboards can be shared with your team or embedded into internal and customer-facing tools ## Monitoring Your Data Draxlr makes it easy to stay updated on important changes in your data by allowing you to set up automated alerts. Once you've created and saved a query, you can enable monitoring directly from the query panel. You can configure: - **Slack Alerts**: Get instant notifications in your Slack workspace whenever your data crosses a threshold or changes. - **Email Alerts**: Receive timely updates in your inbox based on schedule or data conditions. These alerts help your team react quickly to trends, anomalies, or critical shifts - without needing to manually check dashboards. ### Conclusion Connecting Neon database to Draxlr unlocks a powerful workflow for teams who rely on Postgres and want fast, scalable analytics. With just a few steps, you can bring your Neon data into Draxlr, explore it visually, and build dashboards that empower your entire organisation to make data-driven decisions. Embrace the combined power of Neon database and Draxlr to transform your raw data into actionable insights. Connect your Neon Database \- **Jaskaran Singh**

Supabase Business Intelligence: The Complete Guide

hello@draxlr.com (Draxlr) — Mon, 01 Dec 2025 00:00:00 GMT

Business intelligence (BI) has become essential for SaaS teams working with Supabase. While Supabase gives you a powerful Postgres backend, managing analytics, dashboards, and insights at scale requires more than ad-hoc SQL queries. In this extensive guide, we explore why BI matters for Supabase teams, what challenges developers face as their data grows, and how Draxlr provides a modern, AI-powered, SQL-centric BI layer built specifically for fast-moving engineering teams. --- ## Why Supabase Needs Business Intelligence? Supabase gives developers: - A managed Postgres database - Authentication - Storage - Edge functions - Realtime APIs While this makes it an excellent backend for SaaS teams, analytics quickly become a bottleneck: - Product teams need user metrics. - Sales teams want customer reports. - Founders want dashboards. - Finance teams need revenue insights. Supabase’s built-in SQL editor is powerful but not meant for ongoing BI. As data grows, teams need: - Repeatable dashboards - Shared queries - Visualization tools - Access controls - Embedded analytics This is where a BI layer becomes essential. --- ## Common BI Challenges for Supabase Teams ### a. Rewriting the same queries again and again Teams often duplicate SQL or maintain scripts in Notion, GitHub, or documents. ### b. Building internal dashboards consumes engineering time Creating custom charts, UIs, performance layers, and API endpoints turns into a multi-sprint effort. ### c. No streamlined role-based access Not everyone should see production data. ### d. Ad-hoc SQL is not scalable One-off queries slow down or produce inconsistent insights. ### e. No customer-facing analytics Building embedded dashboards from scratch is expensive. --- ## What to Look for in a Supabase BI Tool A strong BI tool should: - Connect directly to Supabase Postgres - Provide a fast query layer - Support SQL, AI SQL, and visualizations - Enable embedded dashboards - Offer SDKs, APIs, and exports Draxlr checks all these boxes. --- ## Introducing Draxlr: BI Designed for Supabase Draxlr is a modern business intelligence platform built for engineering-led teams who want simple, SQL-friendly analytics. ### Why Supabase developers prefer Draxlr? - Connects directly to Supabase Postgres - No ETL - Setup in under 2 minutes - SQL-first with AI assistance - React/Vue embeds - Multi-tenant support - Clean UI for technical and non-technical users Draxlr turns raw Supabase tables into dashboards within minutes. --- ## Key Draxlr Features for Supabase Analytics ### 1. AI Chat for SQL (text-to-SQL) Draxlr generates SQL queries + charts from natural language prompts such as: - “Weekly active users for last 6 months” - “Group orders by status” - “Monthly revenue in 2024” ![Draxlr AI](/images/blogs/supabase-business-intelligence/ai.png) ### 2. Visual Query Builder A no-code builder with: - Filters - Aggregations - Joins - Sorting - Grouping Great for non-technical teammates. ![Draxlr Query Builder](/images/blogs/supabase-business-intelligence/query-builder.png) ### 3. Full SQL Editor Developers get: - Schema autocomplete - Syntax highlighting - Saved query library - Query variables like `{{country}}` ![Draxlr Raw SQL](/images/blogs/supabase-business-intelligence/raw-sql.png) ### 4. Dashboards & Visualizations Supports: - Line, bar, area, pie charts - KPI cards - Tables - Pivot tables - Drill-through - Filters Dashboards are shareable and embeddable. ![Draxlr Raw SQL](/images/blogs/supabase-business-intelligence/dashboard.png) ### 5. Embedded Analytics Draxlr offers: - React & Vue SDKs - iFrame embeds - Row-level filters - Multi-tenant views - Secure embed tokens Ideal for SaaS customer analytics. ![Draxlr Raw SQL](/images/blogs/supabase-business-intelligence/embed.png) ### 6. Automations - Slack Alert - Email Alert - Google Sheets Export - API Endpoint Useful for signup trends, revenue reports, churn alerts, etc. ![Draxlr Automations](/images/blogs/supabase-business-intelligence/automations.png) --- ## Real-World Use Cases Supabase teams across SaaS, marketplaces, internal tools, and B2B platforms often reach a point where analytics becomes critical. Below is an expanded look at the most valuable use cases—showcasing how Draxlr turns raw Supabase data into clear, decision-ready insights. ### 1. Product Analytics - Feature usage: Track which features users engage with the most. - Retention: Measure how often users return over time. - Cohorts: Compare user groups based on signup date or behavior. ### 2. Sales Analytics - Pipeline: Monitor deals across each sales stage. - Conversions: Analyze drop-offs at every step of your funnel. - Customer activity: Identify active, inactive, and at-risk accounts. ### 3. Revenue Analytics - MRR: View recurring revenue growth over time. - Churn: Detect when and why customers stop using your product. - Subscription health: Track trends across plans, upgrades, and renewals. ### 4. Operational Metrics - Support data: Measure ticket volume, response time, and resolution quality. - Error logs: Spot error spikes and frequently failing actions. - API usage: Understand endpoint activity, latency, and failures. ### 5. Customer-Facing Dashboards - Embedded dashboards: Offer your customers real-time insights directly inside your SaaS UI. --- ## How to Connect Supabase to Draxlr ### Step 1: Go to Add Database and select Supabase Visit: [https://app.draxlr.com/add-database](https://app.draxlr.com/add-database) ![Draxlr Supabase Setup](/images/blogs/supabase-business-intelligence/select-supabase.png) ### Step 2: Login via Supabase and select Supabase Project ![Draxlr Supabase Setup](/images/blogs/supabase-business-intelligence/select-project.png) ### Step 3: Supabase Credentials Draxlr auto-fetches the Database credentials, except the database password. ![Draxlr Supabase Setup](/images/blogs/supabase-business-intelligence/enter-credentials.png) ### Step 4: Select Schema ![Draxlr Supabase Setup](/images/blogs/supabase-business-intelligence/select-schema.png) Usually `public`. Done. --- ## Pricing, Self-Hosting & Security ### Hosted / Cloud Cloud plan starts at **$75 per month** which comes with up to 20 users, 2 Databases, and 100 AI Credits. ### Self-Hosted Deploy inside your VPC via Docker. Plan starts at $500 per month, [learn more](/self-hosted-bi-tool/). --- ## Conclusion Supabase provides a powerful backend, but scaling analytics requires a BI layer built for developers. Draxlr gives teams SQL analytics, AI assistance, dashboards, and embedded reporting—all without the overhead of building your own BI system. **Try Draxlr:** [https://www.draxlr.com](https://www.draxlr.com) --- ## FAQs ### 1. What is Supabase Business Intelligence? Supabase Business Intelligence refers to using analytics tools to visualize, query, and understand your Supabase Postgres data through dashboards, insights, and reporting. ### 2. Why do Supabase teams need a BI tool like Draxlr? Because Supabase’s SQL editor is not built for recurring analytics, teams use Draxlr to create dashboards, run AI-generated SQL, share insights, and embed reports without engineering overhead. ### 3. Does Draxlr connect directly to Supabase without syncing? Yes. Draxlr runs queries directly on your Supabase Postgres database—no ETL, pipelines, or data copying required. ### 4. Can I embed Draxlr dashboards inside my Supabase-powered SaaS product? Absolutely. Draxlr supports React, Vue, and HTML embeds with secure tokens, row-level filters, and full multi-tenant support.

Best Supabase Reporting Tools

hello@draxlr.com (Draxlr) — Sun, 30 Nov 2025 00:00:00 GMT

Supabase gives you a solid Postgres backend and a fast dev workflow, but the built-in SQL editor eventually stops being enough. As usage grows, you need dashboards, safer data access, and repeatable reporting without exposing production or writing ad-hoc queries all day. Engineering teams often start by [building internal dashboards](https://www.draxlr.com/blogs/how-to-create-and-embed-a-dashboard-for-your-supabase-database-using-draxlr/) themselves, only to discover how much work is involved. Normalizing tables, writing reusable queries, building UI components, managing auth, optimizing performance, and maintaining visualizations over time quickly becomes an ongoing project. If you'd rather not allocate a sprint (or several) to building your own reporting layer, **Supabase reporting tools** handle the heavy lifting. They connect directly to Supabase, give your team controlled access to data, and provide dashboards without needing to reinvent analytics inside your app. In this article, we break down the most reliable Supabase reporting tools to consider for 2026. --- ## What are Supabase reporting tools? **Supabase reporting tools** connect directly to your Supabase Postgres database and give you dashboards, charts, and shared reports without writing SQL for every question. They replace the need to build internal analytics yourself by providing a clean interface for exploring data, applying filters, and sharing insights across your team. Because these tools use Supabase’s native Postgres connection, there’s no need for ETL pipelines, duplicated datasets, or a separate warehouse. Queries run directly against your schema, permissions remain consistent with your Supabase setup, and your data stays live, secure, and accessible to both technical and nontechnical users. --- ## How to choose a Supabase reporting tool Choosing the right Supabase reporting tool depends on what you want to achieve with your data. A small SaaS team that needs quick product visibility will prioritize very different features compared to a growing company that wants embedded analytics for customers or more formal reporting for internal teams. Start by identifying your primary goal. ### 1. If your goal is internal reporting for your team Pick a tool with strong SQL support, an intuitive dashboard builder, and simple sharing. You want clarity without adding another system to maintain. ### 2. If your goal is customer-facing analytics Look for embedding, white-label controls, and row-level filtering. Your users should see analytics that look like part of your product, not an add-on. ### 3. If your goal is replacing an existing analytics stack Prioritize tools with flexible permissions, reusable queries, scheduling, and scalable embedding. These features streamline the entire analytics layer. ### 4. If your goal is minimal engineering involvement Pick a tool that lets product, support, or operations teams explore data on their own. Features like visual query builders and AI reduce the number of ad-hoc requests developers get pulled into. Ultimately, the right Supabase reporting tool depends on whether you prioritize speed, customer experience, or long-term scalability. --- ## Key features to look for in Supabase reporting tools A quick search for Supabase analytics or reporting tools will surface plenty of options, but they’re not all built the same. Choosing the right one becomes much easier when you know which capabilities matter most for teams working directly on Supabase Postgres. Here are the core features to evaluate when comparing Supabase reporting tools. ### 1. Native Postgres compatibility Your Supabase reporting tool should connect directly to Supabase Postgres without ETL, sync jobs, or schema duplication. Native support means: 1. Queries run on your actual schema 2. No pipelines 3. No new infrastructure 4. Consistent permissions Tools that expect you to model data or move it into a warehouse will slow you down and break your Supabase-first workflow. ### 2. Ease of dashboard building Most teams using Supabase don’t have a dedicated data department, so reporting has to be accessible. Look for features like: 1. Visual query building 2. [Text to SQL AI](https://www.draxlr.com/blogs/ai-sql-tool/) 3. Reusable charts 4. Filters & drilldowns 5. Clear layout controls 6. [Simple sharing](https://www.draxlr.com/blogs/create-a-public-dashboard-url-for-your-sql-database/) The goal should be anyone on your team should be able to answer basic questions without developer involvement. ### 3. Embeddability (if you want customer-facing dashboards) If you plan to [embed analytics](https://www.draxlr.com/blogs/best-embedded-analytics-tools/) inside your product, choose a tool with: 1. Secure, stateless embed tokens 2. Row-level filtering for multi-tenant apps 3. White-label options Dashboards should feel native inside your product not like an iframe pasted on top. ### 4. API access for automation Supabase teams often automate workflows like report generation, alerts, scheduled queries, or embedding logic. An [API-first reporting tool](https://www.draxlr.com/blogs/pull-data-from-api-to-supabase/) gives you: 1. Programmatic control 2. Flexibility for future integrations 3. Easier connection to your auth layer 4. Automation of recurring tasks Developers should be able to trigger or manage analytics via API just like any other part of their stack. ### 5. Data security & access control Your Supabase reporting tool must respect the same security expectations your app has. Important features include: 1. No data copying 2. User-scoped dashboards 3. Secure embedding 4. Strong permission controls 5. [Self-host option](https://www.draxlr.com/blogs/ensuring-seamless-adoption-of-self-service-bi/) Since everything is tied to your live production schema, access control is critical. ### 6. Documentation, support, and reliability Reporting is often implemented late in the product cycle and usually when teams are under pressure. Good documentation, responsive support, and a reliable UI save hours of frustration. Whether through docs, Slack communities, or hands-on support, pick a tool that won’t leave you stuck when your team needs answers fast. --- ## The best Supabase reporting tools to consider in 2026 Without further delay, here’s a breakdown of the top Supabase reporting tools worth exploring in 2026 and beyond. ### 1. Draxlr: Best Supabase reporting tool for dashboards, team insights, and embedded analytics #### Best for SaaS teams using Supabase who want a straightforward way to build dashboards, explore data, and offer embedded analytics without introducing a complicated BI stack. Connect your Database [Draxlr](https://www.draxlr.com/) works directly with your Supabase database and gives teams a clean interface for creating reports, running SQL, and building dashboards without setup overhead. Built for modern SaaS products, it helps teams ship reporting fast, whether the goal is internal visibility or customer-facing analytics. For technical users, Draxlr offers full SQL control, virtual columns, filters, drill-through features, and database-aware autocomplete. For nontechnical teammates, the visual query builder and AI text-to-SQL make data exploration accessible without needing to touch the SQL editor. If your product includes customer analytics, Draxlr supports secure embedded dashboards with row-level filtering, white-label options, and React/Vue components that integrate naturally into your app. Teams can also automate reporting with scheduled emails, Slack alerts, and CSV/Excel exports. Draxlr runs on your existing Supabase infrastructure — no pipelines, warehouses, or modeling layers required. Everything connects directly to your live schema. #### Key features 1. Connects directly to Supabase Postgres with no ETL or prep work 2. Visual Query Builder, full SQL editor, and AI Chat for text-to-SQL 3. Dashboard builder with filters, drill-through, and virtual columns 4. Embedded dashboards with secure tokens, row-level filters, and white-label controls 5. React and Vue SDKs for easy in-product analytics 6. Export options (CSV, Excel, PDF) and scheduled reports 7. Team-level permissions for safe access to production data 8. Supports both internal reporting and customer-facing analytics from the same workspace #### What users say about Draxlr > Draxlr offers a clean, modern interface and makes it incredibly easy to build dashboards — even for non-technical users. I especially like the visual quality and flexibility of the available graph types. It's intuitive, fast to set up, and has excellent usability from the start. The ability to securely share dashboards via link is a game-changer for our client reporting. — [Review from a SaaS Founder on G2](https://www.g2.com/products/draxlr/reviews/draxlr-review-11185807) #### Pricing Draxlr offers multiple plans depending on team size, embedding needs, and data volume. Pricing starts at **$75/month** ### 2. Power BI: Enterprise reporting for teams already in the Microsoft ecosystem #### Best for Large organizations that already use Microsoft tools and want to add BI dashboards connected to Supabase via Postgres. [Power BI](https://www.microsoft.com/en-us/power-platform/products/power-bi) is a feature-rich, enterprise-focused reporting tool built for analyst-driven workflows. It connects to Supabase through Postgres credentials, allowing teams to build complex dashboards and run advanced modeling. It’s powerful, but often heavier than what most SaaS or product teams need for day-to-day reporting. #### Key features 1. Advanced modeling and DAX expressions 2. Large library of visualizations 3. Enterprise sharing and workspace controls #### What users say about Power BI > What I like most about Power BI is how it turns complex data into something easy to understand. The ability to create interactive dashboards and dynamic reports is a game changer. #### Pricing Starts with a free tier. Pro and Premium plans available. ### 3. Tableau: Advanced data visualization for analyst-heavy teams #### Best for Organizations that need sophisticated visualizations and already operate with a BI analyst workflow. [Tableau](https://www.tableau.com/) offers some of the most powerful visualization capabilities in the industry. It connects to Supabase’s Postgres database and enables deep analytical exploration. However, it requires setup, modeling, and ongoing BI resources — making it better suited for enterprise teams than fast-moving SaaS products. #### Key features 1. Complex visualizations and chart types 2. Tableau Prep for data modeling 3. Strong enterprise governance #### What users say about Tableau > What I like best is Tableau’s design philosophy — it emphasizes data exploration and visual storytelling over just rigid reporting. #### Pricing Premium pricing tailored toward enterprise and team deployments. ### 4. Holistics: SQL-centric BI with modeling for data teams #### Best for Teams that want a model-driven reporting workflow and prefer SQL-based data modeling over drag-and-drop dashboards. [Holistics](https://www.holistics.io/) integrates with Supabase via Postgres and offers a semantic modeling layer, scheduled reports, and dashboard automation. It’s designed for data teams who want to define metrics centrally and reuse them across dashboards, rather than building charts directly against raw tables. More structured, but heavier than what most early-stage Supabase teams need. #### Key features 1. SQL-based data modeling layer 2. Scheduled email reports and automation 3. Reusable metrics and dataset definitions #### What users say about Holistics > It's very easy to use, very intuitive. It has all the data visualizations that an organization might need, but it has the capability to let you create new visualizations via (xml- vega lite visualizations). #### Pricing Holistics provides team-based pricing with tiers based on usage and features. ### 5. Apache Superset: Open-source BI for teams with engineering support #### Best for Companies that want an open-source BI platform, have in-house engineering capacity, and don’t mind managing their own analytics infrastructure. [Apache Superset](https://superset.apache.org/) is a powerful open-source analytics tool that connects to Supabase Postgres and supports SQL exploration, interactive dashboards, and a wide selection of visualizations. It’s a strong option if you want full control over your reporting stack and are comfortable hosting and maintaining the platform yourself. #### Key features 1. SQL IDE and visual dashboard builder 2. Broad library of charts and visualizations 3. Fully open-source and self-hosted #### What users say about Apache Superset > We can combine data from different systems for thorough analysis because the platform supports an extensive amount of data sources, including databases like Postgres. #### Pricing Free and open-source operational costs depend on hosting and maintenance. --- ## Implementing your Supabase reporting tool effectively Choosing the right Supabase reporting tool is only half the job. A tool can give you clean dashboards and powerful querying, but it won’t deliver value on its own. Like any part of your product, success depends on how well you implement it and how quickly your team starts using it. Here’s how to get the most out of whichever Supabase reporting tool you choose. ### 1. Onboarding your team Invest time in proper onboarding. Developers, product managers, support teams, and anyone responsible for reporting should know how the tool works from day one. A short, focused onboarding session prevents future bottlenecks and avoids misconfigurations that lead to bad data. ### 2. Start with a pilot Instead of rolling dashboards out to everyone at once, start with a small group. A controlled pilot helps you validate metrics, refine permissions, and confirm that the dashboards you built actually answer the questions teams have. It also reduces the chance of misaligned definitions or noisy, unused reports. ### 3. Collect feedback continuously Reporting is not a “set it and forget it” feature. Once dashboards go live, actively ask your users — internal or customer-facing — what works and what needs improvement. As your product evolves, your reporting layer should evolve alongside it. ### 4. Monitor performance and usage Track how often dashboards are viewed, which queries run most frequently, and which reports are ignored. If certain dashboards never get opened, refine them. If certain queries slow down, optimize them. Understanding how your users interact with analytics helps you measure ROI and improve reporting over time. --- ## Conclusion The Supabase reporting tool you choose becomes part of your product’s identity. It shapes how teams understand what’s happening in the business and how your customers interpret their own data. It needs to feel consistent with your product, load reliably, and give users the clarity they’re looking for. If you want a reporting layer that works naturally with Supabase without extra infrastructure, pipelines, or modeling tools like Draxlr help you ship dashboards quickly while keeping your stack simple. Whether you need internal visibility, customer-facing analytics, or embedded dashboards inside your product, the right tool should help you move fast without compromising on clarity or experience. If you’d like to explore a Supabase-native approach to dashboards and reporting, you can [try Draxlr](https://app.draxlr.com/register/) and see how it fits your workflow. --- ## FAQs ### 1. What is a Supabase reporting tool? A Supabase reporting tool connects directly to your Supabase Postgres database and lets you build dashboards, charts, and reports without writing SQL for every request. These tools help teams explore live data safely without setting up ETL pipelines or extra infrastructure. ### 2. Can I build customer-facing dashboards with Supabase? Yes. Several reporting tools support embedding dashboards inside your product. Look for options like secure embed tokens, row-level filtering for multi-tenant applications, and styling controls so the dashboards match your product’s UI. ### 3. Does Supabase have built-in reporting? Supabase has a SQL editor suitable for development and debugging, but it’s not designed for dashboards or ongoing reporting. ### 4. Is it safe to connect a reporting tool directly to Supabase? Yes, as long as the tool respects Postgres permissions, uses secure connections, and supports user-level filtering when embedding dashboards. Most modern reporting tools offer these safeguards. Avoid tools that require exporting or duplicating your data. ### 5. Can non-technical team members use Supabase reporting tools? Yes. Choose a tool that offers visual query builders, saved reports, dashboard filters, and easy sharing so non-technical users can work with Supabase data without writing SQL. ### 6. Do I need a data warehouse for reporting on Supabase? No. Supabase uses Postgres, and most reporting tools connect directly to it. Unless you have very large datasets or complex analytical workloads, a warehouse isn’t required. Direct connections are faster to set up and easier to maintain.

How to embed a PostgreSQL dashboard in your app

hello@draxlr.com (Draxlr) — Wed, 19 Nov 2025 00:00:00 GMT

**Embedded PostgreSQL dashboards** have become a reliable way for SaaS teams to add in-app analytics without taking on the work of building a reporting system. When your product already runs on PostgreSQL, the simplest upgrade you can offer users is a clean, embedded dashboard that loads right inside your UI. No separate tabs. Instead of writing and maintaining your own analytics layer, you can connect Postgres to an [embedded analytics tool](/embedded-analytics-tool/), design the dashboard once, and embed PostgreSQL dashboard directly into your application as a secure, controlled view. Users get the exact dashboard you want them to see, backed by your live PostgreSQL data, and your team avoids taking on a long-term BI engineering project. This guide shows how to embed Postgres dashboards, what to consider before implementing them, and how to set up a production-ready embed flow using Draxlr. --- ## Why embed a Postgres dashboard instead of building analytics yourself Using an **embed Postgres dashboard** setup removes most of the engineering complexity involved in building analytics directly into your product. Instead of maintaining your own charting system, filters, access rules, and SQL logic, **embedded dashboards** handle the entire reporting layer for you. Here’s what embedding helps you avoid: - **Custom chart UI development** — no need to maintain libraries, styling, responsiveness, or consistent layouts. - **Manually managing SQL queries** — the BI layer handles query generation and optimization for your PostgreSQL data. - **User-level permissions** — built-in filtering ensures each user sees only their allowed data. - **Multi-tenant logic** — no separate routes, conditionals, or database guards to manage. - **Versioning dashboards** — updates happen in your BI tool, and your app automatically serves the newest version. - **Performance tuning** — caching and query optimization are handled outside your app code. - **Long-term upkeep** — no deployments for small metric changes or layout adjustments. Choosing a **PostgreSQL dashboard embed** lets your team ship analytics quickly while keeping your developers focused on core product work—not a BI system. --- ## How Embedded Dashboards Work With PostgreSQL The workflow is simple: 1. Connect PostgreSQL to your BI tool 2. Build your dashboard 3. Configure which rows each user can see 4. Enable embed mode 5. Add the embed link or component inside your product Your database remains your source of truth. The BI layer reads from it securely and sends the dashboard to your UI as an iframe or a front-end component. --- ## How to build and embed a PostgreSQL dashboard with Draxlr ### 1. Connect your PostgreSQL database To connect your PostgreSQL database to Draxlr, enter your database credentials or use the “Import from URL” option if you already have a Postgres connection string. If your PostgreSQL server uses IP allowlists, add the Draxlr IP shown on the screen to grant access. SSH connections are supported as well, so you can keep your PostgreSQL instance behind a secure tunnel while still using Draxlr in real time. Once connected, Draxlr automatically reads your schema and makes your tables available for building embedded dashboards. ### 2. Build your dashboard Once your PostgreSQL database is connected, you can start building your dashboard. Draxlr gives you three ways to work with your data: - **Visual Query Builder** for building queries without writing SQL through clicks. - **AI SQL** for generating queries from plain text prompts. - **Raw SQL** if you prefer full control. Every chart, table, and metric is powered directly by your live PostgreSQL data. You can mix query types within the same dashboard, rearrange elements with drag-and-drop, and adjust settings without touching your application code. As you build, Draxlr stores your queries and structure so the final dashboard is ready for embedding. Any changes you make later, new filters, edits to a query, or layout adjustments, update instantly across all your embedded dashboards. ### 3. Set access rules Before embedding, define how each user’s data should be filtered. Draxlr lets you pass identifiers like `user_id`, `org_id`, or any custom field from your application and applies these values to the dashboard in real time. You can configure: - **Row-level filters** to restrict data per user or organization - **Parameter mapping** to inject values from your backend - **Dashboard permissions** to control who can view each embed Once access rules are set, every embedded dashboard loads with the correct PostgreSQL data without custom filtering logic in your frontend or backend. ### 4. Configure embed settings Open the Embed page to customize how the Postgres dashboard should appear inside your application. You can enable or disable export buttons, adjust background colors, hide the header, configure drill-down behavior, and control other UI elements. These settings allow you to tailor the embedded dashboard so it matches your product’s design and user experience. ### 5. Manage dashboard filters If your embedded dashboard needs filters, configure them directly in the Embed page. Enable or disable filters, choose operators such as BETWEEN or IN, and set default values as needed. Any filter you activate becomes part of the embed, giving users the ability to refine their view without leaving your application. ### 6. Copy your embed code Once everything is configured, copy the generated embed code. Draxlr provides: - **HTML embed** - **React component** - **Vue component** If you need more control—such as injecting backend parameters, you can enable the backend API mode. Paste the embed code into your application, and the dashboard loads instantly with your PostgreSQL data. --- ## A typical Postgres dashboard embed in Draxlr looks like

--- ## Common use cases for a Postgres dashboard embed Teams typically embed dashboards for: - Client portals - Usage analytics inside SaaS products - Operational reporting for customers - Partner or reseller dashboards - Internal tools that need analytics but not a full BI interface Each viewer sees only the data they’re allowed to see. --- ## What a good Postgres dashboard embed setup should include 1. **Direct PostgreSQL connection:** Avoid systems that require pushing data to an external warehouse. 2. **Server-side filtering:** User-scoped rules must be enforced on the backend, not through client-side logic. 3. **Clean embedding:** No-edit mode, responsive layout, consistent theming. 4. **No-code dashboard updates:** Your team should be able to update visuals without releasing new code. 5. **Stable performance:** The BI layer should generate optimized SQL and handle caching. Draxlr is built around these requirements specifically for operational databases like Postgres. --- ## Conclusion Embedded PostgreSQL dashboards empower SaaS teams to deliver rich, real-time analytics directly inside their applications without the burden of building and maintaining complex BI infrastructure. By embedding live, secure dashboards tailored to each user’s data, your product stays focused, your engineering team stays agile, and your customers get instant, actionable insights exactly where they need them. Don’t settle for disconnected reporting or delayed insights. Embrace embedded dashboards to accelerate your analytics delivery, improve user engagement, and reduce maintenance overhead. Get started today with **Draxlr** and transform how your SaaS delivers value faster, smarter, and easier than ever before. ## FAQs ### 1. What is an embedded Postgres dashboard? An embedded Postgres dashboard is a dashboard that’s built in a BI tool but displayed inside your own application using an embed code or component. ### 2. Is it safe to embed dashboards connected to my PostgreSQL database? Yes. The embed only displays the dashboard not your credentials. Row-level filtering, JWT tokens, and parameter mapping ensure users see only their allowed data. ### 3. Will my embedded dashboard update automatically if the data changes in PostgreSQL? Yes. Since the dashboard queries your live PostgreSQL data, embedded dashboards always reflect the latest information without redeploying your application. ### 4. What does embedded analytics mean? Embedded analytics refers to analytics components—dashboards, charts, metrics, or reports that are integrated directly into your application rather than shown in a separate BI tool. ### 5. Can I embed Postgres dashboards in React, Next.js, or Vue? Yes. Most BI tools like Draxlr, provide HTML, React, and Vue embed snippets that you can drop directly into your components or pages. ### 6. What are the best embedded dashboard tools? Some of the commonly used embedded dashboard tools include **Draxlr**, **Looker**, **Tableau Embedded**, and **Power BI Embedded**. Each offers a different approach to embedding, depending on whether you need direct SQL access, multi-tenant control, or a full enterprise analytics stack.

How SaaS Startups Use Embedded Dashboards to Retain Customers

hello@draxlr.com (Draxlr) — Mon, 10 Nov 2025 00:00:00 GMT

In the SaaS world, retention is survival. You can’t out-spend churn forever — sooner or later, growth comes down to one thing: how well you help customers realize value. That’s where embedded dashboards come in. Startups that integrate analytics directly into their product help users _see_ value faster, engage more deeply, and make their product indispensable. Embedded dashboards turn your app into a data companion — one that doesn’t just store or process information but actively translates it into insight. Let’s break down **why embedded dashboards are a retention engine for SaaS founders**, and how leading startups use them to turn one-time signups into long-term customers. --- ## Why Embedded Dashboards Drive Retention ### 1. They deliver instant proof of value Onboarding is the make-or-break moment. When a new user logs in and immediately sees metrics about their own data, they experience that all-important _“aha!”_ moment. That early validation keeps them engaged — and paying. ### 2. They deepen engagement Once users start checking dashboards for progress, performance, or ROI, your product becomes a habit. Dashboards drive repeat visits and help you create a product people rely on every week — not just every renewal cycle. ### 3. They empower non-technical users Your customers shouldn’t need to export CSVs or hire analysts just to interpret their data. By embedding analytics inside your app, you make insights accessible to every user — even the least technical. That kind of accessibility keeps customers satisfied and loyal. ### 4. They open up premium and enterprise tiers Founders often overlook this: embedded dashboards aren’t just about retention — they’re about revenue. A strong analytics layer lets you upsell “Pro” or “Analytics” plans that directly tie to customer value. ### 5. They make your product sticky When a dashboard becomes part of a customer’s daily workflow — shared in meetings, reviewed in reports, tied to KPIs — leaving your product isn’t just inconvenient, it’s disruptive. That’s when you know you’ve achieved product-led retention. --- ## How Smart SaaS Founders Implement Embedded Dashboards ### Build vs. Buy You can build dashboards in-house — but maintaining them is a full-time job. Most founders underestimate how fast analytics needs evolve. Embedding a purpose-built analytics tool accelerates go-to-market, reduces engineering overhead, and ensures scalability. ### Contextual insights over vanity metrics Your dashboard shouldn’t just look pretty — it should _help users take action._ Show metrics that move the customer forward: - “Your conversion rate improved 12% this week.” - “Your top customer segment grew 30% faster than average.” - “You’re close to hitting your monthly goal.” Context keeps dashboards meaningful — and customers coming back. ### Self-service customization Founders love metrics; customers love control. When users can filter, slice, or save their own dashboards, engagement skyrockets. Self-service analytics transforms your dashboard from a static report into an interactive experience. ### Consistent, branded UI Dashboards should feel native — not bolted on. The more consistent your color palette, typography, and interactivity are with your main app, the more your users trust the data. ### Use dashboards as engagement triggers Data can be your best retention tool _and_ your best notification system. When embedded dashboards reveal trends — for instance, _“Orders pending shipment increased by 25%”_, _“Subscription renewals due next week”_, or _“API usage nearing your plan limit”_ — that’s your cue to re-engage users through in-app messages, reminders, or upgrade suggestions. These real-time, product-specific insights turn data into action — helping users stay proactive while reinforcing your product’s daily relevance. --- ## Common Embedded Dashboard Use Cases for SaaS Startups ### 1. Usage and activity tracking Show customers how they’re using your product — and where they’re getting results. Seeing progress is addictive. **Example metrics:** active users, features adopted, time saved, goals achieved. ### 2. ROI visibility Visualize the return on investment your product creates. When customers can literally see the ROI, renewals become easy. **Example metrics:** hours saved, revenue generated, tasks automated. ### 3. Customer health monitoring Embed dashboards that help customers monitor their own success — and help you spot churn risks early. **Example metrics:** engagement scores, login frequency, feature usage trends. ### 4. Multi-stakeholder reporting If your customer is a team or organization, give them shareable dashboards. When your charts are being presented in internal meetings, your product becomes the data source that powers their operations. **Example metrics:** team performance, adoption by department, KPI tracking. ### 5. Industry benchmarking Show users how they compare to others in their industry. Benchmark dashboards turn insights into motivation. **Example metrics:** performance percentile, trend vs. peers, top 10 rankings. --- ## From Retention Tactic to Growth Strategy For SaaS founders, retention isn’t a department — it’s a product decision. Embedded dashboards connect your users’ data to their outcomes, creating a virtuous cycle of insight, action, and loyalty. By helping customers measure success _inside your app_, you stop being a tool they occasionally use — you become a partner they depend on. --- ## Powering Embedded Dashboards with Draxlr Building analytics from scratch can slow your product roadmap. Draxlr lets you **embed interactive dashboards directly into your SaaS product** — with zero heavy lifting. You can: - Connect databases like PostgreSQL, MySQL, SQL Server, Databricks, or Supabase. - Build dashboards visually, using AI or SQL. - Embed them securely into your app using React or Vue SDKs. - White-label and customize dashboards to match your product design. - Offer self-service analytics without building a BI team. Startups use Draxlr to give users real-time insights and create data-driven experiences that keep customers loyal. **See how Draxlr can help your SaaS retain more customers:** [draxlr.com](https://draxlr.com)

Top Domo Alternatives

hello@draxlr.com (Draxlr) — Sun, 09 Nov 2025 00:00:00 GMT

Domo alternatives are becoming increasingly popular among teams that want flexible, transparent, and cost-effective business intelligence solutions. Domo is a well-established BI platform known for its strong visualization capabilities and cloud-based data management. However, as teams evolve, their analytics needs often diversify, some want simpler visual builders, AI, self-hosting options, or closer integration with their existing data stack, and this guide summarizes the leading alternatives and when to pick each. --- ## What to Look For in a Domo Alternative When evaluating Domo alternatives, consider the following criteria rooted in real-user feedback about BI tools like Domo so you can choose a platform that avoids common pain points: - **Advanced analytics:** Choose a platform that supports AI-assisted insights, natural language querying (NLQ), and advanced graphs and charts. - **Pricing transparency:** Look for predictable pricing that scales cleanly with data volume or users, avoiding opaque cost tiers. - **Performance at scale:** Ensure the tool can handle large datasets and frequent refreshes without lag or data sync issues. - **Customization flexibility:** Opt for dashboards that allow full control over layout, filters, and visuals without technical dependency. - **Data governance:** A good BI tool should offer clear folder structures, version control, and role-based permissions to manage content efficiently. - **Integration coverage:** Confirm that it connects seamlessly to your key data sources—SQL, cloud warehouses, and third-party APIs. - **Support ecosystem:** Prioritize responsive support. --- ## Quick Comparison

Alternative	Strength	Pricing transparency
Draxlr	Seamless set-up, AI-powered	Clear, affordable plans
Power BI	Native Microsoft integration	Flexible, mid range
Tableau	Deep visual customization	Premium, clear tiers
Looker	Unified metrics, SQL-driven	Enterprise, upfront
Qlik Sense	Associative exploration	Subscription-based, scalable by usage
ThoughtSpot	AI-powered insights, NLQ	Subscription-based, consultation pricing

--- ## 1. Draxlr — SQL-Native BI with AI Querying Draxlr is a modern business intelligence tool that connects to your SQL databases, enabling you to create a clean, intuitive interface for building dashboards, a powerful SQL editor, **AI-assisted querying**, and a visual query builder. Dashboards can be exported as PDFs, automated for email or Slack alerts, or **embedded** directly into your product. With native SDKs for React and Vue, Draxlr makes it easy to add analytics anywhere, complete with **white-labeling** and permission controls. Draxlr also has a **self-hosted** option for teams that value control over their data. **Key Highlights** - Build dashboards using AI chat, visual builder, or direct SQL queries. - Self-hosted and cloud versions with flexible sharing and access control. - Automated PDF exports and email summaries. - Embedded analytics SDKs for React and Vue. - Transparent pricing designed for teams of any size. - User permissions, team sharing, and access control. **Best for:** Businesses seeking a reliable Domo alternative with strong value-for-money and self-service analytics. --- ## 2. Power BI — Deep Integration Across Microsoft Ecosystem Power BI brings robust analytics to teams already using Microsoft tools like Excel, Azure, and Teams. Its drag-and-drop dashboarding and automated refresh options make it a natural fit for data-driven teams. **Key Highlights** - Native integration with Microsoft services. - Strong visualization features and rich community resources. - Secure governance and access controls. **Best for:** Organizations invested in Microsoft infrastructure seeking a Domo alternative. --- ## 3. Tableau — Best for Visual Analytics & Interactive Dashboards Tableau remains a favorite for intuitive and highly interactive data visualizations, enabling users to create compelling dashboards without extensive coding. It suits organizations prioritizing visual storytelling for varied data sources. **Key Highlights** - Powerful drag-and-drop visual builder. - Wide range of chart types and data modeling capabilities. - Large user community and extensive learning resources. **Best for:** Data-driven teams focused on visual analytics and compelling storytelling --- ## 4. Looker — Governed Metrics for Data-Warehouse Teams Looker (part of Google Cloud) offers a data-modeling layer using LookML, ensuring consistency and trust across metrics. It’s a strong choice for teams that prioritize governance and warehouse-first analytics. **Key Highlights** - Consistent metric definitions across the organization. - Tight integration with BigQuery and Snowflake. - Advanced embedding and API support. **Best for:** Teams with established data infrastructure looking for structured, model-driven analytics. --- ## 5. Qlik Sense — Associative Data Exploration Qlik Sense lets users explore data relationships dynamically using its associative engine, uncovering patterns that linear dashboards might miss. **Key Highlights** - Interactive, in-memory associative analysis. - Responsive, mobile-friendly dashboards. - Scalable architecture for large data volumes. **Best for:** Teams that want to empower users with flexible data discovery and exploration. --- ## 6. ThoughtSpot — Search and AI-Driven Insights ThoughtSpot uses AI and natural language search to democratize data access, allowing users to ask questions and get instant insights. **Key Highlights** - Natural language query interface - AI-powered insight generation - Embedded analytics capabilities Best for: Organizations wanting fast, intuitive AI-driven insights accessible to all users, regardless of technical skill. --- ## Conclusion Choosing the right business intelligence tool is crucial for organizations wanting to empower their teams with data-driven decision-making. While Domo offers a comprehensive and user-friendly BI platform, several alternatives provide strong, specialized capabilities that may better align with your company’s size, technical expertise, and budget. Evaluating these alternatives based on setup speed, embedded analytics capabilities, pricing transparency, and customization options will help you find the ideal Domo alternative that supports your growth and analytics maturity effectively. ## FAQs ### 1. What are the top Domo alternatives? The top Domo alternatives include Draxlr, Power BI, Tableau, Looker, Qlik Sense, and ThoughtSpot, each with unique strengths in integration, analytics, and deployment flexibility. ### 2. Which Domo alternative offers the best value? Among the top Domo alternatives, Draxlr stands out for offering the best value, especially for founders and growing teams seeking a cost-effective, user-friendly BI tool without compromising on powerful features. ### 3. How easy is it to switch from Domo to another BI tool? Many modern BI tools provide easy data integrations and migration support, but complexity depends on your data environment and custom dashboards; planning and vendor support are key. ### 4. What should teams consider when choosing a BI tool over Domo? Teams should evaluate pricing transparency, ease of use, setup speed, integration flexibility, embedded analytics capabilities, and how well the tool supports data democratization across teams.

How to Create and Embed a Dashboard for Your Supabase Database Using Draxlr

hello@draxlr.com (Draxlr) — Fri, 07 Nov 2025 00:00:00 GMT

If you’re building your product on **Supabase**, you already have the perfect backend - authentication, APIs, and a fast PostgreSQL database. But what happens when your customers start asking for **insights**? Usage stats, revenue charts, or activity reports, they all need a dashboard. Building those dashboards from scratch can be time-consuming and technical. That’s where **[Draxlr](https://draxlr.com)** comes in a simple way to turn your Supabase data into beautiful, shareable dashboards that you can embed right inside your app. Let’s see how. --- ## 1. Why Embed Analytics into Your Supabase App Your users love data. Whether it’s tracking their team’s activity, revenue trends, or app performance - giving customers **insights inside your product** makes it more valuable. Adding dashboards helps you: - Increase customer engagement and trust - Reduce custom reporting requests - Offer analytics as a premium feature - Strengthen your product’s stickiness But building analytics features often means handling SQL, APIs, chart libraries, and permissions - all of which take time away from your core product. That’s why many Supabase users turn to **Draxlr**. It connects directly to your database and lets you build, manage, and embed dashboards - without needing a full BI team. --- ## 2. Connect Supabase to Draxlr Connecting your Supabase project to Draxlr takes just a minute. 1. Log in to [**app.draxlr.com**](https://app.draxlr.com). 2. You can either select **Postgres** (to add details manually) or **Supabase** (to connect you Supabase account and we fetch your project details automatically). - To get your connection details in your Supabase project, go to **Connect** and select Direct in connection method. 3. Confirm your database credentials - Host, Port, Username, Password, and Database Name. 4. Click on Next and Draxlr will connect to your Supabase database. ![Connect your Supabase database](/images/blogs/how-to-create-and-embed-a-dashboard-for-your-supabase-database-using-draxlr/images/connect-database.png) That’s it! Draxlr will automatically detect your tables and columns so you can start exploring your data instantly. --- ## 3. Build Dashboards the Easy Way Once connected, you can create dashboards using **Draxlr’s drag-and-drop builder**. No need to write code (unless you want to). You can: - Add charts, tables, or KPIs - Choose your data visually - Apply filters to slice by customer, date, or category ![Draxlr Query Builder](/images/blogs/how-to-create-and-embed-a-dashboard-for-your-supabase-database-using-draxlr/images/query-builder.png) If you love SQL, you can still write your own queries - and even use **AI Query Builder** to generate them for you. Just describe what you want (like “show revenue by month”) and Draxlr will create the query automatically. You can mix and match different widgets to tell the story you want your customers to see. --- ## 4. Embed Your Dashboard in Your App Now for the fun part - embedding your dashboard inside your Supabase app. Each dashboard in Draxlr can be **shared securely** using an embed link. Here’s how: 1. Go to your dashboard and click **Embed**. ![Draxlr Embed Option](/images/blogs/how-to-create-and-embed-a-dashboard-for-your-supabase-database-using-draxlr/images/embed-option.png) 2. It'll open the embed preview window where you can customize the appearance and access settings. ![Draxlr Embed Settings](/images/blogs/how-to-create-and-embed-a-dashboard-for-your-supabase-database-using-draxlr/images/embed-preview.png) 3. Copy the **iframe code** Draxlr gives you and paste it into your web app. **Simple iframe embed** - the easiest way to get started. Copy your dashboard’s embed link from Draxlr and drop it into your app. Perfect for internal or shared views. ```html

``` And your **dashboard is live** inside your app! ![Draxlr Embedded Dashboard](/images/blogs/how-to-create-and-embed-a-dashboard-for-your-supabase-database-using-draxlr/images/embeded.png) **Frontend libraries (React & Vue.js)** - If you’re using a modern frontend framework, Draxlr also provides **React and Vue.js libraries** to make embedding even smoother. **Backend embed API** - for secure, customer-specific dashboards, you can generate a token from your backend using Draxlr’s API. This token enables authenticated embedding and can include filters (like `customer_id`) so each user only sees their own data. You can know more about embedding dashboards in Draxlr from our [documentation](https://docs.draxlr.com/docs/dashboard/embed-dashboard). --- ## 5. Conclusion If you’re using Supabase for your backend, you already have all the data your customers care about. With Draxlr, you can finally show it to them - beautifully, securely, and with zero extra dev effort. Draxlr helps you: - Build dashboards visually - Use AI to generate insights - Embed analytics directly in your SaaS Connect your Supabase Database

How to generate SQL dashboard PDF reports

hello@draxlr.com (Draxlr) — Wed, 05 Nov 2025 00:00:00 GMT

Reporting is a critical part of every data-driven business. Whether you’re tracking KPIs, presenting performance insights to management, or sharing updates with clients, dashboards make data easy to understand. But when you need to present these insights offline or share them outside your analytics tool, exporting your dashboard as a **PDF report** becomes essential. With **Draxlr**, you can go from dashboard to PDF in just a few clicks—without writing a single line of code. It connects directly to your SQL database, allowing you to build dashboards visually, using AI, or with SQL, and instantly export them as clean, print-ready PDFs. In this blog, we’ll walk through how to generate SQL dashboard PDF reports using Draxlr, why they’re useful, and how you can automate them for recurring reporting. --- ## Why generate SQL dashboard PDF reports Exporting dashboards as PDFs might sound simple, but it’s a powerful feature that improves how teams share, present, and store insights. Here’s why it matters: - **Easy sharing:** PDF reports are universally accessible and can be shared via Email, Slack, or internal portals. - **Offline access:** Perfect for teams who need to review data during meetings or in low-connectivity environments. - **Print-ready format:** PDF exports maintain dashboard formatting, color themes, and chart alignment for clean reports. - **Historical analysis:** Saving PDFs of dashboards for future comparison, audit trails, or compliance records. - **Controlled dissemination:** Ensuring consistent data presentation without the risk of dashboard misinterpretation or errors during viewing. Whether you’re a founder preparing investor updates or an analyst sharing monthly KPIs, a downloadable PDF report makes communication clear and consistent. --- ## How to generate SQL dashboard PDF reports using Draxlr

### Step 1: Connect your data Start by signing up to [Draxlr](https://app.draxlr.com/register/). Once signed up, connect your SQL database to Draxlr or use demo db to explore. Draxlr supports **PostgreSQL, MySQL, SQL Server, MariaDB, BigQuery, Databricks, Snowflake, Supabase**, and more. Once connected, your schema syncs instantly. ### Step 2: Create a Dashboard Open demo dashboard or create a new one using one of Draxlr’s three modes: 1. **AI Chat** – Ask questions in plain English, and Draxlr generates SQL and visualizations automatically. 2. **Visual Query Builder** – Build queries without SQL; drag and drop tables, filters, and metrics. 3. **SQL Editor** – Write your own SQL for maximum flexibility and control. Once your dashboard is ready, it’s only a few clicks away from becoming a PDF. ### Step 3: Refine your layout Add filters, resize visualizations, and rearrange to make your dashboard report-ready. Draxlr automatically preserves these configurations when exporting. ### Step 4: Export as PDF Click **Export Dashboard as PDF** to generate a downloadable, print-ready report. The export includes your charts, filters, and layout exactly as displayed on the dashboard. --- ## Send summary emails with SQL dashboard PDF reports Along with manual PDF exports, Draxlr also lets you automate summary emails that include a PDF version of your dashboard. Inside Dashboard Settings, you can: 1. Enable the toggle for "Receive Summary Email". 2. Add one or more recipient email addresses. 3. Choose the notification interval (daily, weekly, or monthly). 4. Set the time when the report should be sent. 5. Define the cache interval to control how frequently data is refreshed. When the email is delivered, it includes a quick summary of key metrics along with the attached PDF dashboard report.
This feature helps teams who want consistent updates without manually logging in or exporting data. It’s perfect for: 1. Daily sales or performance summaries. 2. Weekly marketing campaign reports. 3. Monthly executive dashboards. Set it once—and Draxlr handles the rest. --- ## Key Benefits of Using Draxlr for PDF Reports When compared to other BI tools, Draxlr stands out for its simplicity and flexibility in PDF generation: - **Real-time data access:** Connects directly to live databases like PostgreSQL, MySQL, SQL Server, BigQuery, Databricks, Snowflake, and Supabase. - **No-code workflow:** Anyone can build dashboards and export reports. - **Secure architecture:** Data isn’t stored. Only queried and cached temporarily for faster performance. - **Custom branding:** Add your logo, colors, and layout preferences to maintain visual consistency. Whether you’re part of a startup or an enterprise team, Draxlr’s dashboard-to-PDF workflow saves time, reduces manual effort, and ensures consistent reporting quality. --- ## Conclusion Generating PDF reports from SQL dashboards doesn’t have to involve complex scripts or external plugins. With **Draxlr**, you can connect your database, visualize data, and export or automate PDF reports—all in minutes. From product analytics to financial summaries, every report stays connected to real-time SQL data while maintaining your brand identity. 👉 **[Try 7-day free trial of Draxlr – No Credit Card Required](https://app.draxlr.com/register/)** and turn your live dashboards into shareable, presentation-ready PDF reports today. --- ## FAQs ### 1. Can I schedule automatic PDF exports? Yes. Draxlr lets you schedule dashboards to be automatically sent as PDF reports to your team or clients at defined intervals. ### 2. Will the PDF reflect real-time data? Yes. Each export queries your live database, ensuring your PDF reflects up-to-date information at the time of generation. ### 3. Do I need technical knowledge to export reports? No. Draxlr is built for everyone. Its visual interface and AI chat make it easy to build and export dashboards without SQL expertise. ### 4. Are there limits on sharing or downloading SQL dashboard PDF reports from Draxlr? No, you can share and download PDF reports as often as needed with Draxlr.

Embed SQL Dashboards in Dark and Light Mode

hello@draxlr.com (Draxlr) — Tue, 04 Nov 2025 00:00:00 GMT

### Overview When you embed a dashboard inside your web app, it should feel native to your product — including its theme. Whether your app uses light mode, dark mode, or a custom color scheme, Draxlr makes it simple to match your embedded dashboard to your app’s look and feel. With Draxlr, you can switch between dark and light mode dynamically using just a few lines of configuration. No need to rebuild or re-embed. --- ## Why Theme-Aware Dashboards Matter Modern web apps often support user-controlled themes. When a user switches to dark mode, every component — from navigation bars to charts — should adapt automatically. Embedding dashboards that respect this behavior keeps the UI consistent and improves accessibility, especially for users who prefer darker screens during nighttime work sessions. --- ## How Draxlr Makes It Easy Draxlr’s embedding configuration accepts multiple styling options. You can define: - **Background color** – overall dashboard background - **Card background color** – surface color for individual widgets - **Header and Download options visibility** - **Other display preferences** such as filters, drill-downs, and more These options can be set in your embed code and changed dynamically based on your app’s active theme or requirements. --- ## Example: Embedding a Dashboard in Light Mode ```html

``` This setup renders your dashboard in **light mode**, ideal for white or pastel UIs. --- ## Example: Switching to Dark Mode Dynamically You can easily change the configuration on theme switch: ```js function toggleTheme(isDark) { const config = dashboard67a2fd96f762c56b91c3724f.configuration if (isDark) { config.backgroundColor = "#111111" config.cardBackgroundColor = "#2b2b2b" } else { config.backgroundColor = "#ffffff" config.cardBackgroundColor = "#fefefe" } draxlr.render() // re-render with new config } ``` This lets your embedded dashboard automatically follow the user’s system preference or your app’s internal theme toggle. --- ## Draxlr SDKs for React and Vue.js Draxlr provides ready-to-use SDKs for React and Vue.js, making embedding dashboards into your modern frontend apps even easier. The SDKs handle initialization, events, and configuration updates seamlessly — all you need to do is import the component and set your dashboard credentials. ### React Example ```js // Install // npm i react-draxlr-embed --save // Documentation: https://www.npmjs.com/package/react-draxlr-embed import DraxlrEmbed from 'react-draxlr-embed' function App() { return ( console.log('Loading...')} onAfterLoad={() => console.log('Load complete')} onError={(error) => console.error('Error:', error)} /> ) } ``` ## Real Example Below you can see the same Draxlr dashboard embedded in two modes: ### Light Mode ![Light Mode Dashboard](/images/blogs/embed-sql-dashboard-in-dark-light-mode/light-mode.png) ### Dark Mode ![Dark Mode Dashboard](/images/blogs/embed-sql-dashboard-in-dark-light-mode/dark-mode.png) Both versions use the exact same embed, only the configuration values for background colors differ. --- ## Benefits of Theme-Adaptive Embedding - Seamless integration into your app’s design - Better accessibility and reduced eye strain - No need to maintain multiple embeds - Instant theme switch with a single JavaScript function --- ## Conclusion Whether your app runs in bright daylight mode or sleek dark theme, Draxlr lets you embed dashboards that blend perfectly with your UI. You control the look and feel directly through the embed configuration — making it effortless to keep your analytics visually consistent with your product’s design. --- ## FAQs ### 1. Can Draxlr dashboards automatically switch between light and dark mode? Yes. You can dynamically update the `backgroundColor` and `cardBackgroundColor` properties in the configuration object and call `draxlr.render()` to re-apply the theme instantly. ### 2. Do I need to re-embed the dashboard for theme changes? No. Draxlr allows real-time theme switching through configuration updates — no additional embed or reload is required. ### 3. Can I use custom brand colors instead of standard light/dark themes? Absolutely. Draxlr lets you define any color values (hex, rgba, etc.), so you can match your brand palette or create entirely custom themes. ### 4. Is dark mode supported in embedded dashboards on all browsers? Yes. Draxlr’s embedded dashboards are fully responsive and compatible with modern browsers in both light and dark modes.

Sigma Computing Alternatives: Top Tools to Consider

hello@draxlr.com (Draxlr) — Fri, 31 Oct 2025 00:00:00 GMT

Sigma Computing is popular for its spreadsheet-style analytics and direct connections to cloud warehouses like BigQuery, Snowflake, and Redshift. If you're exploring options that emphasize AI, embedded analytics, self-hosting, or tighter governance, this guide summarizes the leading alternatives and when to pick each. ## What to Look For in a Sigma Alternative - **Ease of use:** intuitive UI for business users - **Advanced analytics:** AI/NLQ, automated insights - **Customization:** embed, white-label, SDKs - **Performance & scale:** smooth on large/complex data - **Integrations:** fits your data stack and workflows - **Pricing fit:** aligns with your team and growth stage --- ## Quick Comparison

Alternative	Best For
Draxlr	Secure self-hosting & SQL-centric dashboards
ThoughtSpot	AI-powered search & self-service
Sisense	Embedded analytics & low-code customization
Tableau	Advanced visualization & data storytelling
Microsoft Power BI	Microsoft ecosystem & cost-effective BI
Qlik Sense	Associative exploration & real-time insights
Looker	Centralized governance & Google Cloud alignment

--- ## 1. Draxlr — Best for Secure Self-Hosted SQL Dashboards **Summary:** Draxlr blends cloud convenience with a **self-hosted** option for teams that want full control, while offering a smooth UI for visual exploration and a powerful SQL editor with **AI-assisted querying**. Native **embedded analytics** (React/Vue) and white-labeling make it easy to integrate analytics into your product. **Highlights** - Self-host or managed cloud, with strong data control - Visual builder + SQL editor with **AI query assistance** - Interactive dashboards with filters, drill-downs, pivot tables - Native **embedding** via components; white-label & multi-tenant options - Transparent, affordable pricing (Starting at $75/month) --- ## 2. ThoughtSpot — Best for AI-Powered Search Analytics **Summary:** ThoughtSpot is **search-first** BI: users ask questions in natural language and get instant visuals. It’s built to make analytics self-service for non-technical teams, with features like SpotIQ for automated insights. **Highlights** - Natural language queries (NLQ) → immediate visual answers - AI-driven insights (e.g., SpotIQ) - Works well with large cloud data sources - Great for teams prioritizing **self-service** analytics --- ## 3. Sisense — Best for Embedded Analytics & Low-Code Customization **Summary:** Sisense offers a drag-and-drop builder, **NLQ**, and flexible data handling—live connections or its high-performance **ElastiCube** engine. It’s a favorite for **embedding** analytics into apps via robust APIs and SDKs. **Highlights** - Low-code dashboarding with NLQ - Live-query or in-chip engine (ElastiCube) - Powerful **APIs/SDKs** and plugin ecosystem - Designed for **embedded** dashboards and widgets --- ## 4. Tableau — Best for Advanced Visualization & Storytelling **Summary:** Tableau is renowned for its rich visuals, interactivity, and broad data connectivity. Features like **Ask Data** and **Explain Data** add AI assistance to a best-in-class visualization stack. **Highlights** - Deep charting library (maps, geospatial, advanced visuals) - Interactive dashboards and strong data blending - AI helpers (Ask Data, Explain Data) - Suited for analysts and data storytelling --- ## 5. Microsoft Power BI — Best for Microsoft Ecosystem & Value **Summary:** Power BI pairs an approachable drag-and-drop interface with tight integration across **Excel, Azure SQL, Dynamics**, and more—delivering strong analytics at a very accessible price point. **Highlights** - Real-time dashboards and natural-language **Q&A** - Strong modeling with Power Query and DAX - Seamless with Microsoft stack (Office 365, Azure) - Great **price-to-capability** ratio for most teams --- ## 6. Qlik Sense — Best for Associative Data Exploration **Summary:** Qlik’s associative engine enables free-form exploration across linked datasets. Selecting a value updates every chart to reveal related and unrelated data, which is powerful for discovery. **Highlights** - Associative, in-memory analytics model - Flexible, interactive selections across views - Conversational analytics and alerting - Open APIs for embedding and automation --- ## 7. Looker — Best for Centralized Modeling & Governance **Summary:** Looker (Google Cloud) emphasizes a governed **modeling layer** (LookML) to define consistent metrics organization-wide, with strong live-query, API, and embedded capabilities—great for enterprises prioritizing a single source of truth. **Highlights** - Central metric definitions in **LookML** - Live queries on BigQuery/Snowflake/Redshift - Embedded analytics and extensive APIs - Aligns naturally with **Google Cloud** (multi-cloud supported) --- ## Conclusion Modern BI tools go beyond spreadsheets and static dashboards. If you need secure, SQL-based analytics with AI assistance, Draxlr is a top Sigma alternative — offering self-hosting, cloud deployment, and embedded dashboards with full branding control. For teams focused on AI search, ThoughtSpot leads; for visual storytelling, Tableau excels; and Power BI offers unmatched value in the Microsoft ecosystem. Tools like Looker, Qlik Sense, and Sisense round out strong options for governance and embedded use cases. Choose a platform that aligns with your data stack, scale, and control needs — and unlock faster, smarter insights. ## FAQs ### 1. Who does Sigma Computing compete with? Sigma Computing competes with **Draxlr**, **ThoughtSpot**, **Sisense**, **Tableau**, **Power BI**, **Qlik Sense**, and **Looker** — all offering modern BI and analytics. ### 2. Is Draxlr a good alternative to Sigma Computing? Yes. **Draxlr** provides both **cloud and self-hosted** deployment, AI-powered SQL query generation, and **embedded dashboards**, making it ideal for secure analytics. ### 3. Which Sigma alternative is best for visualization? **Tableau** excels in data visualization and storytelling, while **Draxlr** offers accessible, interactive visuals at a lower cost. ### 4. Which Sigma alternative supports embedded analytics? **Draxlr** and **Sisense** are best for embedded analytics with white-label options and front-end integration APIs.

Top Superset Alternatives for Modern BI and Analytics

hello@draxlr.com (Draxlr) — Fri, 03 Oct 2025 00:00:00 GMT

Apache Superset is a modern, open-source business intelligence (BI) and data visualization tool. It allows users to connect to a wide range of SQL databases, create dashboards, and build interactive charts. Superset is known for its flexibility, scalability, and ability to handle large datasets. It’s widely used by data teams who prefer open-source solutions that can be customized and self-hosted. While Superset is powerful, it often requires technical expertise to deploy, maintain, and extend. Non-technical business users may find it less intuitive compared to some commercial BI tools. ### Drawbacks of Superset Even though Superset is robust, teams often explore alternatives due to: - **Ease of Use**: Superset can be complex for non-technical users. Alternatives may provide more intuitive drag-and-drop interfaces or AI-powered query builders. - **Maintenance Overhead**: Being open-source, Superset requires engineering time for setup, upgrades, scaling, and support. - **Embedding and White-labeling**: Many SaaS companies need smooth embedding of dashboards for their customers, which may require custom engineering in Superset. - **Alerts and Notifications**: Superset has limited built-in alerting capabilities, while some alternatives offer more advanced monitoring and triggers. - **Enterprise Support and Governance**: Companies that need advanced access controls, compliance, or guaranteed support may prefer commercial BI tools. --- ## Popular Superset Alternatives ### 1. Draxlr **Draxlr** is a modern BI and analytics platform designed to make data exploration simple for both technical and non-technical users. Key features include: - **No-code query builder and AI SQL assistant** to generate insights without needing to write SQL. - **Real-time dashboards** with advanced visualization options. - **Built-in alerts** and notifications so teams know when metrics cross thresholds. - **Flexible embedding and white-labeling** for SaaS products. - **Cloud and self-host options** for easy deployment and security flexibility. Draxlr is a great option for startups, SaaS companies, and growing teams who want actionable insights without heavy engineering effort. --- ### 2. Zoho Analytics **Zoho Analytics** is a cloud-based BI tool focused on simplicity and affordability: - Intuitive drag-and-drop interface. - Wide variety of integrations with business apps and databases. - AI-powered assistant (Zia) for generating insights. - Competitive pricing for small and mid-sized businesses. Zoho Analytics is ideal for teams looking for an affordable and user-friendly solution without heavy technical setup. --- ### 3. PowerBI **Power BI** from Microsoft is one of the most widely used BI platforms: - Seamless integration with the Microsoft ecosystem (Excel, Azure, Teams). - Extensive visualization options. - Affordable pricing tiers for smaller teams. - Rich enterprise governance and compliance features. It’s an ideal choice for businesses already using Microsoft products. --- ### 4. Tableau **Tableau** is known for its polished, interactive visualizations and storytelling capabilities: - Drag-and-drop interface that’s accessible to business users. - Wide adoption and strong community. - Advanced visualization and dashboarding features. Tableau is often preferred by organizations looking for beautiful, presentation-ready dashboards and data storytelling. --- ### 5. Domo **Domo** is a cloud-native BI platform designed for real-time, collaborative insights: - Strong focus on connecting to multiple cloud and business apps. - Easy-to-use dashboards with mobile-first design. - Built-in data sharing and collaboration features. Domo works well for organizations that want BI tightly integrated with everyday workflows. ### Other Popular Alternatives **Qlik Sense**: Known for its associative data engine that helps uncover hidden insights. **Sisense**: Strong embedding and API-driven BI tool for SaaS products. **Redash**: Lightweight SQL-first dashboarding tool, popular among developers. **Grafana**: Ideal for time-series and operational metrics, especially in DevOps environments. --- ## Conclusion Superset is a capable open-source BI tool, but depending on your team’s needs, alternatives like **Draxlr, Zoho Analytics, Power BI, Looker, Tableau, and Domo** may be a better fit. Whether you prioritize ease of use, embedding, enterprise governance, or advanced visualizations, there’s a solution tailored to your business. --- ## FAQs **1. What is the best alternative to Superset for startups?** Draxlr is an excellent option for startups because it offers easy setup, no-code exploration, and embedding features without requiring a dedicated data engineering team. **2. What makes Draxlr stand out among alternatives?** Draxlr offers a clean balance of affordability, advanced features like AI SQL, and ease of use. It’s particularly strong for startups and SaaS companies looking for embedded analytics. **3. What makes Draxlr different from Superset?** Draxlr is built for ease of use, with a no-code query builder, AI SQL assistant, and built-in alerts, whereas Superset often requires more technical setup and customization. Draxlr focuses on reducing engineering overhead while making analytics accessible to all team members. **4. Which Superset alternative works best with Microsoft products?** Power BI integrates seamlessly with the Microsoft ecosystem, making it the natural choice for companies already using Office 365, Azure, or Teams. **5. Is there a strong open-source alternative to Superset?** Redash and Metabase (though not listed above) are strong open-source tools. Grafana also works well for time-series data and DevOps monitoring.

ThoughtSpot Alternatives: Top 8 BI Tools to Consider in 2025

hello@draxlr.com (Draxlr) — Tue, 30 Sep 2025 00:00:00 GMT

Before diving into alternatives, it's worth summarizing the appeal and limitations of ThoughtSpot, to see where derivative tools try to improve or diverge. ### What ThoughtSpot brings: - A search-and-AI driven interface (you can ask natural-language questions) and get visual responses. - Strong embedded analytics support (ThoughtSpot Everywhere / Embedded) to integrate dashboards into applications. - Scalability for large datasets in cloud or hybrid environments. - Continuous investment in AI/ML enhancements (e.g. Spotter). ### Possible limitations / areas of concern: - Cost and licensing complexity (enterprises often must negotiate). - Deployment flexibility: some critics argue ThoughtSpot tends toward cloud-centric models. - Custom embedding or UI tailoring may hit constraints versus fully developer-centric platforms. - Some users report limitation in dashboard customization or price at scale. --- ## Draxlr - Focus on embedding analytics into SaaS or customer-facing applications (white-label, flexible UI). - Developer-friendly APIs and SDKs to integrate dashboards, charts, and data workflows in custom apps. - Flexible deployment: on-premises or in private cloud. - More cost-aggressive for smaller teams compared to enterprise incumbents. ## BoldBI - Strong embedding APIs / SDKs, multi-tenant support, transparent pricing. - Flexible deployment options including on-premises. - May lack some AI/ML depth compared to ThoughtSpot. ## Sigma Computing - Spreadsheet-like interface for exploring live cloud data. - High user ratings; easy adoption for business users. - Less AI-first than ThoughtSpot; some dashboarding limitations. ## Domo - All-in-one suite: connectors, dashboards, apps, sharing. - Strong ETL and real-time ingestion. - Can feel heavy; costs scale quickly. ## Tableau - Leading visualizations and dashboard flexibility. - Large ecosystem and strong storytelling features. - Costs and licensing complexity; less search-first. ## Power BI - Tight Microsoft integration and low entry barrier. - Cost-effective for many teams. - Embedding constraints and performance challenges at scale. ## Sisense - Embedding and white-label focus, good customization. - Balanced low-code and developer control. - Pricing complexity, slower ROI realization. ## Holistics - Central semantic modeling, analytics-as-code, Git versioning. - Strong governance focus. - Smaller ecosystem, fewer AI features. --- ## Comparison Canvas: Key Dimensions to Evaluate a BI tool

ThoughtSpot Alternatives – Comparison Canvas
Dimension	What to Evaluate	Why It Matters
Embedding & Developer Flexibility	APIs, SDKs, white labeling, multi-tenant support, theming	If analytics go into your apps, developer control is critical
Ease of Use / Search / Natural Language	Can business users ask questions without SQL? Quality of auto-insights and AI	Reduces dependence on analysts and IT
Semantic Modeling / Governance	Canonical metrics, data definitions, access control	Enables consistency and control at scale
Performance & Scalability	Handling large datasets, concurrency, query optimization	Real-world workloads stress test platforms
Connector & Integration Ecosystem	Data sources, ETL support, cloud platforms, APIs	Ensures compatibility with your stack
Deployment Flexibility & Data Residency	On-prem, private cloud, hybrid, regional hosting	Critical for compliance and regulatory needs
Pricing & Licensing Transparency	Predictable cost models, scaling, user tiers	Avoids surprises in total cost of ownership
Community / Support	Marketplace, community extensions, vendor support SLAs	Speeds adoption and troubleshooting
Analytics Depth & Extensibility	ML features, custom visuals, scripting and extensibility	Future-proofs your analytics roadmap

--- ## Recommendation Tips & Strategy - **Start with your use case.** Prioritize embedding, self-service, or enterprise dashboards. - **Pilot with your data.** Test concurrency and workflows. - **Evaluate TCO over time.** Don't just look at entry pricing. - **Balance power vs usability.** Sigma and Draxlr get teams started fast. - **Check vendor roadmap.** Ensure investment in AI and real-time features. - **Plan for migration.** Minimize lock-in by modular dashboard and data designs. --- ## FAQs ### 1. What is the equivalent of ThoughtSpot? The closest equivalent to ThoughtSpot is **Draxlr**, which provides modern business intelligence with strong embedding, flexible deployment, and developer-friendly integration options. ### 2. What are the disadvantages of ThoughtSpot? Some common drawbacks of ThoughtSpot include its complex and sometimes high cost, limited deployment flexibility (often cloud-centric), and constraints in UI customization compared to developer-focused tools. Users have also noted challenges with dashboard flexibility and scalability at larger usage levels. ### 3. How much does ThoughtSpot cost? ThoughtSpot does not publicly disclose fixed pricing. Costs vary depending on deployment, user count, and enterprise needs. Pricing is typically custom-quoted, so organizations must contact ThoughtSpot sales directly for an accurate estimate.

6 best Looker alternatives

hello@draxlr.com (Draxlr) — Tue, 16 Sep 2025 00:00:00 GMT

As organizations rethink their BI strategy due to cost, complexity, technical dependencies, or visualization needs, it’s time to explore robust alternatives to Looker. Below is a curated selection, beginning with Draxlr and followed by notable contenders. This guide also highlights what makes each option unique so that teams can match their priorities with the right tool more easily. --- ## 1. Draxlr: Self-Service, AI Insights, No-code BI **Draxlr** stands out as a self-hosted or cloud-ready BI tool designed for intuitive SQL-driven analytics: - Fully self-hosted via Docker, giving teams complete control over data, infrastructure, and compliance. - Supports many databases such as PostgreSQL, MySQL, Redshift, Snowflake, BigQuery, Databricks, Supabase, and more. - Offers visual and AI-assisted SQL querying with a drag and drop builder, real-time results, versioned queries. - Dynamic dashboards with pivot tables, charts, drill down or through, filters, embedded analytics (React and Vue friendly). - Alerts via Slack or email, scheduled reports, query audit logs, granular permission controls. - Competitive pricing starting around $75/month for 20 users or $500/month self-hosted. ![Draxlr - Alternative to Looker](/images/blogs/_tools/draxlr.png) --- ## 2. Zoho Analytics: Intuitive, AI Powered, and Cost Effective - Drag and drop UI with strong visualization options and AI assistant “Zia,” enabling natural language queries, anomaly detection, predictive insights. - Over 500 native connectors and automated data blending. - Available on cloud, on-premises, or embedded deployment. - Pricing starts at about $8 per user per month with a free plan. --- ## 3. Holistics: Analytics as Code with Semantic Modeling - Architecturally similar to Looker with semantic modeling and modeling as code. - Ideal for teams needing centralized definitions, reproducibility, consistency across dashboards. --- ## 4. ThoughtSpot: AI Augmented, Search First Analytics - Search-based interface with generative AI (Spotter) that lets users query data using plain language. - Real-time dashboards, proprietary modeling language (TML), embedded analytics. - Excellent for natural language access and AI insights. --- ## 5. Tableau: Visualization Leader with Deep Customization - Exceptional range of chart types, storytelling, interactive dashboards. - Steeper learning curve and pricing, but unmatched for advanced visualizations. --- ## 6. Qlik Sense: Associative Engine for Data Exploration - Uses associative data modeling for intuitive exploration across related tables. - Strong for discovering hidden relationships in complex datasets. --- ## Comparison Snapshot

Use Case or Priority	Recommended Alternative	Why It Fits
AI-assisted query with self-host or cloud	Draxlr	SQL plus AI support, flexible deployment, strong support
Natural language insights with AI	ThoughtSpot	Search-first interface, AI dashboards
Drag and drop ease at low cost	Zoho Analytics	Intuitive UI, Zia assistant, affordable
Semantic modeling and code-based governance	Holistics	LookML-like modeling without Looker
Visualization depth and storytelling	Tableau	Diverse charts, interactivity, polished design
Exploring complex data associations	Qlik Sense	Associative engine, embedded options
Open-source and flexible frameworks	Metabase, Omni, others	Community-driven, customizable

--- ## Final Thoughts - **Draxlr** is the top pick when it comes to combining AI features, SQL flexibility, self-hosting options, and affordability. - **ThoughtSpot** excels for natural language querying and AI-augmented dashboards. - **Zoho Analytics** works well for teams wanting simplicity and low cost. - **Holistics**, **Tableau**, and **Qlik Sense** remain strong choices depending on your needs for modeling, visualization, or exploratory analytics. --- ## FAQs ### 1. What is better than Looker? Draxlr is a strong Looker alternative, designed to empower every team member with actionable insights. As a self-service data analysis tool, it enables your team to explore and learn from data without requiring SQL expertise. ### 2. What are the drawbacks of Looker? Looker has a steep learning curve, a complex setup process, and is comparatively more expensive than some of its alternatives. ### 3. Is Looker or Tableau better? Choose Looker if your focus is on advanced data modeling, scalability, and embedding analytics, and choose Tableau if you prioritize ease of use, interactive dashboards, and a visually rich BI experience. If you’re exploring more Looker alternatives, try Draxlr, it offers the best of both worlds. ### 4. Is there an affordable alternative to Looker? You can consider Draxlr, it is very affordable compared to Looker. It is ideal for small to mid-sized companies, and it also has great customer support.

Why every small business needs an AI assistant for data

hello@draxlr.com (Draxlr) — Fri, 29 Aug 2025 00:00:00 GMT

Running a small business often means wearing multiple hats in a single day - managing sales, keeping customers happy, running marketing campaigns, handling finances, and keeping operations smooth. Unlike large enterprises that can afford specialized data teams and expensive analytics platforms, small businesses often have to do more with less. But one thing remains the same: data is essential. And that’s where AI-powered data assistants are starting to transform how small businesses operate. --- ## 1. Data without the overwhelm Small businesses produce more data than ever before - sales transactions, customer interactions, website visits, social media metrics, and inventory records. The challenge is not about collecting data but about making sense of it. Traditional methods like spreadsheets can quickly become a maze, and writing SQL queries often requires technical expertise that many small business owners don’t have. An AI assistant changes that by allowing you to simply _ask a question in plain language_. For example: _“Show me my monthly sales by product category”_. Within seconds, you can see a clear chart or table. No manual number-crunching, no wasted hours. The result is clarity and confidence without the overwhelm. ## 2. Instant answers to business questions When a question comes up in a meeting or while you’re planning strategy, you shouldn’t have to wait days for an answer. Consultants, agencies, or even internal teams can take time to respond, and by then the opportunity may have passed. An AI data assistant works like a teammate who always has the numbers ready. You can ask questions such as: - _“What were my top-selling products last month?”_ - _“Which marketing campaign brought in the most leads?”_ - _“How do this week’s sales compare to last week’s?”_ In each case, the assistant interprets your question, runs the right query behind the scenes, and gives you results in seconds - often as an interactive chart. This speed of insight empowers small businesses to move faster and stay agile. ## 3. Smarter decision-making Good decisions rely on good data. Whether you’re deciding how much stock to order, which customer segment to target, or when to run a seasonal discount, the more context you have, the better. AI assistants help by spotting patterns, highlighting anomalies, and surfacing insights you may not have noticed otherwise. Imagine you see a sudden dip in sales - an assistant can not only alert you automatically but also help you explore the “why” behind the change. Was it a specific product line? Did a campaign underperform? Did customer behavior shift? Getting these answers quickly means you can act before small problems turn into big ones. ## 4. Affordable and scalable Hiring a dedicated data analyst or building a BI team isn’t always realistic for small businesses. AI assistants offer an affordable alternative. They let you start small, maybe by setting up a few dashboards for your most important metrics, and then scale up as your business grows. This flexibility means you don’t have to invest heavily upfront. You can begin with the essentials - like tracking sales trends or customer retention - and add more as your confidence with data grows. The technology adapts to you, rather than forcing you into a one-size-fits-all model. ## 5. Competitive advantage Large enterprises already benefit from advanced analytics, but small businesses can level the playing field by adopting AI-powered tools. Being able to identify customer behavior patterns, optimize marketing spend, and streamline operations gives you a real edge against competitors who still rely on guesswork. The best part is accessibility. With AI-driven analytics, you don’t need technical expertise. You can ask questions in plain English, get answers instantly, and make decisions that help you stay one step ahead. It’s about competing smarter, not just harder. ## 6. Free up your time Time is one of the scarcest resources for small business owners. Manually pulling reports, updating spreadsheets, and sharing insights can take hours every week - time that could be spent on customers, growth, or innovation. AI assistants cut down on this busywork by automating reporting, scheduling insights, and making it easy to share results with your team or clients. Instead of reacting slowly, you’ll have proactive insights delivered when you need them. That means less firefighting and more time for strategic growth. --- ## Final Thoughts Every small business has valuable data, but not every business uses it to its full potential. AI assistants are changing that by making insights accessible, actionable, and affordable - without requiring a dedicated data team. Tools like [Draxlr](https://www.draxlr.com/) are a good example. They allow small businesses to query databases in plain English, visualize results instantly, and build dashboards without technical barriers. But whichever solution you choose, the important step is to start using your data to guide smarter decisions. 👉 Try exploring an AI data assistant today - you may be surprised at how quickly your data can start working for you, helping you save time, reduce risks, and grow with confidence. Try Draxlr

How to Use an SQL Dashboard Tool to Monitor Key Performance Indicators(KPIs)

hello@draxlr.com (Draxlr) — Tue, 26 Aug 2025 00:00:00 GMT

An **SQL dashboard tool** helps businesses monitor their Key Performance Indicators (KPIs) in real time by connecting directly to their database. Instead of juggling spreadsheets or static reports, teams can use live dashboards to track performance, spot issues early, and make better decisions. With [Draxlr](https://www.draxlr.com/), you can build powerful dashboards quickly and monitor your most important KPIs without needing technical expertise. --- ## What is an SQL Dashboard Tool? An **SQL dashboard tool** isn’t just about charts—it’s about creating a live window into your business data. By connecting directly to your database, it replaces static reports with interactive dashboards that update as your data changes. This means finance, sales, and operations teams no longer have to wait for exports or manual updates—they can see KPIs as they happen. ![Draxlr Dashboard Example](/images/blogs/create-a-public-dashboard-url-for-your-sql-database/dashboard.png) Draxlr takes this one step further by making dashboards accessible to everyone on the team. You can: - **Build visually** with a drag-and-drop Query Builder. - **Ask in plain English** and let AI generate the SQL for you. - **Write your own SQL** when you want complete control. Whether you’re a founder checking revenue growth or an analyst diving deep into customer churn, Draxlr adapts to your workflow and keeps KPIs at your fingertips. --- ## Why KPIs Work Better with an SQL Dashboard Tool Traditional KPI tracking often breaks down because: - Spreadsheets require manual updates. - Teams rely on multiple versions of the truth. - Reports lag behind real performance. An SQL dashboard tool solves these problems by working directly on your live database. This means: - **Always current** – Dashboards refresh with the latest data. - **Single source of truth** – Everyone sees the same numbers. - **Less overhead** – No exporting, cleaning, or emailing files. Draxlr goes one step further by making this process accessible to both technical and non-technical users. Whether you’re a founder checking growth metrics or an operations lead monitoring delivery times, you can set up dashboards without needing a data engineer. Connect your Database --- ## How an SQL Dashboard Tool Fits Into Daily Business Use Using an SQL dashboard tool isn’t complicated. In practice, the flow looks like this: 1. **Connect your database** — Draxlr supports PostgreSQL, MySQL, MSSQL, MariaDB, Supabase, PlanetScale, Databricks, and more. 2. **Choose your KPIs** — Whether it’s churn, MRR, average order value, or campaign ROI. 3. **Build your dashboard** — Use Draxlr’s Visual Query Builder, AI SQL Assistant, or write raw SQL if you prefer. 4. **Share insights instantly** — Create links, embed dashboards in your tools, or set Slack and Email alerts for KPI changes. --- ## Examples of KPI Dashboards You Can Build Different teams care about different KPIs. An SQL dashboard tool makes it possible to serve everyone from a single database: - **SaaS Teams** – Track Monthly Recurring Revenue (MRR), churn rate, and daily active users. - **Ecommerce Teams** – Monitor average order value, repeat purchase rate, and cart abandonment. - **Operations Teams** – Keep an eye on delivery times, stock availability, and vendor SLAs. - **Marketing Teams** – Measure campaign ROI, website traffic, and conversion rates. --- ## Making KPI Dashboards Actionable A dashboard should do more than display KPIs—it should guide decisions. Here’s how to make dashboards actionable: - **Add context, not just numbers** → Show trends, targets, or comparisons alongside each KPI. - **Use filters to personalize views** → Let teams slice data by campaign, region, or customer type. - **Set alerts and notifications** → Get notified in Slack or email when KPIs cross a threshold. - **Enable drill-downs** → Move from high-level metrics (e.g., “Conversion = 12%”) into specific campaigns or user segments. - **Share where work happens** → Embed dashboards in your CRM, internal portal, or client dashboard. With Draxlr, these features are built in, so dashboards become part of everyday workflows—not just monthly check-ins. --- ## Why Choose Draxlr as Your SQL Dashboard Tool There are many BI platforms out there, but Draxlr focuses on making KPI tracking simple and fast: - **AI-first querying** → Type a question, get SQL instantly. - **Easy dashboard filters and drill-downs** → Let teams interact with the data without editing queries. - **Real-time monitoring** → Dashboards always reflect the latest numbers in your database. - **Flexible sharing** → Share links, embed dashboards, or assign role-based access. - **Deployment options** → Cloud or self-hosted, depending on your compliance needs. Instead of adding another layer of complexity, Draxlr removes the friction between data and decision-making. --- ## Conclusion Monitoring KPIs is essential—but it’s only effective when the data is live, centralized, and easy to act on. An **SQL dashboard tool** like Draxlr gives your team a real-time view of the metrics that matter most, without manual work or technical bottlenecks. If you’re ready to replace spreadsheets with live dashboards, try [Draxlr](https://www.draxlr.com/) today and start building your KPI dashboard in minutes. --- ## FAQs ### 1. What is an SQL dashboard tool used for? An SQL dashboard tool connects directly to your database and helps you build live dashboards to track KPIs. It allows teams to monitor business performance in real time without relying on static reports or spreadsheets. ### 2. Which KPIs can I track with an SQL dashboard tool? You can track almost any KPI stored in your database, including: - Revenue and MRR for SaaS - Conversion rates and average order value for ecommerce - Customer churn and retention metrics - Operational data like delivery times or inventory levels ### 3. How do SQL dashboards stay up to date? Unlike spreadsheets, SQL dashboards pull data directly from your live database. This means KPIs refresh automatically, so you always see the latest numbers without manual updates. ### 4. Can I share KPI dashboards with my team or clients? Yes. Tools like Draxlr let you generate shareable links, embed dashboards in apps or portals, and even apply role-based access so each user sees only what’s relevant to them. ### 5. Do I need to know SQL to use a SQL Dashboard tool like Draxlr? Not necessarily. Draxlr offers a Visual Query Builder and an AI SQL Assistant for non-technical users, while still allowing analysts and developers to write raw SQL if they prefer.

Sisense Alternatives: Top 5 BI Tools to Consider in 2025

hello@draxlr.com (Draxlr) — Tue, 26 Aug 2025 00:00:00 GMT

Choosing the right business intelligence (BI) platform is critical for organizations that want to make informed, data-driven decisions. **Sisense** has long been a popular choice, known for its embedded analytics and AI-powered insights. However, like every tool, Sisense comes with its own limitations—ranging from steep learning curves to high costs. --- ## What is Sisense? Sisense is a BI platform that enables businesses to connect multiple data sources, analyze them, and deliver insights through interactive dashboards. It offers strong embedded analytics capabilities and advanced features like in-memory processing (via ElastiCube) and AI-driven insights. ### Drawbacks of Sisense While Sisense is a robust tool, many users highlight these pain points: - **Steep learning curve & complex implementation:** Initial setup and customization require technical expertise. - **High cost:** Pricing can be prohibitive for small to mid-sized businesses. - **Scaling challenges:** Performance may slow as data volume and complexity grow. - **Customization limits:** Deep customization of embedded dashboards is limited. - **Dashboard inflexibility:** Adjusting layouts or precise designs can be difficult. - **Exporting & scheduling issues:** Features like PDF export and scheduled reporting are weaker than competitors. - **Support and stability concerns:** Users cite performance issues with ElastiCubes and mixed experiences with support. --- ## Top 5 Alternatives to Sisense ### 1. Draxlr **Draxlr** is a modern BI platform built for teams that want powerful analytics without unnecessary complexity. Unlike Sisense, Draxlr offers a straightforward experience for both technical and non-technical users. **Why Choose Draxlr:** - Simple yet powerful **Query Builder** for SQL and no-SQL users. - **AI SQL generation** for natural language queries. - Rich visualization options: charts, pivot tables, and dashboards. - **Embedded analytics** with domain control and multi-tenant support. - Flexible **self-hosting** options for enterprises prioritizing security. - Cost-effective compared to larger BI platforms. Draxlr is especially useful for SaaS companies, startups, and SMBs that need an easy-to-use no-code platform without overwhelming costs or complexity. --- ### 2. Holistics **Holistics** is a cloud-based BI platform designed with engineers and product teams in mind. Its semantic modeling ensures data consistency across the organization. **Why Choose Holistics:** - Streamlined embedded analytics with minimal engineering overhead. - Strong semantic data modeling. - Drag-and-drop dashboard building for self-service exploration. - SQL-first approach, giving developers greater control. Holistics is a great option for teams that want consistency in reporting and easy integration with their products. --- ### 3. Zoho Analytics **Zoho Analytics** is a cost-effective, AI-powered BI tool ideal for small and medium-sized businesses. **Why Choose Zoho Analytics:** - Transparent, budget-friendly pricing. - Built-in AI assistant for natural language querying. - Easy drag-and-drop interface for building dashboards. - Integrates seamlessly with other Zoho applications. This platform is well-suited for SMBs that want affordable BI without compromising on core features. --- ### 4. Domo **Domo** is a cloud-native BI platform designed for real-time collaboration and data sharing. **Why Choose Domo:** - Real-time dashboards and live data integration. - Drag-and-drop dashboard creation. - Mobile-friendly access with collaboration tools. - AI-powered insights built directly into dashboards. Domo works well for larger teams and enterprises that prioritize real-time collaboration and mobile-first accessibility. --- ### 5. Tableau **Tableau** is one of the most recognized names in the BI space, celebrated for its powerful data visualizations. **Why Choose Tableau:** - Advanced, interactive visualizations with a drag-and-drop interface. - Strong AI features such as natural language querying. - Extensive integration with databases and third-party tools. - Suitable for both technical analysts and business users. Tableau is best for organizations that want deep, visually rich analytics and can invest in enterprise-grade BI. --- ## Quick Comparison Table

Alternative	Strengths	Ideal For
Draxlr	Query Builder, AI SQL, embedded analytics, cost-effective	SaaS products, startups, enterprises
Holistics	Easy embedding, semantic consistency, SQL-first	Engineering teams embedding BI
Zoho Analytics	Affordable, AI insights, intuitive UX	SMBs and cost-conscious businesses
Domo	Real-time data, mobile-friendly, collaborative	Large teams and enterprises
Tableau	Rich visualizations, AI features, broad integrations	Analysts and visualization-driven teams

--- ## Conclusion Sisense remains a strong player in the BI industry, but it may not fit every company’s needs due to its cost, complexity, and scaling challenges. If you’re evaluating **Sisense alternatives**, one of the most compelling choices is **Draxlr**. It combines affordability, AI-powered SQL generation, flexible embedding, and self-hosting capabilities—all in a platform that is simple to set up and easy to use. For organizations that want the power of enterprise BI without the heavy overhead, Draxlr is a practical and modern alternative worth serious consideration. --- ## FAQs **1. Why should I consider a Sisense alternative?** Sisense is powerful but can be expensive, complex to implement, and less flexible in certain areas like dashboard customization and export scheduling. Exploring alternatives helps you find a tool that better fits your needs. **2. What makes Draxlr stand out among alternatives?** Draxlr offers a clean balance of affordability, advanced features like AI SQL, and ease of use. It’s particularly strong for startups and SaaS companies looking for embedded analytics. **3. Is Draxlr suitable for enterprises?** Yes. Draxlr provides self-hosting options, white-label, and multi-tenant embedding—features enterprises often require for security and scalability. **4. How do I choose the right Sisense alternative?** Focus on your priorities—whether that’s cost, embedding flexibility, real-time collaboration, or advanced visualizations. Then, compare platforms like Draxlr, Tableau, or Domo against those requirements.

How to Create a Public Dashboard URL for Your SQL Database

hello@draxlr.com (Draxlr) — Mon, 25 Aug 2025 00:00:00 GMT

If you’ve ever needed to share insights from your SQL database without giving direct database access, you know how tricky it can be. You want the recipient to see the right data—without making them sign up or log in to yet another app. That’s where **Draxlr** comes in. With Draxlr, you can **connect your SQL database, build a dashboard, and share it with anyone via a secure public URL**—even with **PIN code protection** for extra privacy. --- ## Step 1: Connect Your SQL Database to Draxlr Draxlr supports all major SQL databases—PostgreSQL, MySQL, SQL Server, Databricks, and more. 1. Sign in to your Draxlr account. 2. Head to the Add Connection page. 3. Select your database type, fill in connection details (host, port, database name, username, password), and save. Once connected, Draxlr can query your database in real-time, so your dashboard is always up to date. --- ## Step 2: Build Your Dashboard With Draxlr’s **Query Builder** and **AI** features, you don’t need to be a SQL expert to create beautiful charts and tables. - Write your own SQL query, or - Use the AI SQL Generator to build queries from plain English prompts, or - Use the Query Builder to generate results. After running a query, you can choose from multiple visualization types — bar charts, line graphs, tables, pivots, and more—to represent your data clearly. Arrange these visualizations into a custom dashboard. ![Dashboard Example](/images/blogs/create-a-public-dashboard-url-for-your-sql-database/dashboard.png) --- ## Step 3: Create a Public Dashboard URL Once your dashboard is ready, you can make it shareable: 1. Open your dashboard in Draxlr. 2. Click the **Share Dashboard** option. 3. Create a Public URL with option to provide values to filters, this is useful if you want to share the Dashboard with filtered data. Example - creating a single dashboard and then creating multiple Public URLs (one per customer) with filtered customer's data. This will generate a unique public link that anyone can open in their browser—no login required. ![Public Dashboard Form](/images/blogs/create-a-public-dashboard-url-for-your-sql-database/form.png) --- ## Step 4: PIN Code Protection Sometimes, you want to share your dashboard but still control access. Draxlr's Public Dashboard URLs are pin-protected by default. Anyone opening the dashboard will be prompted to enter the PIN before they can view it. --- ## Step 5: Share the URL Share the public dashboard link via email, chat, etc. Your viewers will always see the **latest data from your SQL database** without you needing to send files manually. ![Public URL](/images/blogs/create-a-public-dashboard-url-for-your-sql-database/public.png) --- ## Why Use Draxlr for Public Dashboard URLs? - **Live SQL data** — no static exports. - **Custom visualizations** to present your data effectively. - **PIN code security** for controlled access. - **No coding required** for dashboard creation. - **Filter-specific URLs** Create a Public URL with preset filter values. This is useful if you want to share the same dashboard with different filtered views. For example, you can build one dashboard and then generate multiple Public URLs (one per customer) that automatically show each customer’s data. --- ## Conclusion With Draxlr, turning your SQL database into a shareable, secure, and visually appealing public dashboard takes just a few clicks. Whether you’re sharing metrics with a client, reporting to a partner, or publishing open data for the public, Draxlr gives you control and simplicity. --- ## FAQs ### 1. How to create a dashboard link? In Draxlr, open your dashboard, go to the Share option, and create a Public URL. A unique link is generated instantly which you can copy and share. ### 2. What is a dashboard URL? A dashboard URL is a shareable link that lets others view your SQL dashboard in their browser without logging in. You can also add a PIN code for extra privacy. ### 3. Can I share filtered versions of the same dashboard? Yes. You can create multiple public URLs for a single dashboard, each with predefined filter values (e.g., one link per customer). ### 4. Do viewers need a Draxlr account to see the public dashboard? No. With Public URLs enabled, recipients can view the dashboard directly in their browser without creating an account. There is no charge for viewers. ### 5. Is the data live in Public Dashboard URLs? Yes. Public dashboards in Draxlr always pull the latest data from your SQL database, so viewers see up-to-date insights.

How to Analyze Google Sheets Using BigQuery and Draxlr

hello@draxlr.com (Draxlr) — Mon, 18 Aug 2025 00:00:00 GMT

For businesses, data-driven decisions are essential for steering growth and strategy in the right direction. Google Sheets is an excellent tool for collecting and sharing information, but when your data grows or you need deeper analysis, it can quickly hit its limits. BigQuery changes that by letting you run fast, complex queries on massive datasets without performance issues. Paired with Draxlr, you can turn that data into clear, interactive dashboards in minutes (no technical skills required). In this guide, you’ll learn how to: - Connect Google Sheets to BigQuery. - Connect BigQuery to Draxlr. - Analyze and visualize your data using Draxlr’s AI and visual tools. --- ## Connect Google Sheets to BigQuery Connecting Google Sheets to BigQuery allows you to scale your spreadsheet-based workflows. Instead of working within the limits of Google Sheets, you can use BigQuery’s analytical power to query large datasets, join multiple sheets, and use SQL to find insights faster and more reliably. #### Step 1: 1. Prepare Your Google Sheet 2. Ensure your data has a clear header row with proper column names. #### Step 2: 1. Visit Google BigQuery Console. 2. Select an existing project or create a new one and then create/select the dataset. #### Step 3: 1. Clicking on Create table and Choose Google Drive as Create table from option. 2. Paste your shareable Google Sheets URL. 3. Select Google Sheets as the format. 4. Confirm header row and configure column types. 5. Save and your Google Sheet is now available as a BigQuery table ready for analysis. > Make sure to share the google sheet with the service account email of your BigQuery project. ![Adding Google Sheets to BigQuery](/images/blogs/how-to-analyze-google-sheets-using-bigquery-and-draxlr/images/connecting-google-sheet-with-bigquery.png) --- ## Connect BigQuery to Draxlr Once your data is in BigQuery, Draxlr helps you explore and understand it using AI-generated queries or SQL queries. This connection enables businesses to gain valuable insights without relying heavily on technical teams, making it easier to drive decisions across teams. 1. Go to Draxlr and sign in 2. From your dashboard, click on "+ Connect a Database" 3. Select BigQuery and provide the required details: - Connection Name - Service Account JSON Key 4. Click on "Next" and Draxlr will connect to your BigQuery project and sync your tables. ![Connecting BigQuery](/images/blogs/how-to-analyze-google-sheets-using-bigquery-and-draxlr/images/connecting-bigquery-to-draxlr.png) ## Analyze Your Sheet Data in Draxlr With the connection set up, you can now explore and analyze your spreadsheet data in depth. Draxlr currently supports two methods for working with BigQuery data: the AI Query Builder and SQL editor. These tools help uncover trends, segment data, and extract key business metrics quickly and efficiently. #### AI Query Builder - Type your request in plain language (e.g., "Show revenue growth by quarter"). - Draxlr generates the SQL and runs the query for you, making analysis accessible to non-technical users. - Draxlr will automatically generate Graphs and Charts based on the query results, allowing you to visualize your data instantly. ![Draxlr AI BigQuery](/images/blogs/how-to-analyze-google-sheets-using-bigquery-and-draxlr/images/draxlr-ai-bigquery.png) #### SQL Editor - Use standard SQL to run advanced analytics, join datasets, or filter records. - You can visualize the results using table, dashboard table or charts. - Ideal for users comfortable with writing custom queries. **Note**: Query Builder for BigQuery is coming soon. ## Visualize and Share Insights Turning data into action requires clear communication. Draxlr helps you create interactive dashboards and reports, making insights easy to digest and share with your team, stakeholders, or clients - all without leaving your browser. - Once you have saved your queries, you can visualize the results using Draxlr's built-in charting tools. - You can use these saved queries to build interactive dashboards that update automatically as your data changes. - You can share these insights with your team or stakeholders by inviting them to Draxlr, sharing the public link or embedding them in your own application. --- ## Conclusion For businesses, the ability to quickly transform raw data into actionable insights is critical. By combining Google Sheets, BigQuery, and Draxlr, you get the flexibility of spreadsheets, the speed and scale of enterprise-grade SQL, and the clarity of visual analytics - enabling informed, data-driven decisions. Start analyzing your Sheets data with Draxlr today Connect your BigQuery Database \- **Jaskaran Singh**

Pull data from API Third-party to Supabase

hello@draxlr.com (Draxlr) — Tue, 29 Jul 2025 00:00:00 GMT

External APIs often hold valuable data—from analytics to transactions to user behavior. But using it effectively often means pulling it into your own database, where you can visualize and analyze it at scale. In this guide, we'll show how to **fetch data from a public API and store it in your own database** using **Supabase Edge Functions**. From there, you’ll be able to build dashboards, trigger alerts, and unlock real insights. To keep things simple, we’ll use the free and fake API [JSONPlaceholder](https://jsonplaceholder.typicode.com/) and store the data in a **posts** table inside Supabase. --- ## Why Supabase? [Supabase](https://supabase.com) is a great backend-as-a-service that combines a PostgreSQL database with serverless edge functions, real-time subscriptions, and a slick dashboard. It’s a powerful option if you’re looking to build quickly without managing infrastructure. In this example, we’ll use Supabase’s Edge Functions to pull in external data and store it directly in the database. --- ## What You’ll Learn? - How to set up Supabase Edge Functions - How to fetch data from an external API - How to insert that data into your Supabase database - How to prepare your data for dashboards --- ## Prerequisites - A Supabase project - Node.js and Supabase CLI installed - Basic familiarity with JavaScript / TypeScript --- ## Step 1: Set Up Your Supabase Project If you don’t already have a Supabase project, create one at [supabase.com](https://supabase.com/). Install the Supabase CLI: ```bash npm install -g supabase ``` Initialize Supabase in your project folder: ```bash supabase init ``` --- ## Step 2: Create a Table in Supabase Go to the **SQL Editor** in Supabase and run this SQL to create a `posts` table: ```sql create table posts ( id integer primary key, userId integer, title text, body text ); ``` ![Create Table](/images/blogs/pull-data-from-api-to-supabase/create-table.png) --- ## Step 3: Create a Supabase Edge Function In your project directory: ```bash supabase functions new fetch-posts ``` Replace the content of `supabase/functions/fetch-posts/index.ts` with the following: ```ts import { serve } from "https://deno.land/std/http/server.ts" serve(async () => { const res = await fetch("https://jsonplaceholder.typicode.com/posts") const posts = await res.json() const supabaseUrl = Deno.env.get("SUPABASE_URL")! const supabaseKey = Deno.env.get("SUPABASE_SERVICE_ROLE_KEY")! const insertRes = await fetch(`${supabaseUrl}/rest/v1/posts`, { method: "POST", headers: { "Content-Type": "application/json", apikey: supabaseKey, Authorization: `Bearer ${supabaseKey}`, Prefer: "resolution=merge-duplicates", }, body: JSON.stringify(posts), }) const responseText = await insertRes.text() return new Response( JSON.stringify({ message: "Inserted posts", status: insertRes.status, body: responseText, }), { headers: { "Content-Type": "application/json" }, status: insertRes.status, } ) }) ``` --- ## Step 4: Add Environment Variables In your `.env` file: ``` SUPABASE_URL=https://your-project.supabase.co SUPABASE_SERVICE_ROLE_KEY=your-service-role-key ``` --- ## Step 5: Deploy and Run the Function ```bash supabase functions deploy fetch-posts ``` To call it via HTTP after deployment: ```bash curl -X POST https://.functions.supabase.co/fetch-posts \ -H "Authorization: Bearer " \ -H "Content-Type: application/json" ``` ![Data](/images/blogs/pull-data-from-api-to-supabase/data.png) --- ## Step 6: Build Dashboards Once data is in Supabase, visualize it using Draxlr. Example: Total Posts ![Count of Posts](/images/blogs/pull-data-from-api-to-supabase/query.png) --- ## Alternative: Use Draxlr for ETL While Supabase Edge Functions work well for basic syncing, complex integrations may require more scalable solutions. Draxlr provides a **fully managed ETL and dashboarding service**, helping teams automate data pulls and build dashboards without code.

How to build Dashboards from Moodle data

hello@draxlr.com (Draxlr) — Fri, 30 May 2025 00:00:00 GMT

[Moodle](https://www.moodle.org/) is one of the most popular Learning Management Systems (LMS) used by educational institutions worldwide. It generates a vast amount of valuable data, such as course interactions, student engagement metrics, quiz results, and more. But how can you leverage this data to gain actionable insights? With Draxlr, you can create a custom Moodle dashboard to track various metrics. This article guides you through the process of building interactive dashboards directly from your Moodle database. --- ## Using Draxlr to Build Dashboards from Moodle Data > Draxlr is a no-code business intelligence solution that allows you to create dashboards, receive data alerts, and embed dashboards on customer-facing websites using Visual query builder and powerful AI query builder. Ok, let's go! ## Connecting Moodle Database with Draxlr 1. Login into Draxlr and navigate to the [Add Database page](https://app.draxlr.com/add-database). 2. Select **MySQL/MariaDB/Postgres** from the available database options (based on your Moodle setup). ![Draxlr Select database type](/images/blogs/how-to-build-dashboards-from-moodle-data/images/mariadb-selection.png) 3. Add the database credentials into respective fields. ![Draxlr Database Credentials](/images/blogs/how-to-build-dashboards-from-moodle-data/images/credentials.png) 4. After entering the credentials, click on **Connect**. Draxlr will sync the database schema and tables. ![Draxlr database synced](/images/blogs/how-to-build-dashboards-from-moodle-data/images/connected.png) --- ## Create Queries Using the Visual Query Builder Draxlr makes it easy to explore your Moodle data using its **Visual Query Builder**—a no-code interface designed for simplicity and power. From the left panel, you can build queries step-by-step: - **Filter**: Add conditions to narrow down rows (e.g., courses with specific names). - **Join**: Combine data from multiple tables like `mdl_user`, `mdl_course`, or `mdl_enrol`. - **Aggregate**: Group and summarize data (e.g., total enrolments per course). - **Sort**: Order your results by any column. - **Select**: Choose which fields to display in the result. In the preview area, results update live so you can instantly see the effect of your changes. ![Draxlr Visual Query Builder](/images/blogs/how-to-build-dashboards-from-moodle-data/images/moodle-query-builder.png) This intuitive layout lets you build even complex queries without writing SQL, making it ideal for educators, administrators, or analysts working with Moodle data. ### Step by Step Example: Building a Dashboard for Moodle Course Data

## Use the AI Query Builder Draxlr includes an AI-powered Query Builder that works like ChatGPT to help you generate SQL queries using plain English. Simply describe what you want—for example: > Create a bar chart of count of courses by their category name The AI will instantly generate the correct SQL query for your Moodle database, which you can run, tweak, or visualize. This is especially useful if: - You're not familiar with SQL - You want to speed up report creation - You need help exploring unfamiliar Moodle tables With Draxlr's AI assistant, anyone on your team can build complex queries with ease—just ask a question. ![Draxlr AI Moodle](/images/blogs/how-to-build-dashboards-from-moodle-data/images/moodle-ai.png) --- ## Conclusion Integrating your Moodle database with Draxlr unlocks powerful data exploration and visualization capabilities—without needing to write SQL. Whether you're tracking student engagement, course performance, or enrolment trends, Draxlr makes it easy to build custom dashboards and monitor what matters. Start turning your Moodle data into insights today with Draxlr BI experience. Connect your Moodle Database \- **Jaskaran Singh**

How to create a Pivot Table from SQL Data

hello@draxlr.com (Draxlr) — Mon, 12 May 2025 00:00:00 GMT

## Introduction: What is a Pivot Table? Creating Pivot Tables from SQL Data helps organize large datasets into readable summaries. In this guide, we’ll walk through how to a Create Pivot Table in SQL and how tools like Draxlr simplify the process. A pivot table is a powerful data summarization tool that allows you to automatically sort, count, and total data stored in one table or spreadsheet and create a second table displaying the summarized data. It’s especially useful when you’re working with large datasets and want to explore trends, groupings, or performance insights. --- ## Benefits of Using Pivot Tables Pivot tables are particularly useful for: - Aggregating sales data by region or product - Analyzing customer behavior over time - Generating quick summaries for reporting purposes --- ## Creating Pivot Tables Using SQL Statements Below, we’ll cover how to use SQL to build pivot-like views manually, an option suited for technical users who need precision and control. One common approach to building SQL Pivot Tables involves using aggregate functions and conditional logic. While SQL does not offer a dedicated pivot table feature, you can create pivot-like summaries using a combination of `GROUP BY`, `CASE`, and aggregate functions. Here's an example that summarizes sales data by region, showing each product's total as a separate column: ```sql SELECT region, SUM(CASE WHEN product = 'Product A' THEN sales ELSE 0 END) AS product_a_sales, SUM(CASE WHEN product = 'Product B' THEN sales ELSE 0 END) AS product_b_sales FROM sales_data GROUP BY region; ``` You can also use `PIVOT` operators in SQL Server, which allow you to rotate rows into columns. For example: ```sql SELECT * FROM ( SELECT region, product, sales FROM sales_data ) AS source_table PIVOT ( SUM(sales) FOR product IN ([Product A], [Product B]) ) AS pivot_table; ``` This query will give you a pivot-style table with regions as rows and products as columns, showing total sales per region per product. The result looks like this:

Region	Product A Sales	Product B Sales
North	1200	900
South	800	1100
East	950	1000

--- ## Limitations Compared to a tool like Draxlr: - Writing pivot queries manually is not user-friendly and can be difficult for non-technical users. - Modifying or customizing the pivot view is time-consuming. - No built-in drill-down support to explore what's behind each number. - Sharing static SQL results requires additional tools or exports. --- ## Building Pivot Tables with Draxlr Draxlr is a no-code BI tool that simplifies dashboard and pivot table creation from SQL databases. If you're trying to Create Pivot Table in SQL but want a faster way, Draxlr is a visual solution that eliminates the need to write raw SQL for pivoting: 1. **Connect Your Database** Add your SQL database by entering the required credentials. ![Draxlr Connect Database](/images/blogs/how-to-create-pivot-table-from-sql-data/add-database.png) 2. **Query Your Data** Use Draxlr’s query builder, AI feature, or write custom SQL queries to fetch your dataset. 3. **Create Pivot Table** Choose the pivot table visualization, then configure: - Rows, Columns, Values as needed - Customize the color formatting of a cell based on its value - You can either choose a single color with conditional formatting like "highlight in red for sales number < 10" - Or, you can choose multi-color option that breaks data into 5 segments (0-20%, 20-40%, and so on). ![Draxlr Pivot](/images/blogs/how-to-create-pivot-table-from-sql-data/draxlr-pivot.png) 4. **Pivot Table Drill-Down** With Draxlr, you can [drill down](/features/drill-down/) on your pivot tables to see the data behind each table cell. ![Draxlr Pivot Drill DOwn](/images/blogs/how-to-create-pivot-table-from-sql-data/draxlr-pivot-drill-down.png) 5. **Add to Dashboard** Add the pivot table to your dashboard and share it with your team for collaborative analysis. Draxlr enables interactive, real-time pivot analysis without writing code. --- ## Real-World Use Cases of Pivot Tables - Track monthly sales by product category and region. - Compare ticket volume by department and issue status. - Analyze campaign performance by channel and time period. - Evaluate support response times across different teams. --- ## When Should You Use SQL vs. a Tool Like Draxlr? **Use raw SQL when:** - You need complete control over data transformation. - You’re working within a backend service or need custom logic. - You're comfortable writing and debugging SQL queries. **Use Draxlr when:** - You want to build visual pivot tables quickly. - You don’t want to write or maintain SQL code. - You need to collaborate and share dashboards easily. --- ## Conclusion Whether you’re using manual SQL Pivot Table queries or working with a tool like Draxlr, there are multiple ways to build Pivot Tables from SQL Data depending on your technical comfort level. Creating pivot tables from SQL data is a powerful way to analyze and interpret datasets. Writing SQL-based pivot queries is possible but comes with limitations. Draxlr streamlines the process, making pivot creation more accessible, visual, and collaborative. --- ## FAQs **1: What’s the easiest way for a non-technical user to create pivot tables from SQL data?** Using a no-code tool like Draxlr is the easiest way. It connects directly to your SQL database and offers a visual interface to build pivot tables, no SQL knowledge required. **2: Do I need to know SQL to use Draxlr?** No. Draxlr’s visual query builder lets you create pivot tables without writing SQL. **3: Can I share pivot tables created in Draxlr?** Yes. You can share dashboards or individual pivot tables with others.

5 best SQL dashboard building tools

hello@draxlr.com (Draxlr) — Fri, 02 May 2025 00:00:00 GMT

SQL dashboard building tools play a crucial role in modern business environments for data visualization and analysis. They serve the essential purpose of providing a consolidated and real-time view of key performance indicators (KPIs) and relevant metrics within an organization, which enables decision-makers to quickly grasp trends, identify patterns, and make informed choices based on data-driven insights. But what are SQL dashboard building tools? **SQL dashboard building tools** are platforms that connect directly to your SQL databases and help you turn raw query results into visual, interactive dashboards. Instead of running manual queries or exporting data into spreadsheets, these tools let you build charts, track KPIs, and monitor trends in real time, often without writing a single line of code. They’re designed to make data accessible across teams, enabling faster insights and better decision-making. Continue reading the blog as we reveal the 5 best SQL dashboard building tools for SQL data. ## SQL Dashboard Building Tools at a Glance

Tool	Best feature	Price(Starts at)
Draxlr	Generate SQL with AI.	$75/month(flat price)
Tableau	Drag-and-drop interface	$15/user/month
Looker	Unique modeling language: LookML	$5000/month
Power BI	Microsoft ecosystem	$10/user/month
Sisense	Single-stack architecture	Quotation Based

--- ## Things to consider while choosing SQL dashboard building tools ### 1. The tool should be convenient to set up. Having a tool that works great is awesome, but if it's a pain to set up, it becomes frustrating. It's crucial for an SQL dashboard building tool to be easy to start using, ensuring that people can quickly get it up and running without unnecessary complications. ### 2. The tool should have a variety of visualization options. A great SQL dashboard building tool offers a bunch of ways to show your data, making it easy to understand and make wise decisions based on it. Diverse visualization options elevate a tool, transforming data into an engaging, comprehensible experience for users. ### 3. The tool should support embedding. All effective SQL dashboard building tools go beyond stand-alone dashboards, and they should integrate into various applications or websites well. Whether spotlighting your analytics or illustrating order origins, embedding capabilities enhance the tool's adaptability. Users can seamlessly integrate its features into critical spaces, maximizing its functionality where it counts the most. ### 4. The tool should be easy to use to collaborate with team members. Efficient collaboration is imperative, and a premium tool should seamlessly enable this. Consider a scenario where a tool, despite possessing exceptional features, complicates teamwork, defeating its purpose. Effective teamwork is foundational for success. Features facilitating straightforward collaboration transform the tool into a collaborative asset, ensuring members can collaborate effortlessly, thereby elevating overall productivity. ### 5. The tool should be capable of handling real-time or near-real-time data updates. A proficient tool should ensure real-time or near-real-time data updates, avoiding the display of outdated information on the dashboard. Displaying outdated information on the dashboard diminishes its value. Users expect current stats when accessing the dashboard, emphasizing the importance of the tool's capability to deliver timely and relevant data for a more meaningful and informed user experience. ### 6. The tool should be able to set up alerts or notifications on data thresholds. Enabling alerts or notifications on data thresholds is crucial for staying informed about significant changes, such as new installations or downgraded plans. SQL dashboard building tools should offer this feature through communication tools or email, ensuring timely notifications when specific data thresholds are reached. This enhances user awareness and facilitates proactive responses to critical data changes. ### 7. The tool should be able to handle large databases. The tool's scalability is compromised if it struggles with querying large datasets. Even smaller applications can accumulate substantial database data, necessitating robust handling capabilities from the tool. Therefore, SQL dashboard building tools should effectively manage large databases to ensure their scalability and performance and meet diverse data requirements. --- ## The 5 best SQL dashboard building tools

1. Draxlr

![Draxlr](/images/blogs/_tools/draxlr.png) Draxlr is a **modern self-serve business intelligence tool** for growing businesses. Seamlessly connecting with multiple SQL databases, it transforms raw SQL data into polished dashboards effortlessly within minutes, eliminating the need for coding skills. Empowering users to effortlessly visualize and interpret data, Draxlr is tailored for modern business insights. #### Who is it for? Draxlr caters to small and medium-sized enterprises and entrepreneurs with freelance support. It addresses the specific needs of those seeking a comprehensive overview of critical data points, complemented by timely alerts. Draxlr empowers users to stay informed and make informed decisions, enhancing their business operations. #### Best features - **Generate SQL with AI:**
With its cutting-edge AI feature, Draxlr continues to be at the forefront of innovation. By introducing artificial intelligence to the SQL generation process, users can effortlessly generate complex queries simply by communicating their requirements through written text. - **Customizable Dashboards:**
Users can explore a variety of graphs and charts within Draxlr, such as bar graphs, line charts, pie charts, maps, bubble graphs, and more, catering to different data visualization needs. The tool facilitates resizing dashboard elements for a personalized view. Users can effortlessly build queries and add query results with dashboard elements like "Sales Over Time," "Product Category Distribution," or "User Engagement Trends" into their dashboards for comprehensive decision-making. - **Dashboard Summary Email:**
Users have the option to receive a condensed version of the dashboard directly in their email inbox. Customize the experience by setting the preferred email addresses, delivery frequency (daily or weekly), and specific delivery time. This feature ensures that the dashboard's key insights are conveniently delivered to users' inboxes at their specified intervals. - **Public Dashboard Sharing with PIN:**
Draxlr facilitates effortless sharing of specific dashboards with team members or external collaborators. Generate **public URLs** for single or multiple dashboards, securing access through a regeneratable PIN for enhanced security. This feature ensures seamless collaboration and information sharing while maintaining control over dashboard access. - **Embed dashboard:**
Draxlr lets you spice up any website with the embedded dashboard feature. And the best part: the flexibility to choose whether to include individual filters in the embedded dashboard adds a user-centric touch. Additionally, users have control over embedding permissions by specifying not-allowable domains, ensuring a secure integration tailored to their specific needs. - **Live mode:**
Upon entering Draxlr's live mode, the displayed data reflects real-time changes. This feature proves valuable for dynamic showcases, such as presenting the live subscriber count of your app on a TV screen. - **No-code query builder:**
Leveraging Draxlr's advanced filters, such as joins, summarize, and more, users can effortlessly generate queries without writing any SQL code. Additionally, a single click on the **generate insight button** automatically produces valuable query results from any table, streamlining the query generation process for enhanced user convenience. - **Drill Down:**
Users benefit from Draxlr's user-friendly one-click drill-down functionality. This intuitive feature allows for a smooth transition from broad insights to in-depth data, enhancing the overall experience of exploring and analyzing information. - **Alerts - Slack, Email:**
With Draxlr, users receive data change snapshots through both Slack and email. The tool allows for flexible alert settings, enabling users to schedule alerts at specific times (e.g., daily at 11 AM for yesterday's new installs count), set periodic updates (e.g., every 4 hours), or opt for alerts triggered solely by actual data changes. - **Google Sheets integration:**
Users can seamlessly generate queries in Draxlr and effortlessly import the results into Google Sheets, streamlining the data-sharing process for enhanced collaboration and accessibility. - **Access saved query results via API**
Users can retrieve saved query results through API responses, providing a resource for individuals looking to enhance and customize their dashboards independently. - **Export CSV:**
Users can efficiently export various dashboard elements into a CSV format using Draxlr, providing a straightforward solution for data extraction and further analysis. ---

2. Tableau

![Tableau](/images/blogs/5-best-sql-dashboard-building-tools/images/tableau.png) Tableau is a business intelligence software that was founded in 2003 to assist individuals in gaining a better understanding of their data. Tableau allows you to transform data into insights that can be put into action. #### Who is it for? Tableau is well-suited for midsize to large businesses with a larger financial capacity to meet their diverse data visualization requirements. #### Best features - Tableau lets you analyze and see data visually with a drag-and-drop interface. - The Tableau Embedding Playground lets you embed Tableau visualizations. - You can connect to a lot of different data sources with Tableau. - Tableau has many types of charts, such as treemaps, bubble charts, and more. - It offers a range of deployment options, such as on-premises, cloud, and hybrid solutions. You can read about Tableau alternatives here: [7 Tableau Alternatives](/blogs/7-tableau-alternatives-in-2025/). ---

3. Looker

![Looker](/images/blogs/5-best-sql-dashboard-building-tools/images/looker.png) Looker is yet another business intelligence tool that Google's GCP purchased in 2019 to offer dashboarding and reporting solutions. #### Who is it for? Looker is primarily designed for large businesses that have substantial budgets. However, in order to use it, one must first become familiar with LookML, which necessitates support from the respective developers. #### Best features - Looker uses a powerful SQL-based modeling language called LookML to create semantic data models. - Looker is directly connected to the database. It is BYODB. - Looker is based in the cloud. There is no desktop version or such. - Looker supports exportable reports and visualizations. ---

4. Power BI

![Power BI](/images/blogs/5-best-sql-dashboard-building-tools/images/powerbi.png) Power BI is one of the leading business intelligence solutions that was officially released by Microsoft in 2015 to provide dashboarding and reporting solutions. #### Who is it for? Power BI is for a wide spectrum of users, but mostly for large businesses. #### Best features - Power BI allows users to embed Power BI reports and dashboards. - Power BI includes a natural language query feature called Q&A, allowing users to ask questions about their data in plain language and receive interactive visual responses. - The platform supports custom visualizations, allowing users to extend their reporting capabilities by incorporating visuals from the Power BI marketplace or creating their own. - Power BI offers extensive data connectivity, allowing users to connect to a wide range of data sources, both on-premises and in the cloud. ---

5. Sisense

![Sisense](/images/blogs/5-best-sql-dashboard-building-tools/images/sisense.png) Sisense is the last business intelligence platform on our list, and it was founded in 2004. It operates on a single-stack architecture to provide insights as dashboards. #### Who is it for? Sisense is for midsize businesses and can also be considered for large ones. The pricing plan is a custom plan for each business. #### Best features - Offers both an on-premise and a cloud-based option. - Sisense provides drag-and-drop dashboard creation functionality. - Provides augmented analytics - It connects with 100+ SQL and NoSQL databases. --- ## Conclusion We hope that this blog has assisted you in finding a dashboard-building tool that allows you to effectively visualize raw data and receive timely alerts for significant data changes. Empowering you to make informed decisions based on real-time insights. --- ## FAQs ### 1. Which is the best tool to create dashboards? The best tool depends on your needs. If you're working with SQL databases and want a self-service option, SQL dashboard building tools like Draxlr are a great choice. They let you connect directly to your SQL data, build charts, and create dashboards without heavy engineering effort. For more advanced analytics or enterprise-scale reporting, you might consider Power BI, Tableau, or Looker. Choose a tool that fits your data sources, team skills, and sharing needs. ### 2. What is a dashboard tool? A dashboard tool is a software application that helps you visualize and monitor key data in one place. It connects to data sources like SQL databases or spreadsheets and turns raw data into charts, graphs, and tables. ### 3. How do I choose a dashboard tool? Start by identifying where your data lives, whether it is in SQL databases, spreadsheets, cloud apps, etc. Then consider who will use the tool, like do they need a no-code interface, or are they comfortable with SQL? Then look for features like real-time data updates, chart customization, sharing options, and access control. If you want to embed dashboards in your product or need alerts, make sure those are supported. Finally, test a few tools like Draxlr with a free trial to see which fits your workflow best.

Top 6 Power BI alternatives for actionable data insights

hello@draxlr.com (Draxlr) — Tue, 29 Apr 2025 00:00:00 GMT

Power BI has earned its place as a trusted business intelligence tool for many organizations. One is its integration with other Microsoft tools, another is its strong community support, and the third is its advanced machine-learning capabilities. But even with all its strengths, it’s not a one-size-fits-all solution. From pricing constraints to performance bottlenecks, many teams eventually start looking for Power BI alternatives that offer more flexibility, scalability, or simplicity. This blog introduces **6 Power BI alternatives** that empower teams to make data-driven decisions faster. --- ## What are the limitations of using Power BI? Despite its advanced capabilities, Power BI falls short in multiple ways, leading companies to search for Power BI alternatives. Below are the most common hurdles users face with Power BI. 1. Figuring out the DAX language poses a challenge. 2. The user interface is not easy-to-use with steep learning curve. 3. Power BI's lack of built-in essential charts like radar charts, box plots pushes users toward costly third-party solutions, adding unnecessary costs. 4. Power BI embedding requires an Azure subscription for full functionality, adding cost and setup effort. 5. Limits Power BI Pro users to 8 refreshes per day. Users need to upgrade to Premium for more frequent refreshes. 6. Complex licensing structure. Fortunately, we've done the in-depth analysis to save you time. Here are the top 6 Power BI alternatives in 2025: ### Top Power BI Alternatives at a glance

Power BI alternatives	Top feature	Starting price
Draxlr	User-friendly query builder, highly responsive customer support, AI-powered insights, custom features	$75/month
Qlik Sense	Associative data engine, mobile-friendly	$825/month/20 users
Zoho Analytics	Collaborative data analysis, instant alerts	$24/month/2 users
Tableau	Multilingual support, guided exploration of insights	$35/month/user
Looker	Direct access to raw data, real-time collaboration	Tailored pricing
Holistics	Auto timezone alignment, schehuled report generation	$960/month

---

1. Draxlr

[Draxlr](https://www.draxlr.com) is a business intelligence tool that simplifies SQL data analysis, making insights accessible to everyone on your team. Connect your Database ![Power BI alternative - Draxlr](/images/blogs/_tools/draxlr.png) ### Draxlr Features 1. **Data Integrations:** You can connect Draxlr to a range of data sources, including SQL, [Excel](https://www.draxlr.com/blogs/how-to-build-dashboards-from-excel-sheet/), and other 100+ third-party tools. 2. **Data Visualization:** Supports basic charts like bar and line graphs, as well as more advanced visualization options such as radar charts and pivot tables. 3. **No-Code Query Builder:** Offers no-code interface for building and modifying database queries. 4. **Slack and Email Alerts:** Draxlr sends scheduled or condition-based alerts through [Slack](https://www.draxlr.com/blogs/alerts-for-sql-data/) or [Email](https://www.draxlr.com/blogs/how-to-set-email-alerts-when-your-sql-data-changes/). 5. **AI Query Assistant:** [Text-based input generates query results](https://www.draxlr.com/blogs/how-to-generate-sql-query-with-AI/), data visualizations, and a link to explore in the builder. 6. **Drill Down:** Allows step-by-step exploration from high-level metrics to granular values directly in the dashboard. 7. **Drill Through:** Selected data points can be passed into other dashboards or reports for deeper inspection. 8. **Embeddable Analytics:** Dashboards can be embedded by copy-pasting a few lines of code into any application. 9. **Table Formatting:** Tables can be customized with badges, label text, prefix or postfix values, progress bars, and other formatting options. 10. **Dashboard Filters:** Default values can be set manually or pulled from a saved query. 11. **AI Insights:** Generate dashboard-ready insights from any table using a one-click button. 12. **Dashboard Sharing:** Dashboards can be shared through a public link, with control over PIN access and filter visibility. ### Draxlr Pricing Starts at $75 per month for 20 users. ### Draxlr Support 1. 24/7 Critical support: For P1/P2 issues. 2. Email/Case Portal: For all users. 3. Live Chat: For all users. 4. Knowledge Base: Extensive. ---

2. Qlik Sense

[Qlik Sense](https://www.qlik.com) is a Power BI alternative with an associative analytics engine that helps to explore data easily. ![Power BI alternative - Qlik Sense](/images/blogs/top-6-power-bi-alternatives-for-actionable-data-insights/images/qlik-sense.jpeg) ### Qlik Sense Features 1. Interactive and high-quality data visualizations. 2. Script-free data loading with a drag-and-drop feature. 3. The associative model links all data, revealing insights from complex relationships. 4. Data visuals with intelligent commentary for a complete perspective. 5. Integration of analysis extensions and plug-ins for enhanced functionality. 6. Rapid data indexing, compression, and real-time recalculations using the QIX engine. 7. Access Qlik Sense apps from any location or device. 8. Supports report sharing through a central hub. 9. Export reports as PDF, PowerPoint, Excel, and Access. 10. Tailored guided applications using standardized API. 11. Embed visualizations into external platforms. 12. Data security and monitoring with centralized control via the Qlik Management Console. ### Qlik Sense Pricing Starts at $825 per month for 20 users. ### Qlik Sense Support 1. 24/7 Critical support (SaaS): For P1/P2 issues. 2. Email/Case Portal: Primary for on-prem users. 3. Live Chat (SaaS only): Limited availability. 4. Phone Support: High-priority cases (premium plans). 5. Knowledge Base: Extensive. ---

3. Zoho

[Zoho Analytics](https://www.zoho.com/analytics/) lets you analyze data from over 500 sources, making reporting easier and helping you make better decisions. ![Power BI alternative - Zoho Analytics](/images/blogs/top-6-power-bi-alternatives-for-actionable-data-insights/images/zoho-analytic.jpeg) ### Zoho Analytics Features 1. Connect to databases, data lakes, and warehouses with live or periodic sync options. 2. A large range of visualization options with AI recommendations. 3. Interactive tooltips, drill-downs, and filters for on-the-fly analysis. 4. Actionable insights with natural language queries via AI. 5. AI-powered generation of automated reports and dashboards. 6. Univariate and multivariate models to anticipate future trends. 7. Instant alerts with ML-driven anomaly detection. 8. Discover patterns by clustering and analyzing data. 9. Threaded comments directly on reports and dashboards. 10. Data-rich presentations using Zoho Show, an online platform. 11. Zoho Analytics reports anytime via the mobile app. 12. Role-based access control for users, viewers, and administrators. ### Zoho Analytics Pricing Starts at $24 per month for 2 users. ### Zoho Analytics Support 1. Email/Ticket Support: Available for all users, including free-tier users, but response times are faster for paid plans. 2. Live chat: Offered to paid users (Starter plan and above). 3. Phone support: Limited to Enterprise-tier customers. 4. Documentation: Comprehensive and well-structured. ---

4. Tableau

[Tableau](https://www.tableau.com/) is a Power BI alternative with flexible deployment options that lets you connect to your data and gain insights with AI or ML. ![Power BI alternative - Tableau](/images/blogs/5-redash-alternatives/tableau.png) #### Tableau Features 1. Integrates with diverse data sources. 2. Connects to both live and external data sources. 3. Customizable pre-built dashboards for myriad use cases. 4. Duplication of dashboards and their components across worksheets. 5. Automated refresh of extracted data. 6. Data analysis in 6 languages. 7. AI-driven analysis to reveal insights across related metrics and trends. 8. Grouping of metrics by time range makes insights more relevant. 9. Compatible with mobile screens to access metrics on the go. 10. Daily mobile reminders to stay updated about key metrics. 11. Supports more than a hundred currency symbols. 12. Guided questions to explore insights. #### Tableau Pricing Starts at $35 per user per month. #### Tableau Support 1. 24/7 Phone & Email: For Tableau Creator/Enterprise. 2. Live Chat: Available for paid users. 3. Documentation: Well-structured with video tutorials. 4. Tableau Premier Support (Paid Add-on): Dedicated manager. 5. Response Time: Critical (1-2 hrs), Standard (24 hrs). ---

5. Looker

[Looker](https://cloud.google.com/looker) makes business intelligence easy with AI-powered insights and cloud infrastructure. ![Power BI Alternative - Looker](/images/blogs/5-redash-alternatives/looker.jpg) #### Looker Features 1. Connects directly to raw data. 2. Row-level and column-level security permissions. 3. Automated report creation with scheduled delivery. 4. Collaborative data app development leveraging Git version control. 5. Role-based access controls. 6. Real-time collaboration on reports through commenting and editing. 7. Custom calculations and metrics allow tailored analytics. 8. Dashboards are accessible on any device. 9. Customized report distribution to multiple recipients. 10. Data integration and analysis through an API-driven approach. 11. Looks or reports with filters for viewers and date ranges. 12. Embedded analytics to power dashboards in apps. #### Looker Pricing The sales team must be contacted to get a quote. #### Looker Support 1. Email/Ticketing: Via Google Cloud Support Portal. 2. Documentation: Extensive but can be overwhelming for beginners. 3. Chat Support: Available for paid plans (not always 24/7). 4. Dedicated support managers: Enterprise customers. ---

6. Holistics

[Holistics](https://www.holistics.io/) is a helpful data visualization tool that supports the creation and reuse of analytics for easy reporting. ![Holistics - Alternative to Metabase](/images/blogs/6-best-metabase-alternatives-in-2025/images/holistics-homepage.png) #### Holistics Features 1. Data analysis using a drag-and-drop interface. 2. Adaptive dashboards for mobile viewing. 3. Report generation in CSV, Excel, or PDF format. 4. Automatic timezone alignment for datetime values. 5. Multi-period performance comparison. 6. Complete programmatic control through Git-based versioning. 7. API support with easy workflow integration with Holistics. 8. Private links for secure access to dashboards. 9. Auto-scheduled reports to preferred platforms. 10. Immediate alerts via email, Slack, or webhook. 11. User-specific access to reports and data sources. 12. Restricted data column access. #### Holistics Pricing Starts at $960 per month. #### Holistics Support 1. Email Support: Included in all plans but responses may be delayed outside business hours 2. Documentation: Detailed. 3. Onboarding & Training: Available for enterprise customers. --- ## Conclusion The ideal Power BI alternative depends on your data strategy and business goals. Exploring various options can help you identify the best fit for your needs. [Select the right tool](https://www.draxlr.com/blogs/business-intelligence-tools-for-saas-owners/) from the options listed above to ensure cost efficiency, alignment with your team's technical expertise, and enhanced decision-making. --- ## FAQs ### 1. Is anything better than Power BI? Draxlr is a better alternative to Power BI for multiple reasons. Draxlr’s intuitive interface makes it easy for beginners and experts to explore, visualize, and analyze data effortlessly. What sets Draxlr apart is its exceptional customer support, ensuring a smooth experience from setup to execution. ### 2. What is the best alternative for Power BI? There are numerous alternatives to Power BI, but when it comes to affordability, ease of use for both technical and non-technical users, and rich visualization options, Draxlr stands out as the top choice. ### 3. What is Google's alternative to Power BI? Looker is Google's alternative to Power BI. However, with a steep learning curve and high pricing, Looker fails to be a great alternative to Power BI. Instead, tools like Draxlr offer powerful data integration and analysis features with a gentle learning curve and great affordability.

How to build Dashboards from PostgreSQL data

hello@draxlr.com (Draxlr) — Fri, 21 Mar 2025 00:00:00 GMT

[PostgreSQL](https://www.postgresql.org/) is the most advanced open-source object-relational database management system (ORDBMS) that extends SQL with advanced features, handles complex data workloads. Key features of **Postgres** are: - Postgres is always ACID compliant. - Open source. - Postgres offers advanced indexing techniques like GIN and GiST. - Postgres has more diverse SQL Data types. - Easy to scale. - Full text search. - Uses MVCC for high-performance transactions. --- ## Using Draxlr to build Dashboards from PostgreSQL data > **Draxlr** is a self-service BI tool that makes PostgreSQL data analyzation simple, giving all the users the power to generate insights easily. Here is a quick video walkthrough of how to [build a dashboard from Postgres data](https://www.draxlr.com/databases/postgres/) in **Draxlr**: Ok, let's get started. --- ## Connecting Postgres with Draxlr 1. Login into Draxlr and go to the [Add Database page](https://app.draxlr.com/add-database). 2. Choose **PostgreSQL** from the list of supported databases. ![Draxlr Select PostgreSQL](/images/blogs/how-to-build-dashboards-from-postgresql-data/images/draxlr-postgres-select.png) 3. Enter the PostgreSQL database credentials into respective fields. ![Draxlr PostgreSQL DB Credentials](/images/blogs/how-to-build-dashboards-from-postgresql-data/images/postgres_database_credentials.png) 4. Once the credentials are verified, Draxlr connects to your database and displays all available schemas. You can select the desired schema and proceed. ![Draxlr PostgreSQL Schema Selection](/images/blogs/how-to-build-dashboards-from-postgresql-data/images/draxlr-postgres-table-sync.png) 5. After selecting a schema, you can pick a table, optionally share context for better AI suggestions, and click Generate Dashboard to create your first dashboard. ![Draxlr PostgreSQL Table synced](/images/blogs/how-to-build-dashboards-from-postgresql-data/images/draxlr-postgres-onboard.png) --- ## How to build queries and dashboard from PostgresQL data? Before you can visualize your PostgreSQL data, you need queries. Draxlr offers three powerful ways to create them: 1. **No-code Query Builder** – An interactive, no-code way to play with raw data. 2. **SQL Editor** – Full flexibility to write complex queries. 3. **AI-powered Querying** – Describe what you need, and AI writes the SQL for you. #### Example: Query Builder Let's consider an example to figure out how many orders did customers have ordered. 1. Creating the visualization with Draxlr’s Visual Query Builder - Select **Orders** as the base table. - Join it with Customers using an **INNER JOIN**. - **Group by** the Contact Name of Customers. - **Count** the total Orders. - Click **Execute** to generate the query. For a more customized graph, I adjusted: - Bar colors to differentiate values. - Displaying values on the graph. - Hiding x-axis values for a cleaner look. 2. Visualize your data with bar, pie, tree map, and other chart types, and adjust them using various customization options. ![Draxlr Postgres Saved Query Graph](/images/blogs/how-to-build-dashboards-from-postgresql-data/images/draxlr-postgres-bar-graph.png) 3. Save the query, add it to the dashboard, and enable [Slack](https://www.draxlr.com/blogs/how-to-set-slack-alerts-when-your-business-data-changes/) and/or [email](https://www.draxlr.com/blogs/how-to-set-email-alerts-when-your-sql-data-changes/) alerts for real-time data updates. 4. **Your dashboard is now live!** ![Draxlr MySQL Dashboard](/images/blogs/how-to-build-dashboards-from-postgresql-data/images/draxlr-postgres-dashboard.png) 5. Give your team access or embed it where your customers can see it. --- Draxlr lets you explore your PostgreSQL data, define critical success indicators, and transform them into visual reports. Connect your PostgreSQL Database \- **Ameena**

SQL data visualization

hello@draxlr.com (Draxlr) — Thu, 20 Mar 2025 00:00:00 GMT

Visualizing SQL data using Draxlr isn’t just about making your graph look better, it's about making your data easier to interpret, ensuring accuracy, and effectively conveying the insights you want to share. Following three easier steps: **Query**, **Plot**, **Format,** will set you up with the exemplar envision. --- ## 1. Constructing a SQL Query Before creating a graph, you must generate the desired dataset by constructing SQL queries. Draxlr offers 3 distinct ways to create SQL queries: - [Query Builder](/features/query-builder/) - Raw SQL - [AI-Powered Query Generation](/features/AI/) ![image.png](/images/blogs/sql-data-visualization/image.png) --- ## 2. Plotting a graph The next step is visualizing data using charts, which is a great way to make complex information more digestible. Here are a few common types of charts that Draxlr offers: - Bar chart - Pie chart - Line chart - Area chart - Scatter chart - Scatter Bubble chart - Radar chart - Polar chart - Funnel chart - Map chart - Box and Whiskers chart - TreeMap chart - Sankey chart - Doughnut chart ![image.png](/images/blogs/sql-data-visualization/image%201.png) --- ## 3. Formatting different aspects of a Graph Formatting a graph involves tailoring different elements such as titles, labels, axes, colors, grids, and styles to make the graph more readable and visually appealing. Here are different types and examples varying to different charts for formatting a graph: ### Tooltip Label Tooltip Label is a text box or pop-up that appears when you hover over or interact with a specific data point on the graph. It can be added specifically to different axes in case of a multiple axes graph. ![image.png](/images/blogs/sql-data-visualization/image%202.png) ### Label Color Setting label colors in a graph can be useful for a variety of reasons, particularly in enhancing the graph's readability, aesthetics, and accessibility. ![image.png](/images/blogs/sql-data-visualization/image%203.png) ### Title of the Graph Providing a title to a graph enhances the understanding and context of the **data being presented.** ![image.png](/images/blogs/sql-data-visualization/image%204.png) ### Axes Title Axis titles make graphs more **accessible**, especially for people who are unfamiliar with the data or subject matter. ![image.png](/images/blogs/sql-data-visualization/image%205.png) ### Font Size Choosing the right font size ensures that your graph conveys information clearly and is accessible to a wide audience. ![image.png](/images/blogs/sql-data-visualization/image%206.png) ### Graph legend Legends act as a guide to identify what different colors, lines, or markers represent, especially in graphs with multiple data sets. ![image.png](/images/blogs/sql-data-visualization/image%207.png) ### Individual Bar Colors Individual bar colors can be set for Bar Charts. By assigning different colors to each bar, you can highlight distinctions between categories, draw attention to specific data points, and improve the overall aesthetic of the graph. ![image.png](/images/blogs/sql-data-visualization/image%208.png) ### Logarithmic Graph Plotting a **logarithmic graph** (or using a **log scale**) can be extremely useful for visualizing data that spans a wide range of values, especially when the data grows exponentially or has a few large outliers. ![image.png](/images/blogs/sql-data-visualization/image%209.png) ### Orientation of a Graph It typically refers to whether the graph is **vertical** (portrait) or **horizontal** (landscape). Choosing the correct orientation depends on the type of data you're working with, the number of categories, and the specific context in which the graph will be presented. ![image.png](/images/blogs/sql-data-visualization/image%2010.png) ### Sort Order of Axis Sorting can help reduce clutter in the graph and make the data more readable. Sorting them alphabetically, numerically, or by frequency (from highest to lowest) helps organize the chart, making it easier to follow. ![image.png](/images/blogs/sql-data-visualization/image%2011.png) ### Max Labels Grouping smaller parts into a limited number of labels is a helpful technique for maintaining clarity in a chart. It ensures that the chart is focused on the significant data points while still accounting for minor contributors. **Grouping them into "Other" prevents the chart from becoming unreadable**. ![image.png](/images/blogs/sql-data-visualization/image%2012.png) ### Axis Range Setting a range is helpful, especially when you want to focus on a specific portion of the data or ensure that the axis limits are fixed for comparison purposes. ![image.png](/images/blogs/sql-data-visualization/image%2013.png) ### Trend Line Trend lines are especially useful when you want to emphasize the general direction or behavior of the data points rather than focusing on each individual data point. Choosing the right **type of trend line** and applying it thoughtfully, you can significantly improve the communication of your data: 1. **Linear** - Steady, consistent growth or decline. 2. **Exponential -** Rapid growth or decay. 3. **Polynomial -** Complex fluctuations or multi-peak data. 4. **Logarithmic -** Fast initial change that levels off. 5. **Power -** Data showing proportional relationships. 6. **Minimum, Maximum, Average -** Smoothed minimum, maximum and average over a window. 7. **Custom Value -** Tailored to specific parameters or insights. ![image.png](/images/blogs/sql-data-visualization/image%2014.png) ### Axis Time Formatting Time-based data often spans days, weeks, months, quarters or years. Proper formatting ensures that the time axis is easy to read. Draxlr automatically determine the spacing between time intervals once the interval type is selected to match your data’s characteristics. ![image.png](/images/blogs/sql-data-visualization/image%2015.png) ### Showing as Percentage Displaying values as percentages in charts like **pie charts** and **doughnut charts** helps to visually communicate how each category contributes to the whole dataset in terms of percentage. ![image.png](/images/blogs/sql-data-visualization/image%2016.png) ### Prefix and Postfix **Prefix** and P**ostfix** are generally terms related to the positioning or formatting of data, and labels with characters to convey a relationship within the SQL charts. ![image.png](/images/blogs/sql-data-visualization/image%2017.png) ### Precision Refers to controlling the number of decimal places displayed for data points. You may want to display values with higher or lower precision, depending on the context and the level of detail you need to show. ![image.png](/images/blogs/sql-data-visualization/image%2018.png) ### Pre-formatting Adding pre-formatting to a graph typically involves configuring how data is displayed before it is visualized, means use formatting present in Query result and ensure that the presentation aligns with specific formatting needs. ![image.png](/images/blogs/sql-data-visualization/image%2019.png) --- ## Frequently Asked Questions (FAQs) ### 1. What is graph formatting in a BI tool? Graph formatting refers to the process of customizing the appearance of charts or graphs to make them clearer, more visually appealing, and easier to interpret. This includes adjusting colors, axis labels, titles, legends, fonts, gridlines, and other graphical elements. ### 2. Can I visualize SQL data in real-time? Yes, Draxlr allow for real-time data visualization by setting up live connections to your SQL database. Meaning the charts will automatically update when the underlying data changes. ### 3. What kind of tool is SQL visualization? SQL visualization is a tool that transforms raw SQL query results into interactive charts and graphs for better data analysis. Draxlr is a powerful SQL visualization tool that allows users to run queries, create insightful visualizations, and format graphs effortlessly. With Draxlr, you can convert complex SQL data into easy-to-understand visual insights, helping you make data-driven decisions quickly. 🚀 --- ## Conclusion Using Draxlr, you can ensure your data has **Improved Readability, Visually Appealing, Highlighting Outliers**, **Trends**, and **Tailoring to Audience Needs.** Draxlr provides a wide range of chart types, each chart type comes with extensive customization options, allowing users to personalize their visualizations to match their data needs and presentation preferences.

Supabase functions to SQL queries: Your Guide to Advanced Data Analytics

hello@draxlr.com (Draxlr) — Fri, 07 Mar 2025 00:00:00 GMT

[Supabase](https://supabase.com/) has quickly become a favorite platform among developers thanks to its ease of use, powerful real-time features, and integrated database management. While Supabase provides a robust set of built-in functions for common database tasks, many users often find themselves needing deeper insights through custom analytics queries. In this blog, we'll explore how some common Supabase functions translate directly into SQL queries, highlighting how understanding this equivalence can unlock greater analytical power and flexibility. We'll also introduce you to **Draxlr**, a powerful analytics platform that seamlessly integrates with your Supabase database, empowering you with both visual query building and raw SQL capabilities. --- ## From Supabase Functions to Raw SQL Here's a quick comparison using a bookstore database: **1. Fetching Data (Select)** - **Supabase:** ```javascript supabase.from("book").select("title, isbn13") ``` - **SQL:** ```sql SELECT title, isbn13 FROM book; ``` **2. Filtering Data** - **Supabase:** ```javascript supabase.from("book").select("*").eq("publisher_id", 10) ``` - **SQL:** ```sql SELECT * FROM book WHERE publisher_id = 10; ``` **3. Aggregations** - **Supabase:** ```javascript supabase.from("order_line").select("SUM(price)").gte("price", 15) ``` - **SQL:** ```sql SELECT SUM(price) FROM order_line WHERE price >= 15; ``` **4. Ordering and Limits** - **Supabase:** ```javascript supabase .from("book") .select("*") .order("publication_date", { ascending: false }) .limit(5) ``` - **SQL:** ```sql SELECT * FROM book ORDER BY publication_date DESC LIMIT 5; ``` --- ## Complex Queries with Supabase and SQL Consider some advanced examples from a bookstore database: **1. Join Multiple Tables** - **Supabase:** ```javascript supabase .from("cust_order") .select(`order_id, order_date, customer(customer_id, customer_address(city))`) .eq("customer.customer_id", 5) ``` - **SQL:** ```sql SELECT cust_order.order_id, cust_order.order_date, address.city FROM cust_order INNER JOIN customer ON cust_order.customer_id = customer.customer_id INNER JOIN customer_address ON customer.customer_id = customer_address.customer_id INNER JOIN address ON customer_address.address_id = address.address_id WHERE customer.customer_id = 5 ``` **2. Aggregating and Grouping Data** - **Supabase:** ```javascript supabase.from("order_line").select("book_id, sum(price)").group("book_id") ``` - **SQL:** ```sql SELECT book_id, SUM(price) AS total_sales FROM order_line GROUP BY book_id; ``` **3. Total orders count** - **Supabase:** ```javascript supabase.from("cust_order").select("*", { count: "exact", head: true }) ``` - **SQL:** ```sql SELECT COUNT(*) AS total_orders FROM cust_order; ``` **4. Top 5 best selling books** - **Supabase:** ```javascript supabase .from("order_line") .select("book_id, sum(price)") .order("sum(price)", { ascending: false }) .limit(5) ``` - **SQL:** ```sql SELECT book_id, SUM(price) AS total_sales FROM order_line GROUP BY book_id ORDER BY total_sales DESC LIMIT 5; ``` **5. Nested Subqueries** - **Supabase (through RPC or Raw SQL):** Since Supabase doesn't directly support raw SQL queries through its standard client methods, you can create a custom Postgres function and invoke it via RPC: **Step 1:** Create a custom function in Supabase ```sql CREATE OR REPLACE FUNCTION fetch_books_by_author(name text) RETURNS TABLE(title varchar) AS $$ BEGIN RETURN QUERY SELECT book.title FROM book JOIN book_author ON book.book_id = book_author.book_id JOIN author ON book_author.author_id = author.author_id WHERE author.author_name = name; END; $$ LANGUAGE plpgsql; ``` **Step 2:** Invoke the function using Supabase client ```sql supabase.rpc('fetch_books_by_author', { author_name: 'J.R.R. Tolkien' }); ``` --- ## Why Go Beyond Supabase Functions? Supabase functions are excellent for quick queries and basic database operations. However, for advanced analytics, custom reports, and in-depth insights, mastering raw SQL or leveraging a visual query builder can significantly enhance your capabilities. --- ## Unlock Advanced Analytics with Draxlr With **Draxlr**, you can: - **Directly connect to your Supabase database** in just a few clicks. - **Visually build complex SQL queries** without manually writing code. - **Seamlessly switch between visual queries and raw SQL**, perfect for both beginners and experts. - **Instantly perform advanced analytics** such as joins, nested queries, and complex aggregations. - **Create stunning dashboards** with a wide range of visualization options. - **Set up automated alerts** to monitor key metrics and data changes. Whether you're a developer looking to streamline analytics workflows or a data analyst diving deeper into Supabase data, Draxlr empowers you to achieve more with ease. It helps you explore and visualize your data, monitor key metrics, and share insights across your organization. Here is a quick video walkthrough of how to build a dashboard from **Supabase data** in **Draxlr**: Ready to supercharge your Supabase analytics? Connect your Supabase Database \- **Jaskaran Singh**

How to build MySQL Dashboards from MySQL data

hello@draxlr.com (Draxlr) — Fri, 28 Feb 2025 00:00:00 GMT

A MySQL dashboard gives teams access to real-time data they need to make better decisions. But without it, metrics stay buried in a database, and every update requires engineering help. You wait longer to spot trends, track progress, or respond to changes. That delay adds up. Visibility shouldn't be a bottleneck. Here’s what usually happens without a MySQL dashboard: 1. You wait for a developer to run a query 2. The numbers arrive hours or days later 3. By then, the moment to act may have passed **Your business metrics should be available on demand, not behind a request queue.** Dashboards for MySQL solve this by turning raw data into live, accessible insights without waiting on someone to write a query every time you need an update. Whether you're tracking product usage, customer metrics, or operational KPIs, MySQL dashboards give everyone a clear view of what’s happening, as it happens. This guide covers two ways to build MySQL dashboards from your MySQL data: 1. Build it yourself(if you have time). 2. Or use a BI tool like Draxlr to save time and build dashboards for MySQL. --- ## First, a quick look at MySQL [MySQL](https://www.mysql.com/) is the most popular open-source relational database management system (RDBMS) that simplifies data management with structured tables, relationships. Key features of **MySQL** are: - Uses structured tables to store and manage data. - Open source. - Handles large databases with support for indexing, partitioning, and replication. - Cross-platform compatibility. - Supports standard SQL syntax for queries and data manipulation. - JSON support. --- ## How to build MySQL dashboard with your own code If you have engineering resources and want full control, you can build MySQL dashboards directly from your MySQL data using backend and frontend charting libraries. This approach gives you flexibility in design, logic, and hosting. In fact, many teams choose to build dashboards for MySQL from scratch when they need deep customization, tight integration with internal tools, or full control over how data is queried and displayed. Here’s what the process typically involves: 1. Writing backend code (using Rails, Node.js, Django, etc.) to query your MySQL database. 2. Processing the results into the format your frontend can use. 3. Using libraries like [D3.js](https://d3js.org/) to display visualizations in your app or internal tool. While this gives you full control, it comes with trade-offs: 1. Requires time from your dev team. 2. Needs ongoing maintenance --- ## How to build MySQL dashboards using Draxlr > **Draxlr** is a self-serve BI solution for dashboards, automated data change alerts, and embedded analytics on customer-facing platforms. Here is a quick video walkthrough of how to [build a MySQL dashboard from MySQL data](https://www.draxlr.com/databases/mysql/) in **Draxlr**: --- ## Connecting MySQL with Draxlr 1. Login into Draxlr and navigate to the [Add Database page](https://app.draxlr.com/add-database). 2. Select **MySQL** from the available database options. ![Draxlr Select MySQL](/images/blogs/how-to-build-dashboards-from-mysql-data/images/draxlr_mysql_select.png) 3. Add the MySQL database credentials into respective fields. ![Draxlr MySQL DB Credentials](/images/blogs/how-to-build-dashboards-from-mysql-data/images/mysql_database_credentials.png) 4. Once the correct details are added, Draxlr syncs the data and helps you create a chart by selecting a table and defining the x-axis and y-axis values. ![Draxlr MySQL Table synced](/images/blogs/how-to-build-dashboards-from-mysql-data/images/draxlr_mysql_onboard.png) 5. Once the table and axes are selected, Draxlr generates the first saved query, adds it to the dashboard, and sets an alert as a part of smooth onboarding. ![Draxlr MySQL Table synced](/images/blogs/how-to-build-dashboards-from-mysql-data/images/mysql_draxlr_onboard.png) --- ## How to build queries and visualize MySQL data using Draxlr Building a query is the first step in creating MySQL dashboards, setting alerts, and embedding insights. Draxlr offers three ways to generate queries from MySQL data: 1. **Query Builder** – A no-code simple interface for defining queries using point-and-click actions. 2. **Raw SQL** – A SQL editor for writing complex and custom queries manually. 3. **AI-powered SQL Generation** – A natural language interface that translates English inputs into corresponding SQL queries. #### Example: Query Builder Let's consider an example to find the number of products in each category, 1. Let's build the above query with Draxlr's no-code query builder - Choose **Categories** as the table. - Apply a **LEFT JOIN** with the Products table. - **Group by** Category Name. - **Functions** products count. - Click **Execute**, and you’re done! 2. You can now visualize the MySQL data with various chart options, including bar, pie, tree map, and more and also modify them with various customization options. ![Draxlr MySQL Saved Query Graph](/images/blogs/how-to-build-dashboards-from-mysql-data/images/mysql_draxlr_bar_graph.png) 3. Once the query is saved with the desired MySQL visualization, you can add it to the dashboard and set-up data change notifications on [Slack](https://www.draxlr.com/blogs/how-to-set-slack-alerts-when-your-business-data-changes/) and/or [email](https://www.draxlr.com/blogs/how-to-set-email-alerts-when-your-sql-data-changes/). 4. **And your dashboard is ready!** ![Draxlr MySQL Dashboard](/images/blogs/how-to-build-dashboards-from-mysql-data/images/mysql_draxlr_dashboard.png) 5. You can make the dashboard accessible to your team and embed it into your customer-facing app. --- ## Top metrics to track in your MySQL dashboard Once your data is connected to a MySQL dashboard, these are the metrics you’ll want to see first. They reflect growth, engagement, and drop-offs—the areas that matter most to all the teams. 1. New Signups (Daily/Weekly/Monthly) 2. Trial-to-Paid Conversion Rate 3. Net Revenue Retention (NRR) 4. Monthly Recurring Revenue (MRR) 5. Customer Lifetime Value (LTV) 6. Daily Active Users (DAU) 7. Most used features 8. Drop-off points 9. Number of uninstalls --- ## Conclusion Creating MySQL dashboards from the data doesn’t have to be complicated. If you have engineering resources, building your own with code is always an option. But if you want to save time and start tracking key metrics right away, Draxlr offers a faster, more flexible way to get there. With advanced charts, [drill-down](/features/drill-down/) capabilities, and real-time filters, Draxlr helps you move from raw data to clear insights without writing and maintaining custom code. Connect your MySQL Database Whichever path you choose, the most important thing is to make your data visible. Because the sooner you can see what’s working (and what’s not), the faster you can make smarter decisions. --- ## FAQs #### 1. How to create a dashboard in MySQL? MySQL itself doesn’t have built-in dashboard interface. To create a MySQL dashboard, you need to connect your MySQL database to a visualization layer. This can be done with a tool like Draxlr that connects directly to your MySQL database and lets you build dashboards without writing code. #### 2. How to visualize data from MySQL? To visualize MySQL data, use Draxlr, which connects to your database and lets you build MySQL dashboards with advanced chart types like Sankey, Donut, Heatmap, funnel, etc and explore trends using drill-down and drill-through. You can filter data in real-time and track key business metrics without needing engineering help, making it ideal for small to mid size teams. #### 3. What is the data visualization tool for MySQL? There are various tools that can visualize data from MySQL, depending on your technical preferences. Some are code-based, requiring you to build the logic and visuals manually. Others, like Draxlr, are purpose-built to work with MySQL and let you build dashboards quickly, define metrics easily, and share insights with your team. The best tool depends on your need for speed, customization, and engineering effort.

7 best Qlik Sense Alternatives for Data Visualization and Analysis

hello@draxlr.com (Draxlr) — Mon, 24 Feb 2025 00:00:00 GMT

Qlik Sense is a great tool for your data visualization needs. However, it is not bereft of some major shortcomings. Difficulties in the initial configuration of the tool require you to seek IT support—not helpful for [self-service analytics](https://www.draxlr.com/blogs/ensuring-seamless-adoption-of-self-service-bi/). Additionally, its steep learning curve as one starts using it makes it an unfavorable option for your data analytics requirements. The delayed performance when working with large datasets impedes quick decision-making. In light of these facts, the pricing structure that Qlik Sense offers seems staggering in comparison to other BI tools. While Qlik Sense remains a powerful tool, these challenges make exploring alternatives a worthwhile consideration for businesses seeking a more user-friendly and more advanced analytics solution. --- ## Top 7 Qlik Sense alternatives at a glance

Qlik Sense alternatives	Learning curve	Free Trial	Starting price	Best for	G2 Rating
Draxlr	Easy	7 days	$75/month	Self-serve BI, 5-star customer support, real-time analytics	4.9
Domo	Moderate	30 days	Price on request	Enterprise analytics, collaboration	4.4
Tableau	Steep	14 days	Price on request	Data visualization, interactive dashboards	4.4
Looker	Steep	30 days	Price on request	Cloud BI, embedded analytics	4.4
Sisense	Moderate	30 days	Price on request	Big data, scalability	4.2
IBM Cognos Analytics	Steep	30 days	$10.60/user/month	Reporting, data modeling	4.0
Power BI	Steep	30 days	$10/user/month	Microsoft integration, reporting	4.5

---

1. Draxlr

[Draxlr](https://www.draxlr.com/) brings self-service BI to everyone. Makes SQL data accessible, actionable, and easy to explore, no matter your role or skill level without sacrificing advanced querying power. Connect your Database ![Draxlr - Alternative to Qlik Sense](/images/blogs/_tools/draxlr.png) ### Key features 1. Simplify query building with the **no-code visual query builder** with simple button clicks. 2. Draxlr treats every **customer like a VIP** with first-class support. 3. Build **global dashboards** by connecting and visualizing data from multiple databases and varied data sources. 4. Integrate with multiple SQL databases, Excel sheets, and 100+ third-party apps. 5. Stay updated without manual checks. **Slack and email alerts** notify you on schedule, at intervals, or only when data changes. 6. Make data-driven decisions by **embedding** interactive dashboards directly into the product or any website. 7. Draxlr’s **APIs** support provide programmatic access for querying and managing data. 8. **Drill down** through different data levels to see underlying numbers and specifics. 9. Change **how tables look** and behave by rearranging columns, renaming labels, adding prefixes or postfixes, applying category badges, adjusting alignment, and more. 10. Transform raw tables into **suggested dashboard elements** for direct data visualization. 11. Access insights with **Draxlr AI**-assisted querying. Type text inputs and get results with AI-powered query execution. 12. Manage **team member permissions** by assigning admin, read-only, or full roles. 13. View totals, averages, or counts instantly with a **summary row** that aggregates column data. 14. Group saved queries into **folders** to keep them structured and accessible. 15. Narrow down dashboard results with adjustable **dashboard filters**. 16. Write custom raw SQL queries with the **SQL editor** for advanced users. ### Pricing ![Draxlr pricing](/images/blogs/6-best-metabase-alternatives-in-2025/images/draxlr.png) ### G2 Rating [4.9](https://www.g2.com/products/draxlr/reviews) ### How to build dashboards from a SQL database in minutes with Draxlr: ---

2. Domo

[Domo](https://www.domo.com/) is a cloud-based data illustration tool that enables real-time data integration and collaboration. ![Domo](/images/blogs/7-qlik-sense-alternatives/images/domo.png) ### Key features 1. A large array of connectors to connect data. 2. Extraction of data from several file formats — .pdf, .csv, .mp4, and .xls. 3. AI tool to explore data through conversational-style chat. 4. A large suite of chart types to visualize data. 5. Receipt of data alerts through the web app, email, or any mobile device. 6. Scheduled reports of dashboard context directly in the email of both users and non-users. 7. Domo Buzz: Built-in chat tool for team collaboration over data. 8. The ability for end users to edit shared data visualizations. 9. Creation of data visualizations using pre-designed code templates. 10. Provision of own keys to encrypt company’s data. ### Cons 1. Restricted report customization options. 2. Limited theme selection for dashboards. 3. Frequent disruptions in data view and connections. ### Pricing The sales team must be contacted to request a quote. ### G2 Rating 4.4 ---

3. Tableau

[Tableau](https://www.tableau.com/) is an alternative BI tool to Qlik Sense which enables seamless data representation and analysis on the go. ![Tableau - Alternative to Qlik Sense](/images/blogs/6-best-metabase-alternatives-in-2025/images/tableau-homepage.png) ### Key features 1. Pre-built dashboards to add data and customize as per requirements for faster insights. 2. Automated collection of all data sources in one place with metadata to make data exploration easier. 3. Exponential smoothing allows for forecasting any data set that includes date dimensions. 4. Data source filters to hide sensitive data. 5. Easy collaboration within a data visualization or dashboard using comments. 6. Subscriptions to allow the receipt of regular updates of dashboards via email. 7. Role-based assignment of access to ensure the security of sensitive data. 8. AI assists with data analysis by processing queries in plain English. 9. Summarization of key insights in plain language that can be displayed on dashboards for easy understanding. 10. Keep only feature that keeps the data rows with the selected values and remove all other rows from the view for a focused analysis. ### Cons 1. Restricted selection of charts for data visualization. 2. Exported reports lose formatting when converted to non-Tableau formats. 3. Slower load times when handling large data volumes. ### Pricing The sales team must be contacted for pricing details. ### G2 Rating 4.4 ---

4. Looker

[Looker](https://lookerstudio.google.com/navigation/reporting) is a data visualization tool platform that helps organizations to explore, analyze, and visualize their data for better decision-making. ![Looker - Alternative to Qlik Sense](/images/blogs/6-best-metabase-alternatives-in-2025/images/looker-homepage.png) ### Key features 1. Access to a large number of data sources and data connectors. 2. Direct connection to raw data for in-depth insights. 3. Git-integrated IDE with version control to enable teams to work together on developing data apps. 4. Ability to schedule reports to be sent at specific times. 5. Reports that can be sent only after meeting specific conditions. 6. Looker BI findings can be embedded into tools like Salesforce, Confluence, SharePoint, and PowerPoint through its broad third-party ecosystem. 7. Enhanced security by setting up row-level and column-level security permissions. 8. Sharing of data visualizations through any device. 9. Natural language processing for querying and report generation. 10. The Looker API automates secure access by verifying users for the right content. ### Cons 1. It struggles with complex data transformations as it primarily relies on pre-processed data. 2. It is limited to merging five tables or datasets into one view. 3. The initial setup is a steep learning curve and requires technical expertise. ### Pricing Looker offers customized pricing plans. ### G2 Rating 4.4 ---

5. Sisense

[Sisense](https://www.sisense.com/) is a business analytics solution for data integration, analysis, and visualization. ![Sisense](/images/blogs/7-tableau-alternatives-in-2025/images/sisense.png) ### Key features 1. Multiple chart types for data visualization. 2. Several sample dashboards available for customization. 3. Ability to combine multiple data tables. 4. Customizable code for executing Python scripts or SQL queries to cleanse or transform data. 5. Three connection types: Elasticube to import and merge data from various sources, Build to Destination for cloud data warehouse integration, and Live for direct data source connection. 6. Four user roles to control and manage permissions. 7. AI assistant that recommends insights and generates data visualizations. 8. Sisense bots that provide answers to queries with valuable insights. 9. Access to necessary data insights within chat platforms. 10. White labeling options to replace the Sisense logo and name in emails and deployments. ### Cons 1. Limited formatting and design options. 2. Frequent instability in data models. 3. Steep learning curve with insufficient documentation for advanced configurations. ### Pricing Sisense customizes pricing as per the organization’s requirements. ### G2 Rating 4.2 ---

6. IBM Cognos Analytics

[IBM Cognos Analytics](https://www.ibm.com/products/cognos-analytics) is a data analytics platform that enables data visualization, reporting, and AI-driven insights. ![IBM Cognos Analytics](/images/blogs/7-qlik-sense-alternatives/images/ibm-cognos.png) ### Key features 1. Ability to share reports in multiple formats such as Excel, CSV, PDF, HTML, etc. 2. Time series modelling to forecast patterns and reveal deeper insights. 3. Creation of live dashboards with interactive visuals for data sharing, using AI. 4. AI will answer the asked queries and generate insights about selected data sources in natural language. 5. Mobile app to access data and receive data change notifications from anywhere. 6. Ability to connect to cloud or on-premises systems. 7. Supports three deployment options—Cloud as a Service, on-premises, and hybrid environments. 8. A large number of data visualization options, facilitating data exploration. 9. Multiple sharing options, including email, embedding, or exporting as a PDF. 10. Ability to set the minimum time between scheduled reports. ### Cons 1. Limited abilities for interaction with visualizations. 2. Performance issues when working with large data sets. 3. Creation of reports can be a complex process. ### Pricing Starts at $10.60 per user per month. ### G2 Rating 4.0 ---

7. Power BI

[Power BI](https://www.microsoft.com/en-us/power-platform/products/power-bi) is a business analytics tool from Microsoft that enables data visualization, reporting, and insights through interactive dashboards. ![Power BI - Alternative to Qlik Sense](/images/blogs/6-best-metabase-alternatives-in-2025/images/power-bi-homepage.png) ### Key features 1. Power BI compresses data to enable insight extraction from large datasets. 2. Embedding of reports for sharing across Teams, PowerPoint, Excel, and Power Platform. 3. The Power BI mobile app that enables on-the-go data viewing and sharing. 4. A library of custom data visualizations. 5. Customizable and shareable dashboards. 6. Ability to search data using queries in natural language. 7. DAX Data Analysis Function: Built-in codes to analyze and process data. 8. Content packs: A collection of dashboards, data models, and datasets, providing ready-to-use elements without the need for separate searches for each. 9. API for integration. 10. A large suite of data visualization tools. ### Cons 1. For those lacking technical expertise, the learning curve is especially steep. 2. Limited data import options, primarily supporting Microsoft products. 3. Performance slows down with large datasets. ### Pricing Starts at $10 per user per month. ### G2 Rating 4.5 --- ## Conclusion One of the above-mentioned Qlik Sense alternatives can offer your businesses more flexibility, customization, and performance based on your unique business needs. Whether you require enhanced integrations, faster processing, or a more user-friendly interface, there are several powerful options available. Choose the right alternative to ensure more efficient data analysis and better decision-making for your organization. --- ## FAQs ### 1. What is similar to Qlik Sense? Several business intelligence tools provide similar functionalities to Qlik Sense while overcoming its limitations. One standout option is Draxlr, a robust business intelligence platform that enables natural language query generation, Slack and email data alerts, raw SQL query execution, customizable visualizations, and intuitive dashboard creation without extensive coding. Additionally, it allows users to request custom features, enhancing flexibility and usability. ### 2. What are the disadvantages of Qlik Sense? The setup process of Qlik Sense is complex and often requires IT support, making it less practical for self-service analytics. Additionally, the steep learning curve presents a challenge for users, requiring significant time and effort to become proficient. Another limitation is the lack of automatic data updates, which forces users to manually update datasets, adding to the overall time consumption. These factors, along with the high pricing, collectively impact Qlik Sense's efficiency and ease of use. ### 3. Is Qlik Sense low code? Qlik Sense is a low-code tool, but its steep learning curve has made it challenging for users to perform tasks efficiently. A more user-friendly alternative is Draxlr, a business intelligence platform that provides a low-code environment with an intuitive interface and a gentler learning curve. Business owners can leverage Draxlr to generate insights effortlessly, create custom dashboards without extensive technical expertise, automate data analysis, and receive real-time alerts. With its ease of use and powerful features, Draxlr enhances decision-making and streamlines data-driven operations.

Supabase Data Visualization with Draxlr

hello@draxlr.com (Draxlr) — Thu, 20 Feb 2025 00:00:00 GMT

Data visualization is a crucial component of modern data analysis. It helps users: - 📈 Interpret large datasets quickly - 🔍 Identify patterns and trends - 🧠 Make informed decisions - 🎨 Transform raw data into visually engaging charts and graphs - 📑 Improve reporting and enhance communication - 🚀 Drive strategic growth through data insights - ⚠️ Detect anomalies in data - ✅ Ensure data-driven decision-making across teams Supabase is a powerful backend-as-a-service platform that enables developers to manage databases effortlessly. However, making sense of the data requires strong visualization tools. This is where **Draxlr** comes in, providing seamless integration with Supabase for intuitive data visualization. --- ## 3 Ways to Build SQL Queries for Visualization in Draxlr Before creating visualizations, you need to generate the desired dataset by constructing SQL queries. Draxlr offers 3 distinct ways to create SQL queries, catering to users with different levels of SQL expertise: ### 1. Query Builder For those who prefer a no-code approach, Draxlr’s query builder allows users to create queries using a visual interface. This method is perfect for users who may not be familiar with SQL but still want to generate meaningful insights. Draxlr Query Builder offers **5 options in Query Builder**: - 🔎 **Filter** - Filter Supabase table data by value of a specific field. - 🔗 **Join** - To combine multiple Supabase tables to analyze data from them. - 🔄 **Sort** - Order Supabase table data by a field value. - 📊 **Aggregate** - **Group by** - To group data by a field, or formatted field data. - **Function** - Apply functions like min, max, sum, average, count, and distinct count. - 🎯 **Select** - Specify which fields to show in the result. You can apply the above options in steps, each step is applied to the result of the previous step. You can create complex queries using it, and even convert the query to raw SQL query to add further customizations. ![Supabase Data Visualization with Draxlr | Query Builder](/images/blogs/supabase-data-visualization-with-draxlr/query-builder.png) ### 2. Raw SQL For users who are comfortable writing SQL, Draxlr provides an option to enter raw SQL queries. You can incorporate variables into your raw SQL query, enabling dynamic customization and reusability of the query to generate custom results efficiently. ![Supabase Data Visualization with Draxlr | Query Builder](/images/blogs/supabase-data-visualization-with-draxlr/raw-query.png) ### 3. AI-Powered Query Generation Draxlr leverages AI to help users generate SQL queries effortlessly. Users can describe what they need in plain language, and the AI translates it into SQL, eliminating the need to write repetitive queries from scratch. This significantly saves time for tech users by automating the generation of base SQL queries, allowing them to focus on refining complex logic rather than spending time on basic query structuring. ![Supabase Data Visualization with Draxlr | Query Builder](/images/blogs/supabase-data-visualization-with-draxlr/ai-query.png) --- ## How to visualize your Supabase data? Once you have the SQL query, you can now **choose a visualization for your data**. Draxlr offers multiple **chart types**, including: - **Bar Chart** ![Supabase Data Visualization with Draxlr | Bar Chart](/images/blogs/supabase-data-visualization-with-draxlr/bar.png) - **Pie Chart** ![Supabase Data Visualization with Draxlr | Pie Chart](/images/blogs/supabase-data-visualization-with-draxlr/pie.png) - **Area Chart** ![Supabase Data Visualization with Draxlr | Area Chart](/images/blogs/supabase-data-visualization-with-draxlr/area.png) - **Funnel Chart** ![Supabase Data Visualization with Draxlr | Funnel Chart](/images/blogs/supabase-data-visualization-with-draxlr/funnel.png) - **Sankey Chart** ![Supabase Data Visualization with Draxlr | Sankey Chart](/images/blogs/supabase-data-visualization-with-draxlr/sankey.png) - **TreeMap Chart** ![Supabase Data Visualization with Draxlr | TreeMap Chart](/images/blogs/supabase-data-visualization-with-draxlr/treemap.png) - and more Each chart type comes with extensive customization options, allowing users to personalize their visualizations to match their data needs and presentation preferences. Users can modify colors, labels, axes, and other elements to enhance clarity. --- ## Adding Visualizations to Dashboards Once a query is built and visualized, Draxlr allows users to add these visualizations to dashboards. This feature helps consolidate multiple insights in one place, making it easier to monitor and analyze data trends. Some key features of Draxlr Dashboards: - Ability to **add filters** on dashboards to refine data views dynamically - **Drill-down** on graphs to explore detailed insights - **Embed dashboards** into web applications or external platforms - **Real-time dashboard** refresh to display live data on screens or TV monitors - **Secured dashboard sharing** with password protection - Automated **email summaries** of dashboards, sent periodically to stakeholders - **Customizable layouts** to arrange visualizations for better clarity - **User access controls** to define permissions for viewing and editing dashboards Connect your Supabase Database --- ## Setting Alerts To stay informed about **critical data changes** on your visualizations, Draxlr allows users to set **Slack or Email alerts**. You can also sync the visualization result into a **Google Sheets**, or access it via an **API**. --- ## Frequently Asked Questions (FAQs) 1. **Does Supabase have analytics?** Supabase does not provide built-in analytics, but you can use third-party tools like Draxlr to visualize and analyze your Supabase data effectively. 2. **Which software is best for data visualization?** The best software depends on your needs. Draxlr is an excellent choice for Supabase users due to its seamless integration, SQL query builder, and advanced visualization capabilities. 3. **What is the AI tool for data visualization?** Draxlr provides AI-powered query generation, enabling users to generate SQL queries effortlessly, making data visualization faster and more accessible without manual SQL coding. --- ## Conclusion Draxlr’s capabilities make it an excellent choice for Supabase users looking to enhance their data visualization and monitoring experience. Whether using query builder, raw SQL, or AI-generated queries, Draxlr provides a seamless way to turn Supabase data into actionable insights.

Supabase SQL Query Cheat Sheet

hello@draxlr.com (Draxlr) — Thu, 13 Feb 2025 00:00:00 GMT

Supabase is an open-source alternative to Firebase, offering a powerful Postgres-based database with built-in authentication, real-time capabilities, and storage. Whether you're a beginner or an experienced developer, mastering SQL queries is essential to harnessing the full potential of Supabase. This cheat sheet provides a quick reference for common SQL operations, including CRUD operations, joins, filtering, indexing, row-level security, triggers, JSON handling, and more. Keep this guide handy to efficiently manage your Supabase database and optimize performance.

Build Dashboards from Supabase Data

--- ## 1. **Basic CRUD Operations** ### **Create (Insert Data)** ```sql INSERT INTO users (id, name, email) VALUES (1, 'John', 'john@example.com'); ``` ### **Read (Retrieve Data)** ```sql SELECT * FROM users; SELECT name, email FROM users WHERE id = 1; ``` ### **Update Data** ```sql UPDATE users SET email = 'newemail@example.com' WHERE id = 1; ``` ### **Delete Data** ```sql DELETE FROM users WHERE id = 1; ``` --- ## 2. **Joins & Relationships** ### **Inner Join** ```sql SELECT users.name, orders.product_name FROM users INNER JOIN orders ON users.id = orders.user_id; ``` ### **Left Join** ```sql SELECT users.name, orders.product_name FROM users LEFT JOIN orders ON users.id = orders.user_id; ``` ### **Right Join** ```sql SELECT users.name, orders.product_name FROM users RIGHT JOIN orders ON users.id = orders.user_id; ``` --- ## 3. **Filtering & Sorting** ### **Filtering with WHERE Clause** ```sql SELECT * FROM users WHERE email LIKE '%@gmail.com'; ``` ### **Sorting Results** ```sql SELECT * FROM users ORDER BY name ASC; SELECT * FROM orders ORDER BY created_at DESC; ``` --- ## 4. **Aggregations & Grouping** ### **Counting Rows** ```sql SELECT COUNT(*) FROM users; ``` ### **Grouping Data** ```sql SELECT role, COUNT(*) FROM users GROUP BY role; ``` ### **Summing Data** ```sql SELECT SUM(price) FROM orders WHERE user_id = 1; ``` --- ## 5. **Indexes & Performance Optimization** ### **Creating an Index** ```sql CREATE INDEX idx_users_email ON users(email); ``` ### **Using EXPLAIN ANALYZE to Optimize Queries** ```sql EXPLAIN ANALYZE SELECT * FROM users WHERE email = 'john@example.com'; ``` --- ## 6. **Row Level Security (RLS)** ### **Enable RLS on a Table** ```sql ALTER TABLE users ENABLE ROW LEVEL SECURITY; ``` ### **Create RLS Policy for User-Specific Data Access** ```sql CREATE POLICY "user can view own data" ON users FOR SELECT USING (auth.uid() = id); ``` ### **Create RLS Policy with a Joined Table** ```sql CREATE POLICY "user can view their own orders" ON orders FOR SELECT USING ( auth.uid() = (SELECT user_id FROM users WHERE users.id = orders.user_id) ); ``` --- ## 7. **Trigger Functions** ### **Create a Trigger for Auto-updating Timestamps** ```sql CREATE OR REPLACE FUNCTION update_timestamp() RETURNS TRIGGER AS $$ BEGIN NEW.updated_at = NOW(); RETURN NEW; END; $$ LANGUAGE plpgsql; CREATE TRIGGER update_users_timestamp BEFORE UPDATE ON users FOR EACH ROW EXECUTE FUNCTION update_timestamp(); ``` --- ## 8. **Working with JSON Data** ### **Insert JSON Data** ```sql INSERT INTO logs (event_data) VALUES ('{"user": "John", "action": "login"}'); ``` ### **Query JSON Data** ```sql SELECT event_data->>'user' FROM logs; ``` --- ## 9. **Backup & Restore** ### **Backup a Table** ```sql COPY users TO '/path/to/backup/users_backup.csv' WITH CSV HEADER; ``` ### **Restore a Table** ```sql COPY users FROM '/path/to/backup/users_backup.csv' WITH CSV HEADER; ``` --- ## 10. **Advanced Queries for Supabase Users** ### **Using Row Number for Pagination** ```sql SELECT *, ROW_NUMBER() OVER (ORDER BY created_at DESC) AS row_num FROM users LIMIT 10; ``` ### **Fetching the Last Inserted Record** ```sql SELECT * FROM users ORDER BY id DESC LIMIT 1; ``` ### **Get Users Who Haven't Placed an Order** ```sql SELECT users.* FROM users LEFT JOIN orders ON users.id = orders.user_id WHERE orders.id IS NULL; ``` ---

Build Dashboards from Supabase Data

Supabase offers a scalable database, but effective management requires strong SQL skills. This cheat sheet is a quick reference, and for visualizing data, Draxlr can help. **Draxlr** is a no-code self-service BI tool that seamlessly integrates with Supabase, allowing you to [create dashboards](/blogs/how-to-build-dashboards-from-supabase-data/) and set alerts without writing complex SQL queries. If you want better insights and streamlined reporting, **try Draxlr** to get the most out of your Supabase data. Connect your Supabase Database Bookmark this cheat sheet for easy access to essential SQL queries!

How to set email alerts when your SQL data changes

hello@draxlr.com (Draxlr) — Tue, 04 Feb 2025 00:00:00 GMT

You don’t have time to refresh spreadsheets or check dashboards all day. But missing a key update - like a new sign-up from an enterprise-level company, running low on a top-selling product, or an alert on ICU occupancy exceeding a certain percentage - can cost your business. Instead of manual tracking changes, why not let the [updates come to you](https://www.draxlr.com/blogs/alerts-for-sql-data/)? With **automated email alerts**, you’ll be notified instantly when something important happens—without coding or IT support. In this guide, I’ll walk you through the easiest way to set up SQL email alerts using **Draxlr.** Let’s get started. 🚀 --- ## How to set up SQL email alerts in Draxlr (Step-by-Step) Manually tracking SQL data is a time sink. Draxlr lets you set up **real-time SQL email alerts** so you’re always informed—without logging into dashboards or running queries. Here’s how to do it in four simple steps: Connect your Database ### 🛠 Step 1: Connect your data source to Draxlr For Draxlr to send you an alert, you need to first connect it to your SQL database. Here’s how to do: 🔹 Log in or sign up for [Draxlr](https://app.draxlr.com/register/) to get started. 🔹 Choose your SQL database from the available options. You can also connect Google Sheets or Airtable with Draxlr. ![Select SQL data source.png](/images/blogs/how-to-set-email-alerts-when-your-sql-data-changes/images/Select_SQL_data_source.png) 🔹 To establish a secure connection between Draxlr and your database, you need to add your database credentials. ![Add database credentails.png](/images/blogs/how-to-set-email-alerts-when-your-sql-data-changes/images/Add_database_credentails.png) If you’re unsure about any field, refer to your **database hosting provider** or **server settings.** The credentials needed are: - **Host** – The address where your database is hosted, such as an IP address (192.168.1.1) or a domain (database.example.com). - **Port** – The communication endpoint for your database. The default ports are 3306 for MySQL, 5432 for PostgreSQL, and 1433 for SQL Server. - **User** – The username required to access the database. - **Password** – The corresponding password for the database user. - **Database name** – The specific database to which Draxlr should connect. If you are using an **SSH Tunnel**, you will also need: - **SSH Host** – The server that acts as a bridge to connect securely to your database. - **SSH Port** – The port for SSH access, typically 22. - **SSH User** – The username for authentication on the SSH server. - **Key File** – If your SSH connection uses key-based authentication, upload your **private key file** (e.g., .pem or .ppk). - **SSH Key Phrase** - If your SSH key file is encrypted with a passphrase, enter it here to unlock the key.
📌 **Note:** The SSH Key Phrase is different from a password—it is used to decrypt a private key that has been secured with an additional layer of encryption. Once you’ve entered these details, Draxlr will establish a **secure connection** and start syncing your database in real time. 🔹 Draxlr will now sync your database, ensuring that your data is always up to date. ![Sync database.png](/images/blogs/how-to-set-email-alerts-when-your-sql-data-changes/images/Sync_database.png) ### 📌 Step 2: Choose what data you want to track Now that Draxlr is connected to your SQL database, decide what data is critical for your business. Do you want to know when: 1. An enterprise-level company installs your app? 2. Sales drop below a certain level? 3. A large payment comes in? 4. A customer order gets delayed? **Draxlr** allows you to track any business-critical data point from your SQL database and configure real-time email and [Slack](https://www.draxlr.com/blogs/how-to-set-slack-alerts-when-your-business-data-changes/) alerts for immediate action. To demonstrate this, let’s take the example of [Sup Bot](https://www.supbot.com/), a Slack and Microsoft Teams integration for [PTO tracking](https://www.supbot.com/blogs/how-to-use-vacation-tracker-in-slack) and async meetings. Sup Bot uses Draxlr to monitor leave statistics and has set up an SQL email alert to notify them when an enterprise-level company installs their app. To define the data you want to track, Draxlr first requires you to create and save a query or metric. You can do this in three ways, depending on your preference: - **Query Builder (No-Code Approach)** – Select tables, apply filters, and set conditions with a simple click-based interface—no SQL knowledge required. - **AI-Powered Query Generation** – Describe what you need, and Draxlr’s AI will automatically generate the SQL query for you. - **SQL Raw Query Editor (For Technical Users)** – If you prefer full control, write and execute custom SQL queries directly in Draxlr. Once your query is created and saved, you can use it to monitor data changes and trigger alerts when predefined conditions are met. ### 📊 Step 3: Set up an SQL email alert Now that your query is set up, it’s time to configure the SQL email alert in Draxlr. Follow these steps to ensure you receive timely notifications: 1️⃣ **Add Email recipients:** Enter the email addresses of the recipients who should receive the alert. You can add one or multiple recipients as needed. ![Add email.png](/images/blogs/how-to-set-email-alerts-when-your-sql-data-changes/images/Add_email.png) 2️⃣ **Define alert details:** ![integration details.png](/images/blogs/how-to-set-email-alerts-when-your-sql-data-changes/images/integration_details.png) - **Alert name:** Assign a name to your alert for easy identification. - **Select data query:** Choose the query you created in the previous step to define what this alert will monitor. 3️⃣ **Set the trigger conditions:** Decide when the alert should be sent. ![trigger conditions.png](/images/blogs/how-to-set-email-alerts-when-your-sql-data-changes/images/trigger_conditions.png) - **Specific time trigger:** Choose a day of the week (Monday–Sunday) and set the exact hour and minute for the alert. - **Periodic alerts:** Schedule the alert to be sent at fixed intervals, such as every hour, every two hours, etc. 4️⃣ **Enable data change notifications (Optional)** – If you want to receive alerts only when there is a change in your data and do not send any alert when there is no change, enable the **Data Changes** toggle. This prevents unnecessary notifications when the data remains unchanged. ![data change notifications.png](/images/blogs/how-to-set-email-alerts-when-your-sql-data-changes/images/data_change_notifications.png) You can further refine how alerts are triggered based on data changes: - **Threshold alert (Optional)** – Get notified when your data reaches a predefined threshold (e.g., sales drop below USD 50k). - **Relative change (Optional)** – Set alerts based on percentage changes across a given value (e.g., notify if revenue decreases by 15% compared to the previous period). - **Specific value (Optional)** – Receive an alert when your data matches an exact value (e.g., when stock levels hit zero). Once configured, your real-time SQL email alerts will be automatically sent based on the conditions you set, ensuring you stay informed without manually checking reports. 🚀 ### 📩 Step 4: What happens next? Once your email alert is configured, Draxlr will automatically send a snapshot of your data to the specified email recipients whenever the set conditions are met. ![Screenshot 2025-02-03 at 8.42.45 PM.png](/images/blogs/how-to-set-email-alerts-when-your-sql-data-changes/images/email_alert.png) **What the Email Includes:** ✔ A snapshot of your data presented as a graph, table, or computed value, depending on the query you selected. ✔ Data insights formatted for quick understanding. ✔ The alert is triggered only when your conditions are met, ensuring you receive actionable updates instead of unnecessary notifications. With Draxlr, your **data comes to you**—so you can focus on decisions, not data tracking. --- ## Best practices for effective SQL email alerts Setting up email alerts in Draxlr ensures that you receive real-time insights without manually checking reports. However, to make the most of this feature, it's important to configure alerts effectively. Here are some best practices to ensure that your alerts are both actionable and meaningful: ### 1. Set alerts for critical data, not every minor change 🔹 Avoid setting alerts for every small fluctuation in your data, as this can lead to alert fatigue. Focus on key business metrics that **require immediate action**, such as: - Sales dropping below a threshold. - Large transactions above a specified value. - A sudden spike in website traffic. ### 2. Ensure alerts reach the right people 🔹 Assign alerts to the relevant teams to ensure timely action. Avoid overwhelming individuals with alerts that are not relevant to their role. 📍 **Who should get what?** - ✔ **Finance team** – Alerts for large transactions, overdue invoices, or budget thresholds. - ✔ **Sales Team** – Notifications when high-value customers sign up or a key deal is lost. ✅ **Example:** Instead of sending all alerts to the CEO, assign low-stock alerts to inventory managers and payment failure alerts to finance teams. ### 3. Test your SQL email alerts before activating them 🔹 Always run a test to ensure that your alert is correctly configured and delivers the right data. Check for accuracy in recipients, data values, and frequency settings. ### 4. Regularly review & optimize your alerts 🔹 Periodically review your alerts to ensure they are still relevant to your business needs. 🔹 If you notice too many or too few alerts, adjust the triggers, recipients, or frequency accordingly. --- ## Conclusion Setting up automated SQL email alerts for SQL data changes may seem complex, but it’s a valuable step toward improving operational efficiency. Well-configured alerts ensure key stakeholders stay informed, streamline decision-making, and help you respond to critical data shifts in real time. When configured effectively, these alerts become a powerful tool that enables proactive data-driven decisions and minimizes business risks. Use Draxlr to automate your SQL alerts and stay ahead of important data changes effortlessly. Get started today and take control of your business insights. --- ## Frequently asked questions ### 1. How to send an alert based on SQL query results? You can send an alert based on SQL query results using BI tools like **Draxlr**, follow these steps: 1. Link Draxlr to your SQL database (MySQL, PostgreSQL, SQL Server, etc.). 2. Use the query builder, AI-assisted query generation, or write a custom SQL query to define the data you want to track. 3. Choose when the alert should trigger - Threshold-based alerts, Relative change alerts, Specific value alerts. 4. Add email addresses for those who need the alert and select how often it should be sent (instant, daily, or periodic). 5. Run a test email to verify the setup, then activate it. ### 2. Why are SQL data alerts important? SQL data alerts are important because they provide real-time notifications when critical data changes, allowing businesses to respond quickly without manually monitoring reports. By automating alerts based on predefined conditions, businesses can prevent missed opportunities, reduce risks, and streamline workflows, ensuring that data-driven decisions are made proactively rather than reactively. ### 3. How do I track SQL database changes? To track SQL database changes, you can use automated monitoring tools or custom queries that detect and notify you of data updates. One efficient way is to set up SQL data alerts in a BI tool like Draxlr, which continuously monitors your database and triggers notifications when specific conditions are met. ### 4. How can I set up threshold alerts in SQL? Setting up threshold alerts in SQL allows you to receive real-time notifications when your data exceeds or falls below predefined limits. A straightforward way to achieve this is by using Draxlr, which offers a no-code solution for setting up SQL threshold alerts. With Draxlr, you can easily define conditions, monitor key metrics, and get automated email notifications whenever critical data changes. ### 5. Can I get email alerts for real-time SQL data changes? Yes, you can set up email alerts for real-time SQL data changes using monitoring tools like Draxlr. Draxlr continuously tracks your SQL database and triggers automated notifications when specified conditions are met.

Business Intelligence tools for SaaS Owners or Companies

hello@draxlr.com (Draxlr) — Sat, 01 Feb 2025 00:00:00 GMT

Running a SaaS business means managing vast amounts of data, from user behavior and subscription metrics to financial performance and customer support insights. Without a structured approach to analyzing this data, SaaS owners may find themselves struggling with inefficient decision-making and missed opportunities. Lack of real-time analytics can lead to slow responses to customer needs and market shifts. Without proper insights, businesses may fail to optimize pricing strategies, improve user engagement, or effectively forecast revenue. This is why implementing a **business intelligence (BI) tool** is essential for SaaS owners seeking to scale and improve their operations. --- ## Common Problems SaaS Owners Face Without BI Tools 1. **Dependency on Developers for Data Requests** Without a BI tool, SaaS owners and decision-makers frequently rely on developers to extract crucial data, leading to delays and inefficiencies. 2. **Lack of Real-Time Alerts on Critical Data Changes** SaaS businesses often miss sudden shifts in customer behavior, revenue fluctuations, or system performance issues due to a lack of automated alerts. 3. **Difficulty in Identifying Growth Opportunities** Without a structured BI tool, recognizing trends, customer preferences, and revenue-boosting opportunities becomes challenging, leading to slow decision-making and potential losses. --- ## How Business intelligence (BI) tools empower SaaS businesses? - Identify churn patterns and take proactive steps to reduce customer attrition. - Optimize pricing strategies based on usage and customer trends. - Improve customer experience by tracking feature engagement. - Forecast revenue and financial projections with greater accuracy. - Build SaaS dashboards that provide real-time insights for data-driven decision-making. --- ## Key features to look for in BI tools for SaaS companies - **Self-service BI**: It allows non-technical team members in SaaS companies to independently access, analyze, and visualize data without needing specialized knowledge or technical skills. - **Real-time monitoring of KPIs**: Key Performance Indicators (KPIs) such as churn rate, Monthly Recurring Revenue (MRR), and other crucial metrics must be monitored in real-time to track business performance and make timely, data-driven decisions. - **A unified dashboard**: BI tools integrate data from multiple databases into a single dashboard. This enables SaaS companies to analyze cross-functional data cohesively and identify patterns across departments. - **Alerts**: Data change alerts help SaaS companies keep track of data modifications and stay informed. - **API support**: This enables seamless integration with other systems, automates data flow, and allows real-time updates. - **Pricing model for growing teams**: Flexible and flat subscription plans offering tiered pricing, designed to scale with SaaS teams. --- ## Top Business Intelligence Tools for SaaS Owners Several BI tools can help SaaS owners turn raw data into actionable insights. Here are some of the leading options:

BI Tool	Best for	Pricing
Draxlr	Small and mid-sized companies	Starts at flat $75 per month
Tableau	Enterprise-scale businesses	Price on request
Power BI	Enterprise-scale businesses	Starts at $10 per user per month
Looker	Enterprise-scale businesses	Price on request
Sisense	Enterprise-scale businesses	Price on request
Oracle Analytics Cloud	Enterprise-scale businesses	Starts at $16 per user per month
Domo	Mid-sized to large companies	Price on request
Qlik Sense	Enterprise-scale businesses	Starts at flat $825 per month

---

1. Draxlr

![BI tools for SaaS Owners - Draxlr](/images/blogs/_tools/draxlr.png) [Draxlr](https://www.draxlr.com/) is a powerful data analytics platform that turns raw data into valuable insights. It provides user-friendly visualization tools and analytics to streamline data exploration and reporting. #### Key features 1. Supports various SQL Databases, Excel, and Airtable. You can also request to build custom connectors. 2. No-code Query builder. 3. [Generation of SQL queries using Draxlr AI](/blogs/how-to-generate-sql-query-with-AI/). 4. Ability to write raw queries using an SQL editor with formatting tools to create and improve complex queries easily. 5. Large variety of graphs and charts. 6. Drill-down feature on graph. 7. Get dashboard snapshot via email. 8. [Slack and email alerts](/features/alerts-and-automations/) on query data changes. 9. Version history of queries to review and track changes made to queries over time and ensure transparency. 10. [Embed Dashboard](/embedded-analytics-tool/) into your SaaS application. 11. Share Dashboard with your stakeholders without the need to login. 12. Get Query result via API. 13. Export Query result to Google Sheets. 14. Easy-to-use. 15. Excellent customer support. Connect your Database to Draxlr #### Pricing ![BI tools for SaaS Owners - Draxlr pricing](/images/blogs/business-intelligence-tools-for-saas-owners/draxlr-pricing.png) #### Best for Small and mid-sized SaaS companies that require customizations. ---

2. Tableau

[Tableau](https://www.tableau.com/) is a suitable data visualization tool for both technical and non-technical users. ![BI tools for SaaS Owners - Tableau](/images/blogs/business-intelligence-tools-for-saas-owners/tableau.png) #### Key features 1. Ready-to-use dashboards that can be customized with your own data sets to obtain personalized insights faster. 2. Daily reminders on mobile to stay informed of business metrics. 3. Data stories that automatically display insights in natural language for easier understanding of business owners. 4. Hyper—SQL engine to speed up data processing and reduce load times. 5. Table Viz Extensions for users who prefer traditional tabular reporting views to examine data. 6. Insights in the regional language on the web and mobile reduce the chances of misunderstanding arising from language barriers. 7. 130+ different currency symbols for a personalized and clearer understanding of metrics. 8. Ability to set goals manually for a specific metric. 9. Effortless data source organization and cataloging for easier searching and management. 10. AI to assist with data analysis by allowing queries in plain English. #### Cons 1. Limited chart options for data visualization. 2. Longer load times for larger volumes of data. 3. Reports lose their properties when exported into other data formats outside of Tableau. #### Pricing The sales team must be contacted for pricing details. #### Best for Large-scale SaaS businesses. ---

3. Power BI

![BI tools for SaaS Owners - Power BI](/images/blogs/business-intelligence-tools-for-saas-owners/powerbi.png) [Microsoft Power BI](https://www.microsoft.com/en-us/power-platform/products/power-bi) is a data visualization tool with an easy-to-use interface for exploring data. #### Key features 1. Easy embedding and sharing of reports on MS Teams, PowerPoint, and other Microsoft services. 2. The Reset feature updates the embedded report on PowerPoint with the current data for up-to-date insights and metrics. 3. Custom data visualizations using open-source data-viz libraries from R and Python. 4. Suggested questions from Copilot to help explore the available data by searching existing visuals or creating new ones. 5. Autoscale: Automatic addition of computing resources when capacity reaches its limit. 6. Greater control over security and access with the ability to use one’s own encryption keys to encrypt data. 7. Automated scheduled refreshing of datasets, eliminating the need to manually update them. 8. Power BI mobile app that is operational on iOS, Android, and Windows devices, allowing access to reports and dashboards on the go. 9. Power Query for easy data import and transformation from multiple sources with little to no coding required. 10. APIs for integration. #### Cons 1. Steep learning curve. 2. Data import options are mainly from Microsoft products. 3. Slow performance when working with large datasets. #### Pricing Starts at $10 per user per month. #### Best for Large-scale SaaS businesses. ---

4. Looker

[Looker](https://lookerstudio.google.com/navigation/reporting) is an online, cloud-based business intelligence tool that allows users to explore, analyze, and share real-time business data. ![BI tools for SaaS Owners - Looker](/images/blogs/business-intelligence-tools-for-saas-owners/looker.jpg) #### Key features 1. Access to a large number of data sources and connectors. 2. Direct connection to raw data, eliminating the need to create an ETL layer. 3. LLM integration in data products for innovations with generative AI. 4. API for faster building of embedded analytics applications. 5. Full Git integration for easy collaboration and continuous deployment of data products. 6. Direct query architecture for insights in real-time. 7. Looker API for user authentication and automation of secure data access. 8. Ability to set up row-level and column-level permissions. 9. Pre-built sample reports to customize. 10. Conditional formatting for enhancing a data application's appearance by adjusting colors and fonts. #### Cons 1. It is unable to handle complex data transformations since it depends largely on data that has been pre-processed. 2. Cannot merge more than five tables or datasets into a single view. 3. It has a steep learning curve with the initial setup requiring technical expertise. #### Pricing The sales team must be contacted to request custom pricing. #### Best for Large-scale SaaS businesses. ---

5. Sisense

[Sisense](https://www.sisense.com/) is a business intelligence platform that helps organizations turn complex data into useful insights. ![BI tools for SaaS Owners - Sisense](/images/blogs/business-intelligence-tools-for-saas-owners/sisense.png) #### Key features 1. AI assistant that suggests insights and creates data visualizations. 2. Sisense bots that answer queries with insightful information to drive data-driven business decisions. 3. Offers three types of connection—Elasticube to import and merge data from multiple sources, Build to Destination for a cloud data warehouse, and Live for direct connection to the data source. 4. Ability to join multiple data tables. 5. Custom code that allows users to execute Python scripts or SQL queries to clean or transform data. 6. Supports multiple chart types for data visualization. 7. Offers a number of sample dashboards. 8. Four user roles to manage and execute permissions. 9. Receipt of required data insights within chat platforms. 10. White labeling to replace the Sisense logo and name in emails and Sisense deployments. #### Cons 1. Lack of basic formatting and design elements. 2. A steep learning curve with an absence of clear documentation for advanced setups. 3. Frequent crashing of data models. #### Pricing The Sisense team must be contacted for tailored pricing solutions. #### Best for Large-scale SaaS businesses. ---

6. Oracle Analytics Cloud

[Oracle Analytics Cloud](https://www.oracle.com/in/business-analytics/analytics-platform/) is a cloud-based platform that helps businesses explore, analyze, and visualize data in real time. ![BI tools for SaaS Owners - Oracle Analytics Cloud](/images/blogs/business-intelligence-tools-for-saas-owners/oracle.png) #### Key features 1. Ability to consolidate data from multiple sources—on-premises, in the cloud, or in hybrid environments—for seamless analysis across different platforms. 2. Data flows for code-free transformation of data sets into information required for analytics. 3. A diverse set of visualization and reporting capabilities, including pre-built templates for customization. 4. Integrated machine-learning models to enable predictive analytics for trend forecasting. 5. The multi-layered design supports complex and large-scale data processing. 6. Natural language processing to query data and retrieve information in 28 languages. 7. The Oracle Mobile app analyzes individual patterns and data interests to suggest new analyses or data to explore. 8. Autogenerated HTML code can be copied to embed data visualizations into other web applications. 9. Oracle Analytics AI Assistant to implement the required changes through natural language. 10. Recommends the best type of chart or graph to get useful insights from the data. #### Cons 1. A steep learning curve. 2. Limited features for dashboards. 3. The data needs to be organized in a structured format to be analyzed. #### Pricing Starts at $16 per user per month. #### Best for Large-scale SaaS businesses. ---

7. Domo

[Domo](https://www.domo.com/) is a business intelligence tool with a comprehensive suite of tools for data integration, visualization, and analytics. ![BI tools for SaaS Owners - Domo](/images/blogs/business-intelligence-tools-for-saas-owners/domo-homepage.png) #### Key features 1. A large pool of pre-built connectors to connect to data. 2. Domo’s live cache layer to speed up data queries. 3. MySQL or Redshift expressions in Domo to set up data pipelines. 4. Ability to access data on iOS and Android Domo mobile apps. 5. Filters at both the individual chart level and across the entire dashboard. 6. Text bots to answer queries in natural language. 7. Buzz: Built-in chat app for data collaboration. 8. Easy customization of colors, logos, and visualization elements. 9. Ability to edit the shared data visualizations by the end user. 10. Ready-to-use code templates to create data visualizations. #### Cons 1. Limited customization of reports. 2. Lack of variety in themes for dashboards. 3. Frequent break of data view and data connections. #### Pricing The sales team must be contacted to request a quote. #### Best for Mid-size to large-scale SaaS businesses. ---

8. Qlik Sense

[Qlik Sense](https://www.qlik.com/us) is a business intelligence platform that enables users to explore and visualize data with ease. ![BI tools for SaaS Owners - Qlik Sense](/images/blogs/business-intelligence-tools-for-saas-owners/qliksense-dashboard.png) #### Key features 1. Custom color options for column items. 2. AutoML to build ML models and predictions easily without coding. 3. Ability to create and examine analytics on any device. 4. An associative data model that allows easy and flexible data exploration to reveal hidden patterns and connections in the data. 5. Insight advisor that uses machine learning algorithms to examine trends in data and recommend data visualizations. 6. QIX Engine: A fast in-memory engine built for quick data exploration and analysis. 7. Embedded analytics features facilitate the smooth incorporation of analytics into operational applications and websites. 8. Mobile app for data analysis and exploration from anywhere. 9. Supports 40+ languages. 10. Natural language search for easy exploration of data. #### Cons 1. Steep learning curve. 2. Lack of automated data update functionality. 3. Longer load time. #### Pricing Starts at $825 per month. #### Best for Medium to large-sized SaaS businesses. --- ## Conclusion Business intelligence tools are essential for SaaS companies to harness the power of their data. By leveraging advanced analytics, real-time insights, and intuitive reporting, these tools help SaaS businesses optimize performance, make informed decisions, and stay competitive. Choosing the right BI tool tailored to your company’s needs can significantly drive growth, efficiency, and success in the fast-paced SaaS industry. --- ## FAQs **1. What is SaaS BI?** SaaS BI (Business Intelligence) refers to data analytics and visualization tools specifically designed for SaaS businesses to track key metrics, analyze performance, and gain actionable insights for growth. **2. Why do SaaS businesses need BI tools?** SaaS businesses need BI tools to monitor user behavior, track revenue trends, reduce churn, optimize pricing, and improve decision-making through data-driven insights. **3. How do BI tools help with churn reduction?** BI tools help identify churn patterns by analyzing customer engagement, product usage, and feedback, enabling SaaS owners to take proactive measures to retain users. **4. What makes Draxlr a good choice for SaaS businesses?** Draxlr provides an easy-to-use interface, real-time KPI monitoring, automated reporting, and seamless integration with multiple data sources, making it ideal for SaaS owners. **5. How does Power BI compare to Tableau for SaaS analytics?** Power BI offers deep integration with Microsoft products and AI-driven insights, while Tableau excels in interactive dashboards and visualization. The choice depends on business needs and existing tech stacks. **6. Can small SaaS businesses benefit from BI tools?** Yes, even small SaaS businesses can leverage BI tools to gain valuable insights, optimize operations, and scale effectively based on data-driven decisions. **7. What is a business intelligence tool?** A Business Intelligence (BI) tool is a specialized software application designed to help organizations collect, process, analyze, and visualize business data. These tools enable companies to transform raw data from various sources—such as databases, spreadsheets, and cloud applications—into actionable insights. By using BI tools, businesses can identify trends, monitor key performance indicators (KPIs), and make informed, data-driven decisions. **8. Is coding required for business intelligence?** It is not required, you can use AI or Query Builder feature in BI tools to generate insights from your SaaS data. **9. Which business intelligence tool is the best for small to mid-sized SaaS company?** Draxlr is a good choice for small to mid-sized SaaS companies as it has less learning curve, is quite affordable, and has great customer support.

Top 5 Redash Alternatives for Smarter Data Visualization and Analysis

hello@draxlr.com (Draxlr) — Mon, 27 Jan 2025 00:00:00 GMT

Finding the right data visualization tool is essential for turning data into insights. While Redash is a popular choice, it might not meet everyone’s needs. In this blog, we’ll explore top Redash alternatives that offer powerful features, better usability, and more flexibility to fit your requirements. 1. [**Draxlr**](#draxlr): Draxlr is a powerful data visualization and analytics tool with AI functionalities designed to simplify businesses' data interactions. It allows users to connect databases, run queries, and create stunning dashboards without extensive coding knowledge. 2. [**Looker**](#looker): Looker is a modern business intelligence platform that offers customizable dashboards, real-time data exploration, and robust integration with tools like BigQuery and Salesforce for tailored actionable insights. 3. [**Tableau**](#tableau): Tableau is a leading visual analytics platform that transforms data into actionable insights through interactive dashboards, AI-driven features, and seamless integration with various data sources. 4. [**Power BI**](#power-bi): Power BI is a dynamic analytics platform that integrates data visualization, AI-driven insights, and seamless collaboration to transform raw data into actionable intelligence. 5. [**Datapad**](#datapad): Datapad is a lightweight, mobile-first business dashboard that offers real-time performance tracking, team collaboration, and customizable analytics to empower data-driven decisions on the go. ---

1. Draxlr

[Draxlr](https://app.draxlr.com/) is a data analytics platform that transforms raw data into actionable insights. It offers intuitive visualization tools and analytics to simplify data exploration and reporting. Connect your Database ![Redash alternative - Draxlr](/images/blogs/_tools/draxlr.png) ### Key Features 1. Connects with multiple databases to streamline data integration. 2. Query builder to manage data by filtering, summarization, grouping, and joining without code. 3. [Generation of SQL queries](/blogs/how-to-generate-sql-query-with-AI/) via prompts using AI. 4. Draxlr AI: Natural language input for creating dashboards with insights. 5. Accurate correction of fed SQL queries using AI. 6. Intuitive visualization of data using a large suite of graphs and charts. 7. Drill-down feature to dive deeper into charts or graphs and zoom further into available data. 8. Auto-generation of insights from tables without writing SQL. 9. Creation of own dashboards with graphs and tables for data visualization. 10. Embed dashboards to run them on websites. 11. [Slack alerts](/blogs/how-to-set-slack-alerts-when-your-business-data-changes/) for a specific time, periodically, or only when data changes occur. 12. [Email notifications](/features/alerts-and-automations/) when there is a change in business data, ensuring you stay informed in real-time. 13. Sharing dashboard items with the help of public URLs that can be securely accessed via a PIN. 14. Live mode to review updated data in intervals of 10 minutes and broadcast dashboards to TV. 15. Integrates with Slack, Google Sheets, email, and API. 16. Gentle learning curve. 17. Exceptional customer support. ### Pricing ![Redash alternative - Draxlr Pricing](/images/blogs/5-redash-alternatives/draxlr-pricing.png) ---

2. Looker

[Looker](https://lookerstudio.google.com/) is a cloud-based platform that does not require downloading any software. ![Redash alternative - Looker](/images/blogs/5-redash-alternatives/looker.jpg) ### Key Features 1. Connects to a wide range of data sources and performs analysis without the Extract-Transform-Load layer. 2. Embedded analytics for dashboards in apps. 3. API-first data tool that enables users to seamlessly integrate, manage, and analyze their data across various platforms. 4. Ability to schedule and send dashboards by email, webhook, Amazon S3 Bucket, or SFTP server. 5. Ready sample reports to build on. 6. Interactive reports with viewer and date filters. 7. Configuration of row-level and column-level security permissions. 8. Git Integrated IDE with version control: Easy team collaboration for working together on developing a data app. 9. Reports will be delivered only if specific conditions are met. 10. Conditional formatting for color and font formatting needs. ### Pricing Tailored pricing solutions. ---

3. Tableau

[Tableau](https://www.tableau.com/) is a popular data visualization tool with a friendly user interface. ![Redash alternative - Tableau](/images/blogs/5-redash-alternatives/tableau.png) ### Key Features 1. Integrates with a large number of tools. 2. Different levels of access are assigned to users based on their roles within the organization, ensuring sensitive data security. 3. Data encryption for protection against unauthorized entry and possible security violations. 4. The Tableau Cloud Manager enables the creation and management of multiple Tableau sites across preferred regions. 5. Tableau Einstein: A powerful analytics platform built on Salesforce, designed to work seamlessly with Agentforce, a set of customizable AI tools. 6. Table Vix Extension: Incorporates detailed tables and grid views into dashboards to accommodate users who favor traditional reporting formats. 7. AI to help with data analysis through input of queries in the natural English language. 8. Easy organization and cataloging of data sources to simplify searching and management. 9. Unified tooltips to compare different visualizations easily. 10. Subscriptions for scheduled updates of contents such as dashboards in email. ### Pricing The sales team needs to be contacted for pricing. ---

4. Power BI

[Microsoft Power BI](https://www.microsoft.com/en-us/power-platform/products/power-bi) is a data visualization tool that supports a large number of data connectors. ![Redash alternative - Power BI](/images/blogs/5-redash-alternatives/powerbi.png) ### Key Features 1. Library of custom visualizations. 2. DAX Data Analysis Function for pre-written codes used to perform specific data analysis tasks. 3. Content packs to access a collection of elements, eliminating the need to search for them individually. 4. Automated data refreshes for accurate insights. 5. Power BI mobile app for viewing and sharing data on the go. 6. The ability to create measures in a data model and reuse them across multiple connected reports. 7. Creation of custom data visualizations with R and Python. 8. Power Query for a low/no-code import and transformation of data from various sources. 9. The Q&A feature lets users ask questions about data and get answers using natural language processing. 10. APIs for integration. ### Pricing Starts at $10 per user per month. ---

5. Datapad

[Datapad](https://www.datapad.io/) is best used for constant monitoring of business KPIs. ![Redash alternative - Datapad](/images/blogs/5-redash-alternatives/datapad.png) ### Key Features 1. AI-powered data analysis. 2. Supports a wide range of database integrations, allowing users to connect and query data from multiple sources. 3. A suite of pre-built templates to build reports on. 4. Instant notifications for metric changes. 5. Daily email updates for subscribed dashboards. 6. Notifications on the phone for timely updates. 7. Collaboration on metrics right on the dashboard. 8. Easy customization of dashboards. 9. AI assistant that answers queries with charts and tables. 10. Read-only links for sharing performance reports with clients. ### Pricing Starts at $21 per month with a limited number of dashboards. --- ## Conclusion There are various alternatives to Redash, each offering distinct features and functionalities. Choosing the right tool—Draxlr, Looker, Tableau, Power BI, or Datapad—depends on your team’s specific requirements and the desired approach to data visualization and analysis. --- ## FAQs ### 1. What is the free alternative to Redash? Draxlr with a 7-day free trial is the best alternative to Redash. With a gentle learning curve and top-notch customer support, Draxlr is the right choice if you are looking for a data visualization tool that combines ease of use, powerful features, and reliable performance to make data analysis seamless and efficient. ### 2. What is the alternative to Redash self-hosting? Draxlr is the ideal alternative to Redash if you are looking for a data visualization tool that allows you to self-host. You can contact the friendly customer support team at Draxlr to create a custom plan for you that enables you to self-host. ### 3. Does Redash use SQL? Yes, Redash, like other great data visualization tools such as Draxlr, uses SQL as its foundation to create powerful and stunning data visualizations. By leveraging SQL, these tools enable users to efficiently query data, transform it into meaningful insights, and present it through visually compelling charts and dashboards that enhance business decision-making.

Ensuring seamless adoption of self-service BI in your organization

hello@draxlr.com (Draxlr) — Sun, 19 Jan 2025 00:00:00 GMT

As [self-service business intelligence](https://www.itransition.com/business-intelligence/self-service) aims to combat the issues associated with IT-driven BI solutions, more and more organizations are adopting these platforms to empower their business users and reduce their dependence on IT teams. With self-service business intelligence software, everyone in the organization, regardless of their technical proficiency, can access, analyze, and visualize data independently, without relying on IT teams or data specialists. Self-service BI software brings numerous benefits, including boosting overall operational efficiency, raising the skill level of regular employees, and nurturing a data-driven business culture. Yet, despite all the advantages, the widespread adoption of self-service BI solutions can still be slow. In this article, we’ll take a look at the possible issues that hamper the success of self-service BI in a company and provide tips on how to overcome them. --- ## Self-service BI adoption: What can go wrong? Among the critical challenges that self-service BI adoption faces are: - Poor data quality residing in the source systems and data silos resulting in mismatches and fragmented data - Lack of standardization within teams using different metrics and calculations leading to varying interpretations of data and analytics insights - Data security and compliance because of the data being exposed to a larger number of users - The growing number of users and data volumes leading to BI system performance degradation - Over-engineering the solution with additional functionality resulting in complexity for business users - High investments in software licensing, workforce training, system maintenance, and user support - Additional efforts to ensure all users can fulfill their needs with the self-service BI platform, be it basic reporting and dashboard customization or more advanced tasks like creating and modifying semantic models --- ## Strategies to boost self-service BI adoption While self-service BI software can present more challenges than expected, with the right approach, it’s possible to drive its adoption. ### 1. Centralized data governance Employees always get discouraged by the system that produces erroneous results affecting business decisions. The root issue, however, can lie not in the platform itself but in the quality of data coming into it. The thing is that without robust data governance practices established within the organization, varying data management practices can lead to data duplicates and inconsistencies. In such a case, establishing centralized policies and procedures for data usage, security, quality, and access is essential. Solid data governance increases data trustworthiness and reliability, making it a source of value for all users. Additionally, consider assigning data stewards in your organization. These are people oversee data quality and collection processes, enforce data policies, help mitigate data-related issues, facilitate communication between various data stakeholders, and prepare documentation, data dictionaries, and usage guidelines. ### 2. Choosing the right software Self-service BI tools inherently aim to streamline data management tasks, namely, data integration from heterogeneous sources, raw data aggregation and processing, analytics querying, and report generation and sharing with team members. To enable all these tasks, look for technology options that offer: - Drag-and-drop capabilities to move and manipulate digital data sets in an intuitive way - Plug-and-play data links to automatically add and remove sources without manual configurations - Custom dashboards that allow for the visualization of the most relevant data to users according to their roles - Pre-built report and dashboard templates to streamline data visualization and analysis without compromising the reporting quality - Natural language processing to ensure intuitive user experience - Automated suggestions and recommendations to guide users through the data analysis process Apart from that, for the self-service BI software to integrate with your ecosystem and support an uninterrupted data flow between systems, it should provide pre-built connectors and APIs as well as be compatible with popular middleware solutions. Additionally, check whether the BI system comes with round-the-clock customer support and rich documentation for employees to be able to find answers to their questions promptly. Among the recent advancements that are worth looking for in BI software is artificial intelligence. According to Dr. Russell Hunter from the University of Cambridge, AI, particularly generative AI, helps automate manual tasks, enhance data visualization, and generate insights from large datasets, which [makes complex tasks more accessible](https://advanceonline.cam.ac.uk/blog/how-can-generative-ai-impact-business-intelligence) to regular users. For the BI software to become a strategic asset, it’s also advisable to pick a scalable platform that accommodates its capacity to your business needs while offering a flexible pay-as-you-go pricing model. ### 3. Getting employee buy-in For business users to utilize the self-service BI tool confidently, you should add user training to your agenda. Yet, it’s equally important to secure employee buy-in to ensure high software adoption rates. If business users consider the new platform just an additional time-consuming burden, they’ll give up the system and switch back to spreadsheets. Therefore, you must alleviate arising doubts and win your team over to be committed to your company’s strategic goals. With employee commitment, a company can boost workplace productivity, as it elevates employee engagement, interest, and retention. To obtain the much-needed buy-in, follow these steps: - Conduct a change management assessment and craft an all-embracing change management plan. - Establish communication guidelines and channels and determine how and when to conduct training sessions and who’ll be responsible for developing and maintaining learning materials. - Map data management processes in such a way that users have almost no chance to compromise data quality, security, and integrity by leveraging governed data access, role-based permissions, data access checks, approval workflows, and sensitive data identification and masking. - Survey end users, such as team leaders, IT specialists, and team members of different business units before and during the implementation of the self-service BI tool, to assess their expectations, needs, and requirements. Collaborate with them post-launch and gather feedback, including suggestions, observations, and obstacles they face. Adjust your adoption strategy based on this information, such as conducting personalized user training sessions or adding customizable features that can enhance user experience, such as conversational AI or customizable dashboards. If you lack the required expertise to get employee buy-in and foster user adoption, consider partnering with external specialists providing BI consulting and support services to augment your in-house team and guide your company throughout the intricate self-service BI implementation process. --- ## To sum up Companies opt for self-service BI to allow individuals at all levels of an organization to collect, consolidate, and analyze data without the need to apply IT expertise. This type of software addresses the problem of creating a backlog of tasks for IT teams, ensuring sufficient insights for diverse departments, and boosting operational efficiency. Yet, while self-service BI solutions are implemented to give business users the freedom to leverage the BI system independently of the technical team, numerous roadblocks can arise. To drive user adoption, especially when it comes to business users who lack the required technological experience, companies should foster data literacy through training and detailed documentation, choose a tool that’s easy to learn, and make sure everyone clearly understands the value of the self-service BI solution and is eager to become proficient in it.

Explore 6 Metabase Alternatives for Data Visualization and Analysis

hello@draxlr.com (Draxlr) — Mon, 13 Jan 2025 00:00:00 GMT

Did Metabase not live up to your expectations? Or are you just unsure about whether to stick with it or explore other options? Metabase is surely a very popular business intelligence tool, celebrated for its open-source flexibility. But even the most popular tools don’t always fit the bill for every business. With so many BI tools available, choosing the right one for your business can be challenging, so, to simplify your search, we've curated a list of the top 7 Metabase alternatives. --- ## Best 6 Metabase alternatives at a glance

Metabase alternative	What we like	Starting price
Draxlr	Generate SQL with AI, Simple to use, Affordable, Great support	$75/month(flat price)
Tableau	Mature BI tool, Strong community	$15/user/month
Domo	Easy integrations, Intuitive	Contact Sales
Power BI	Seamless integration with Microsoft products	$10/user/month
Looker	Centralized, reusable data models with LookML	$2000/month
Holistics	Integration with modern data stack tools like dbt and GitHub	$125/month

Here is a quick video showcasing these Metabase alternatives: ---

1. Draxlr

![Draxlr - Alternative to Metabase](/images/blogs/_tools/draxlr.png) [Draxlr](https://www.draxlr.com/) is an intuitive Metabase alternative, blending a robust no-code query builder with AI-powered SQL generation for both non-technical and advanced users. It seamlessly integrates with various databases and provides real-time alerts through Slack, email, and more. With features like embeddable dashboards, granular team access, customizable visualizations, and live data updates, Draxlr empowers teams to dive deep into data analysis. Its user-friendly interface, coupled with excellent customer support, makes it an ideal choice for businesses of all sizes. Connect your Database ### Key features 1. **Offers an easy-to-use interface** with a robust no-code query builder, enabling non-technical users to explore complex data, while also providing a raw query interface for technical users. 2. **AI-powered query generation** integrates seamlessly with Draxlr, allowing users to generate SQL queries through simple text commands. 3. **Embeddable dashboards** with domain restrictions and integrated filters. 4. **Granular team access,** allows the invitation of multiple team members and assign each member read, full, or admin access. 5. **Connects seamlessly** with a wide range of databases, including PostgreSQL, MySQL, MariaDB, PlanetScale, YugabyteDB, CockroachDB, Supabase, ClickHouse, Databricks, Airtable and more. 6. **Real-time alerts** through Slack, email, API integration, and Google Sheets, ensuring you never miss critical updates or changes in your data. 7. **Supports a variety of charts and graphs**, including Bar, Pie, Area, Tree Map, Box & Whisker, Funnel, Line and more. 8. **Executes complex database operations** like joins, summarizations, and groupings with just a few clicks using the intuitive query builder, making data analysis accessible even without advanced coding skills. 9. **Customizable, multi-element dashboards** for in-depth data analysis and visualization. 10. **Flexible dashboard sharing options**, share publicly or keep them private with secure PIN access. 11. **Excellent customer support** through chat and email, ensuring prompt assistance and issue resolution. 12. **Simple and user-friendly interface** with a minimal learning curve, making it accessible for users of all skill levels. 13. **Deep data exploration** with customizable drill-down functionality, enabling users to start with high-level metrics and dig into the underlying details for more granular insights. 14. **Live dashboard mode** automatically refreshes the dashboard every few minutes, providing up-to-date data and real-time insights. 15. **Sort query results** on-the-fly by any column for more efficient data analysis and decision-making. 16. **Easily invite multiple team members** to collaborate within Draxlr, enhancing data analysis and dashboard creation through collective effort. ### How to build dashboards from a SQL database in minutes with Draxlr: ### Pricing Draxlr offers a flat, transparent pricing plan to fit different business needs. Users can select a plan that best suits their data analysis requirements, ensuring they only pay for what they need without hidden costs. ![Draxlr - Alternative to Metabase](/images/blogs/_tools/draxlr.png) ---

2. Tableau

![Tableau - Alternative to Metabase](/images/blogs/6-best-metabase-alternatives-in-2025/images/tableau-homepage.png) [Tableau](https://www.tableau.com/) is a powerful salesforce-owned Metabase alternative that transforms raw data into interactive dashboards, enabling teams to quickly visualize and understand complex insights. It integrates with multiple data sources for real-time analysis and supports non-technical users with its intuitive, drag-and-drop interface, allowing for the creation of sophisticated visualizations without coding. ### Key features - **Drag-and-drop interface** for easy creation of customizable dashboards and visualizations without coding. - **Broad range of charts and maps**, from simple bar charts to complex geographic visualizations. - **Seamless integration with multiple data sources**, including SQL, NoSQL, Google Analytics, AWS, and Excel spreadsheets. - **Customizable visualizations** with advanced features like parameters and calculated fields. - **Seamless data integration** supporting large datasets and embedded visuals on websites or apps. - **Strong community** with recognized certifications and a free plan for students and instructors. - **Predictive analytics** through drill-down capabilities for uncovering hidden patterns and trends. - **Supports multiple datasets** simultaneously and offers flexible, complex visualizations. - **Data version history** allows users to download and use older versions of their data sources, enhancing flexibility and reliability in analysis. ### Major Drawbacks - **Steep learning curve** to use the product, requiring significant time and effort to master. - **High costs** can be a barrier, particularly for smaller businesses and startups. - **Stability issues** with large datasets, causing performance lags. - **Paid support plans**, requiring a subscription for assistance. - **Challenging report searchability**, especially when managing numerous reports, making it harder to find specific ones. ### Pricing Tableau offers a per-user pricing model designed to accommodate various user needs and deployment methods. ![Tableau](/images/blogs/6-best-metabase-alternatives-in-2025/images/1.png) You can read about alternatives of Tableau here: [7 Tableau Alternatives](/blogs/7-tableau-alternatives-in-2025/). ---

3. Domo

![Domo - Alternative to Metabase](/images/blogs/6-best-metabase-alternatives-in-2025/images/domo-homepage.png) [Domo](https://www.domo.com/) is a robust BI tool known for its ease of use and powerful data aggregation capabilities. It allows users to effortlessly combine data from various sources, apply advanced formulas, and create comprehensive views for accurate analysis. ### Key features - **Real-time data insights** with customizable dashboards that allow interactive data filtering and drill-down. - **Rapid data loading** and **customizable dashboards** with parameterized charts and flexible visualizations. - **Seamless integration** with numerous enterprise applications through custom connectors, supporting various data sources. - **Powerful ETL tools** for migrating data from spreadsheets and other sources, making data preparation simple. - **Mobile accessibility** through Domo's app, allowing users to access data and stay informed on the go. - **API integration** for almost any application, making it easy to connect other apps to Domo without advanced coding skills. ### Major Drawbacks - **Limited training resources** with fragmented videos instead of comprehensive end-to-end tutorials. - **Inflexible data extraction** into Excel, limiting ease of data portability. - **Steep learning curve** for advanced features, requiring additional training, especially for users new to data analytics tools. - **Limited visualization options** and pre-built templates, which restrict customization. - **High cost** compared to other BI tools, especially when using separate user accounts. ### Pricing Domo's credit-based pricing can be unclear, as it requires a discussion with the sales team to receive a specific quote. ![Domo](/images/blogs/6-best-metabase-alternatives-in-2025/images/2.png) ---

4. Power BI

![Power BI - Alternative to Metabase](/images/blogs/6-best-metabase-alternatives-in-2025/images/power-bi-homepage.png) [Power BI](https://www.microsoft.com/en-us/power-platform/products/power-bi) is a versatile business analytics tool that allows users to create interactive dashboards, visualize data from various sources, and gain real-time insights. It integrates seamlessly with the Microsoft ecosystem with Azure, Office 365 and offers robust data analysis capabilities, including AI and machine learning features. ### Key features - **Rich data source connectors** with effortless integration to Excel, SQL, Azure, Splunk, and cloud services. - **Row-level security** to ensure data privacy by controlling access based on user roles. - **Powerful data transformation** using Query Editor and Applied Steps for streamlined data cleaning and modeling. - **Robust visualization capabilities** with interactive dashboards featuring charts, graphs, maps, and tables. - **Real-time analytics** for live data tracking and instant insights. - **Extensive customization options** including DAX language, R, and Python for advanced analytics. - **Excellent Microsoft integration** for seamless use with Excel, Azure, and other Microsoft products. - **AI integration** to uncover hidden insights, enabling advanced data analysis. ### Major Drawbacks - **Steep learning curve** for beginners, particularly for advanced features and DAX expressions and c**omplex setup** for non-technical users. - **Limited customer support** available mainly through email and blogs, making it hard to get direct assistance. - **Costly licensing** and a confusing pricing structure, which can be restrictive for small businesses. - **Frequent software updates** can be tricky to manage, causing compatibility issues. - **Limited drill-down and customization options** compared to other tools. - **Dependency on the Microsoft ecosystem** restricts integration with non-Microsoft services. ### Pricing It offers multiple pricing options, including a free version for individual users and paid plans like Power BI Pro and Power BI Premium. Pricing is based on user and capacity needs. ![Power BI](/images/blogs/6-best-metabase-alternatives-in-2025/images/3.png) ---

5. Looker

![Looker - Alternative to Metabase](/images/blogs/6-best-metabase-alternatives-in-2025/images/looker-homepage.png) [Looker](https://cloud.google.com/looker) is a leading business intelligence tool acquired by Google and offers powerful data analysis and reporting capabilities. It provides an intuitive, user-friendly interface and seamless integration with various data sources, including Google Sheets and Docs. ### Key features - **Supports data blending** to combine information from different sources into a single dashboard for a holistic view. - **Advanced data modeling with LookML** for building complex data relationships and reusable business logic. - **Customizable visualizations** offering a wide range of charts and graphs, with advanced drill-down options for interactive dashboards. - **Real-time data insights** with live updates, supporting timely decision-making. - **Automated data import** for maintaining up-to-date reports without manual intervention. - **Flexible data sharing** options, including CSV, Excel, and PDF formats, for distributing information efficiently. - **Robust security features** like row-level security to control data visibility based on user roles. - **Collaboration-friendly environment** allowing multiple users to work on reports and analysis simultaneously. - **Integration with Google ecosystem** for seamless use with Google products like Google Ads and BigQuery. - **Scalable for enterprises** with advanced triggers, APIs, and the ability to handle large data volumes. ### Major Drawbacks - **Slow performance** when handling large datasets, leading to delays and the need for multiple refreshes. - **Lack of advanced BI features** compared to other enterprise options, impacting in-depth analysis. - **UI and UX limitations** with a dated design and sometimes confusing navigation for new users. - **Limited free version** with constraints on data volume and access, requiring a paid plan for more robust analysis. - **Steep learning curve** for new users, especially when dealing with advanced features and LookML. - **Slow customer support** response times, leading to delays in issue resolution. ### Pricing **Looker** uses a custom pricing model based on factors like the number of users, data volume, and deployment needs. Pricing starts from $2,000 per month(source: capterra), making it a relatively expensive tool. ![Looker](/images/blogs/6-best-metabase-alternatives-in-2025/images/5.png) You can read about alternatives of Looker here: [5 Looker Alternatives](/blogs/5-best-looker-alternatives/). ---

6. Holistics

![Holistics - Alternative to Metabase](/images/blogs/6-best-metabase-alternatives-in-2025/images/holistics-homepage.png) [Holistics](https://www.holistics.io/) is a versatile BI platform ideal for companies looking to scale their data operations. It promotes a self-service data culture, allowing all teams to access and analyze data securely through interactive dashboards. With features like password-protected sharing, Slack integration, and fast report-building, it offers a balance between technical integration and user accessibility. ### Key features - **Flexible self-service options** for business users with drag-and-drop functionality and intuitive report creation. - **Good integration with dbt** and GitHub, supporting a modern data stack with smooth data modeling and transformation. - **Centralized and reusable analytics logic** using SQL-based data models, reducing duplication and simplifying maintenance. - **Robust access control** with secure sharing options tailored to different user groups and dashboards. - **Dynamic filter functions** and cross-filter options, enhancing user experience and reducing data preparation time. - **Extensive customization options** in dashboards, charts, and visualizations, supporting both simple and advanced use cases. - **Automation and scheduling** with features like email scheduling, Slack notifications, and scheduled exports. ### Major Drawbacks - **Initial learning curve** due to limited documentation and UI difficulties, making it challenging for new users. - **Limited visualization options** and design flexibility, lacking features like drill-down reports and advanced customization. - **SQL dependency** limits ease of use, requiring technical knowledge, making it less accessible for non-technical users. - **Access control limitations** with less granular settings, making it difficult to manage complex user permissions. - **Limited APIs and integrations** reducing flexibility for advanced organizations and specific use cases. - **Pricing complexity** and potential for higher costs, especially when scaling up for larger organizations. ### Pricing The Holistics pricing model typically varies based on the number of users, data sources, and feature requirements. For specific pricing details, customers are encouraged to contact Holistics directly. ![Holistics](/images/blogs/6-best-metabase-alternatives-in-2025/images/3.png) --- ## Find the right Metabase alternative for you We’ve taken a journey exploring top metabase alternatives, each offering a unique flavour. Remember, the best BI tool is the one that integrates seamlessly with your workflow, matches your budget, and ultimately **helps your team close to the insights**. If customer support and ease of use are priorities, **Draxlr** stands out with its responsive service, low learning curve, and affordability Take your time to experiment with these options to find the perfect fit for your team. Happy exploring! --- ## FAQs ### 1. What is better than Metabase? This depends on specific needs. If customer support, affordable pricing and ease of learning is the priority then use tools like Draxlr. It avoids overwhelming you with unnecessary complexity. Other BI tools like Tableau, Power BI, Looker, and Holistics are more costly or require more technical expertise. ### 2. What are the disadvantages of Metabase? The disadvantages of Metabase include the need for technical expertise during setup, especially for on-premise deployment. The pricing increases with each additional user, making it costly for larger teams. Free users have limited support options, and advanced data security measures often require integration with separate tools. ### 3. Can I use Metabase for free? The answer is yes and no. Metabase offers an open-source version that is free to use. However, many advanced features are only available in the paid versions. ### 4. How do I know which Metabase alternative is the best fit for my business? To find the best Metabase alternative for your business, start by listing your specific requirements, such as customer support, data integrations, visualization options, user access controls, and budget. Compare these needs with the features of other BI tools like Draxlr, Tableau, Power BI, Looker, or Holistics. Once you've identified a few suitable options, take advantage of free trials to evaluate their usability and fit for your team. ### 5. What are Metabase competitors? Competitors include Draxlr, Tableau, Power BI, Looker, Holistics, Redash, and Superset, each offering various features for different business intelligence needs.

Explore 7 Tableau Alternatives for Data Visualization and Analysis

hello@draxlr.com (Draxlr) — Mon, 13 Jan 2025 00:00:00 GMT

Welcome to our complete reference, **Tableau Alternatives for Data Visualization and Analysis**. In this fast-changing digital age, data visualization and analysis have become critical for making informed decisions and strategies. Tableau is a well-known product that has had a considerable impact in this sector. Its user-friendly interface and powerful capabilities have made it a popular tool among data analytics professionals all around the world. But every business and person is different, and Tableau might not always be the best fit. Maybe you need functionality that Tableau doesn't provide, or the price is out of your budget. Maybe you simply want to try out several tools to expand your skill set. This is where the blog comes in. We have carefully selected seven potential Tableau substitutes, each with unique key features, benefits, drawbacks, and pricing breakdowns. The list covers a broad spectrum of needs and finances, from powerful enterprise solutions to open-source platforms. --- ## Best Tableau alternatives 1. [**Draxlr**](#draxlr): A Self-serve BI tool that is easy to use, has great customer support, and connects with various SQL databases. 2. [**Power BI**](#power-bi): Developed by Microsoft, Power BI offers robust data visualization capabilities, as well as strong integration with other Microsoft products like Excel and Azure. 3. [**QlikView/Qlik Sense**](#qliksense): Qlik's suite of products provides powerful analytics and visualization tools, allowing users to explore data and make data-driven decisions. 4. [**Looker Studio**](#looker-studio): Looker, now part of Google Cloud, is a data exploration and analytics platform that enables users to create and share reports and dashboards. 5. [**Domo**](#domo): Domo is a cloud-based platform that offers business intelligence and data visualization capabilities, allowing users to connect, prepare, visualize, and share data insights. 6. [**Sisense**](#sisense): Sisense is a business intelligence software that allows users to easily prepare, analyze, and visualize complex data sets. 7. [**Oracle Analytics Cloud**](#oracle-analytics-cloud): Oracle Analytics Cloud (OAC) is a cloud-based platform offering advanced data visualization and predictive analytics powered by machine learning, seamlessly integrated with Oracle databases and services. Here is a quick video showcasing these alternatives: ---

1. Draxlr

[Draxlr](https://www.draxlr.com) is a no-code data visualization tool that simplifies creating dashboards and setting up alerts for SQL databases like PostgreSQL, MySQL, MS SQL, Databricks, and more. It features an intuitive query builder for filtering, sorting, joining, summarizing, and grouping data without coding. Draxlr also supports advanced visualizations, embedded dashboards, and AI-driven insights, as well as real-time data alerts via email or Slack for timely decision-making. Connect your Database ![Draxlr - Alternative to Tableau](/images/blogs/_tools/draxlr.png) #### Key Features - Query builder is available to help non-technical users generate insights. - AI to assist with generating SQL queries and dashboards. - Embed dashboard in your customer-facing apps with the customer's context. - Alerts: Slack and Email alerts when data changes in your queries to keep you up-to-date. - Option of live mode in Dashboard, this will refresh the dashboard every few minutes to show live data on screen. - APIs are available to extract query data from anywhere. - Google Sheets integration to sync the results of queries. - Get the dashboard summary via email every day. - Great customer support: Available for chat and email support. - Easy to use, small learning curve. - Open to take custom feature requests and quick turnarounds on feedback. - Option to self-host for additional security. - Very affordable for startups. #### How to build dashboards from a SQL database in minutes with Draxlr: #### Pricing

Plan	Price	Features
Premium	$75/month (flat price)	2 Databases, 20 users, 200 AI Credits.
Power	$125/month (flat price)	5 Databases, 50 users, 500 AI Credits.

---

2. Power BI

[Microsoft Power BI](https://www.microsoft.com/en-us/power-platform/products/power-bi) is a robust data visualization and business intelligence tool that enables users to create interactive, real-time dashboards and reports with minimal coding. It supports over 100 data connectors, integrates seamlessly with the Azure SQL Database, and features advanced data modeling with the DAX language. Power BI's intuitive interface, frequent AI-driven updates, mobile accessibility, and affordable pricing make it a leading solution for both technical and non-technical users to analyze and present data effectively. ![PowerBI](/images/blogs/7-tableau-alternatives-in-2025/images/power-bi-dashboard.png) #### Key Features - Power BI enables the creation of interactive and eye-catching dashboards with real-time data refresh capabilities. - The software is frequently updated, incorporating AI features like Copilot for DAX creation, enhancing the product's quality. - AI Features: Includes AI-driven features like Q&A for conversational data querying and Copilot for assisting with tasks. - Supports over 100 data connectors, making it easy to import data from various sources with point-and-click simplicity. - High Data Refresh Rate: Provides excellent data refresh rates, ensuring up-to-date insights. - Diverse Visuals: Offers over 60 visuals for report creation, helping to present KPIs effectively. - Robust Data Modeling and DAX Support: Features advanced data modeling capabilities and supports DAX language for complex calculations. - Mobile Accessibility: Reports and dashboards can be easily accessed via mobile devices, enhancing flexibility. - Integrates seamlessly with Azure SQL Database and Microsoft Data Fabric. #### Major Drawbacks - The steep learning curve requires extensive experience and training, particularly for mastering DAX language and advanced data manipulations. - Handling large datasets can lead to performance lags and slower data extraction times. - Cryptic error messages and weak customer support can hinder troubleshooting and efficient problem resolution. - Real-time data visualization can sometimes fail, missing out on data updates. - Collaboration features can be cumbersome, and sharing reports via email or embedding dashboards securely can be challenging. #### Pricing - Power BI Desktop: Free to use - Power BI Pro: $10 per user per month - Power BI Premium: $20 per user per month or $4,995 per month (dedicated cloud resource) - Power BI Embedded: $1 per 1,000 renders ---

3. QlikView/QlikSense

[Qlik Sense](https://www.qlik.com/) is a powerful business intelligence tool that allows users to create interactive, customizable dashboards and reports. It features an associative data model for intuitive data exploration, supports various data sources, and offers advanced features like augmented intelligence and real-time analytics. Though it has a steep learning curve and can be costly, its scalability, robust security, and strong community support make it an effective data management platform. ![Qliksense](/images/blogs/7-tableau-alternatives-in-2025/images/qliksense-dashboard.png) #### Key Features - Complete data and BI solution with excellent ETL capabilities and a strong front end. - Augmented intelligence (the cognitive engine) can build a data model for you or provide recommendations on how to set up relationships within your data. - AI that generates insights by suggesting charts and alternative methods for presenting your data. - Highly customizable UI and integration capabilities, allowing insights to be incorporated into other applications. - Handles large amounts of data efficiently, driving insights from the same platform. - Accessible on mobile devices, enhancing flexibility. - Capable of deploying complex data models and consolidating information from multiple sources. - Supports multi-node deployment, robust data security, and enterprise scalability. #### Major Drawbacks - Requires significant training and familiarity with the software, especially for advanced features and customization. - The pricing structure can be expensive compared to competitors like Power BI. - Current themes are considered dull, and customization options are limited. - Integration with certain data sources, especially Microsoft products, could be improved. - Token-Based Licensing: The licensing model can be confusing and difficult to manage. - Data Extraction Challenges: Limited capabilities for exporting comprehensive data extracts. #### Pricing - Qlik Sense Business: paid annually at $30 per user per month. This is the perfect solution for lone workers and small groups. - Qlik Sense Enterprise: Annual billing of $2700 per month is required for a minimum of 10 users and 50 GB of data. Advanced capabilities including enterprise-grade data governance, more data source connections, and automated report delivery are included in this subscription. - Qlik Sense Enterprise Premium: You must get a quote from Qlik and the price for this package is negotiable. Everything from the Enterprise plan is included, along with improved performance and scalability capabilities. ---

4. Looker Studio

[Looker Studio](https://lookerstudio.google.com/), formerly Google Data Studio, is a user-friendly business intelligence tool that transforms raw data into interactive, customizable dashboards and reports. It integrates seamlessly with Google's ecosystem and supports various data sources, including Google Analytics and BigQuery. Looker Studio offers robust visualization capabilities and real-time collaborative features, making it ideal for teams aiming to enhance their data-driven decision-making. ![Looker Studio](/images/blogs/7-tableau-alternatives-in-2025/images/looker.png) #### Key Features - Intuitive drag-and-drop functionality makes it accessible for non-technical users. - Easy sharing of dashboards with coworkers and clients for enhanced teamwork. - Seamlessly integrates with Google Analytics, Search Console, Semrush, and other data sources. - Ability to schedule reports and alerts, reducing manual reporting efforts. - Responsive and knowledgeable support team with active community forums. - Supports unlimited viewers and allows extensive data exploration without SQL knowledge. - APIs allow integration with custom or third-party applications to automate workflows. #### Major Drawbacks - Steep Learning Curve: Requires significant training, especially for new users or those without a data analytics background. - Cost: High licensing fees can be prohibitive for smaller organizations. - Data Integration Challenges: Integrating multiple data sources can be tricky, with potential issues compromising report accuracy. - Limited Visualization Options: Customizing visualizations can be difficult, and some desired features may be lacking. - Clumsy Interface: The interface can be difficult to navigate and update data. - Error messages provided confusing (often provided as error codes) and it makes it difficult to debug. #### Pricing Looker Studio (Self-serve) is available at no charge. Their pro version's pricing is $9 per project per month. You can read about alternatives of Looker here: [5 Looker Alternatives](/blogs/5-best-looker-alternatives/). ---

5. Domo

[Domo BI](https://www.domo.com/business-intelligence) is a powerful business intelligence platform that transforms raw data into actionable insights. It offers flexible data experiences, diverse visualizations, and automated user management. User-friendly and browser-based, it connects to thousands of data sources with over 1000 connectors. Features like Magic ETL, data lineage tools, and Jupyter integration simplify complex dataflows. With exceptional customer support and robust governance, Domo BI is essential for informed decision-making and operational efficiency. ![Domo](/images/blogs/7-tableau-alternatives-in-2025/images/domo.png) #### Key Features - Users have significant flexibility to tailor data presentations and interactions according to their needs. - Includes a wide range of visualization types and the ability to create custom visuals using D3 Bricks. - Supports various methods for data ingestion and writeback, such as workbench, webforms, and file uploads. - Data Lineage and Magic ETL: Simplifies the maintenance of complex dataflows and transformations with user-friendly tools. - Over 1000 connectors for various data sources, compared to fewer options in other tools. - Active user community and resources to assist with learning and troubleshooting. - Smart Text and Jupyter Integration: Displays the latest dataset updates and allows for advanced data manipulation and visualization. - Doc Cards and Governance: Embeds business procedures and ensures proper data governance and monitoring. #### Major Drawbacks - Significant capital and time investment needed to establish a useful data warehouse. - Difficult to change dataset schemas; modifying column names can disrupt downstream content. - Lack of Mass-Editing: Cannot apply similar changes to multiple cards or dashboards simultaneously. - Poor Customer Service: Non-technical support reps and slow issue resolution; frequent push for premium support. - Frequent Updates: Keeping up with new features and updates can be challenging and time-consuming. Sometimes they neglect existing features while adding new ones. #### Pricing Domo's pricing model is not publicly disclosed on their website, as it can vary depending on factors such as deployment size, usage, and specific customer requirements. ---

6. Sisense

[Sisense](https://www.sisense.com/) is a top business intelligence tool that converts complex data into useful insights. Sisense's Elastic Data Engine (EDT) enables fast query performance and real-time analytics. It provides a simple interface for data processing, viewing, and sharing. Sisense scales quickly, offers advanced analytics, and protects data. Its mobile apps provide on-the-go access to insights, allowing enterprises to make educated decisions more effectively. ![Sisense](/images/blogs/7-tableau-alternatives-in-2025/images/sisense.png) #### Key Features - BloX templates and embedded widgets enable drag-and-drop customization of dashboards, visualisations, and actionable apps. - Analytics and guided exploration driven by AI and ML automatically highlight findings and effects. Additionally facilitates natural language inquiries and predictive analytics. - Simple to set up and integrate with various data sources. - Intuitive interface and easy to learn, even without prior SQL experience. - Elasticube Modeling Layer: Offers diverse options beyond traditional databases. - Easily integrates and embeds analytics into other platforms. - Offers various charts and tables with easy data pivoting. - Pricing is straightforward and cost-effective compared to other tools like Power BI and Looker. - Continuous improvement with new features added regularly. #### Major Drawbacks - They often have issues with their customer support with some customers reporting this as a deal breaker. - BloX feature allows developers to fully customize functionality and appearance using HTML, CSS, and JavaScript, but it is complex and challenging to implement. - Few users have reported consistent bugs in the crucial features - Lack of no-code options while customizing dashboards is frustrating for non-technical users. #### Pricing Sisense provides custom pricing, you can contact their sales with your requirements for more information. ---

7. Oracle Analytics Cloud

[Oracle Analytics Cloud (OAC)](https://www.oracle.com/in/business-analytics/analytics-platform/) is a cloud-based platform that allows businesses to store, manage, and analyze data effortlessly. It features an intuitive interface for advanced data visualization and predictive analytics powered by machine learning. OAC seamlessly integrates with Oracle databases and other Oracle Cloud services, enhancing data insights and decision-making processes. ![Oracle Analytics Cloud](/images/blogs/7-tableau-alternatives-in-2025/images/oracle.png) #### Key Features - Offers easy-to-use visualization tools and seamless integration with Oracle databases and other platforms, making it ideal for developers. - Enables data-related problem-solving without coding, allowing users to upload and manage data effortlessly. - Incorporates machine learning and predictive analytics, enhancing its capability to analyze vast data streams and provide actionable insights. - Reliable for decision-making based on real-time data analysis and supported by impressive customer service. - Scalable, high-performing, and focused on security, making it suitable for organizations of all sizes. - Supports easy collaboration among business users and can be accessed on any device, enhancing data accessibility and usability. #### Major Drawbacks - Limited integration with third-party tools and non-Oracle data sources, which can complicate workflows. - Complex Setup: Setting up and configuring the cloud can be tedious, especially for users with limited technical knowledge. - Query performance can degrade, especially with live connections, and there's a limitation of 60,000 rows for some reporting. - Outdated Visuals and Features: The out-of-the-box visuals and some features feel outdated compared to competitors, lacking in advanced data visualization capabilities without additional purchases. - Cost: High cost relative to the value provided, with complaints about pricing and the cost of additional licenses and features. - Support and Stability: Frequent errors, stability issues, and non-responsive support are common problems. Sev 1 tickets are difficult to resolve. - Customized dashboards can be tedious to create, and the platform lacks sleek, modern features compared to other BI tools. #### Pricing - Professional - BYOL (Bring Your Own License): $0.3226 per Oracle CPU per hour. - Enterprise - BYOL: $0.3226 per Oracle CPU per hour. - Oracle Analytics Server for Oracle Cloud Infrastructure: $1.75 per Oracle CPU per hour. - Professional: $16.00 per user per month. - Enterprise: $80.00 per user per month. --- ## Conclusion Choosing an alternative to Tableau depends on your requirements, team size, technical knowledge, and budget. You can consider the above choices to make an informed decision. PowerBI and Looker Studio are very good alternatives for Tableau but all of them are priced on the higher side and you also have to consider the resources and time needed to help you with the setup. If customer support is important, you can consider Draxlr, it provides exceptional customer service and even builds features on request. The learning curve is very low and it is also budget-friendly for startups. --- ## FAQs #### What is Tableau? Tableau is a powerful data visualization and business intelligence platform that allows users to create interactive and shareable dashboards using a variety of data sources. #### What are Tableau competitors? Top competitors of Tableau are Power BI, Looker Studio, Qlik Sense, Sisense, Domo, and Draxlr. #### What is Google's tool similar to Tableau? Google acquired Looker and integrated it with their own product Google Data Studio, it is currently branded as Looker Studio. #### Is Qlik better than Tableau? Both are very close competitor. Qlik is very good at data association, data security, but has a steep learning curve. Tableau is user friendly, has strong AI capabilities. #### Which is better PowerBI or Tableau? It totally depends on your requirement. Power BI is more affordable and tightly knit with Microsoft products, so it easier to setup for companies in Microsoft ecosystem. Tableau is better at handling large datasets, has advanced visualization capabilities. #### Is there an affordable alternative to Tableau? You can consider Draxlr, it is quite affordable compared to Tableau. It is ideal for small to mid-sized companies, it also has great customer support.

How to build Dashboards from Excel Sheet

hello@draxlr.com (Draxlr) — Fri, 01 Nov 2024 00:00:00 GMT

Excel sheets have long been a go-to tool for organizing data, whether it’s for tracking business metrics, managing budgets, or analyzing trends. Their flexibility makes them an invaluable tool for both individuals and organizations, providing a simple yet effective way to store and manipulate information. But, while Excel can help manage data, making sense of that data often requires more than rows and columns. **To truly understand patterns, draw insights, and make data-driven decisions, you need a clearer, more visual representation—that’s where dashboards come in**. Building dashboards from Excel sheets allows you to transform static data into dynamic, interactive visualizations 📊✨. Dashboards help you to see the bigger picture at a glance 🔍, making it easier to identify key trends, track performance, and communicate insights effectively. Whether you're trying to simplify complex metrics for a client presentation or keep a pulse on business performance, creating dashboards from your existing Excel data can elevate your ability to analyze and share important information seamlessly 🚀. --- ## Using Draxlr to Build Dashboards from Excel Sheet > Draxlr is a no-code business intelligence tool that uses AI to help you build dashboards. > Draxlr enables you to upload Excel sheets to create visualizations and dashboards. Here's a quick video guide on creating a dashboard using **Excel sheet** data in **Draxlr**: [![How to build Dashboards from Excel Sheet](/images/blogs/how-to-build-dashboards-from-excel-sheet/images/youtube.png)](https://youtu.be/i0x0Dr6WN-o?si=GttTmmB2N3hGY3ja) Alright, let's dive in! ### Uploading Excel Sheet to Draxlr Excel files can contain multiple sheets. When you upload the file to Draxlr, each sheet is imported as a separate table. 1. Login to Draxlr, visit the [Add Database page](https://app.draxlr.com/add-database), and select **Excel**. ![Draxlr Select Excel Sheet](/images/blogs/how-to-build-dashboards-from-excel-sheet/images/select_excel.png) 2. Click on **Upload Excel File** to open the file selector. ![Draxlr Select Excel Sheet](/images/blogs/how-to-build-dashboards-from-excel-sheet/images/upload_file.png) 3. Then select the file that you want to upload. ![Draxlr Upload Excel Sheet](/images/blogs/how-to-build-dashboards-from-excel-sheet/images/select_file.png) 4. Draxlr will read the Excel file and create tables for each sheet in the file. Draxlr also determines the data type for each column. ![Draxlr Upload Excel Sheet](/images/blogs/how-to-build-dashboards-from-excel-sheet/images/uploading_file.png) 5. Now you can see the **list of tables**, and you can start exploring your data. ![Draxlr List Tables](/images/blogs/how-to-build-dashboards-from-excel-sheet/images/list_tables.png) --- ### Creating visualizations from the Excel data Once you've uploaded your Excel sheet to Draxlr, you can create visualizations from the data in several ways. Here are the available options: 1. **Using the Query Builder**: The Query Builder allows you to visually construct queries without any coding. It's an easy interface that helps you quickly select the columns, apply filters, and create visualizations - a variety of graph options, such as bar, pie, line, area, and others. 2. **Using AI**: Draxlr's AI feature can help you generate visualizations by understanding your data and providing automated suggestions. Simply input your requirements, and the AI will create suitable charts and insights for you, making the process both quick and intuitive. 3. **Using SQL**: For those who prefer more control, you can use SQL to write custom queries and create detailed visualizations. This option is perfect if you want to dive deeper into your data and extract very specific insights. Let's see an example of each option. #### Query Builder - First, select the table for which you want to generate insight. - In this example, we will try to get the count of orders placed from different states of the USA. - We can group the data by **state** and summarize by **order count**. - Then we can select the graph option with type set as **Map**. - Each graph has various customization options. For instance, in a map chart, you can specify which country's map you want to display. ![Draxlr List Tables](/images/blogs/how-to-build-dashboards-from-excel-sheet/images/query_builder.png) #### AI - On the navbar, click on **AI**. - Enter the prompt for AI, like in this example: **Get total sales by category**. - Draxlr AI will generate the result, which you can visualize in different graphs. - In this example, we choose a pie chart and customize it to suit our needs. ![Draxlr List Tables](/images/blogs/how-to-build-dashboards-from-excel-sheet/images/AI.png) #### SQL Similar to the AI option, you can write a custom SQL query to get the desired result. This option is ideal for technical users who want to dive deeper or create complex data insights. --- **That's it; your dashboard is now complete!** ![Draxlr Excel Sheet - Dashboard View](/images/blogs/how-to-build-dashboards-from-excel-sheet/images/dashboard_view.png) You can **share the dashboard** with your team 👥, or even **embed the dashboard** in your customer-facing application 🌐. --- Building dashboards from Excel sheets has never been easier, thanks to tools like Draxlr. Whether you prefer using the no-code Query Builder, AI-generated insights, or writing SQL queries for greater customization, Draxlr offers flexible options to suit your needs. With powerful visualizations, you can turn raw data into actionable insights, empowering you and your team to make informed decisions effortlessly. Start creating your dashboards today and unlock the full potential of your data! 📊🚀 \- **Vivek**

Best 5 Typeform Alternatives to Choose

hello@draxlr.com (Draxlr) — Mon, 28 Oct 2024 00:00:00 GMT

Typeform is widely recognized for its user-friendly interface and visually appealing designs. However, some businesses and individual users may require different features, price points, or integrations to best suit their unique needs. This article explores the top five alternatives to Typeform, analyzing features, pricing, and ease of use to help you find the perfect form-building tool. ## 1\. forms.app [forms.app](https://forms.app/en/typeform-alternative) offers a flexible online form builder ideal for surveys, registrations, and more. It combines ease of use with a strong range of customization options, allowing users to create professional forms without technical expertise. While the platform provides integrations and analytics, it may feel limited in advanced reporting or extensive branding needs compared to some competitors. ### Standout Features: - **Advanced Customization:** forms.app provides versatile customization options that allow users to tailor form colors, fonts, and layouts to align with their brand identity. This level of customization ensures that forms are not only functional but visually consistent with your brand. - **Conditional Logic:** forms.app supports conditional logic, enabling forms to dynamically adapt based on user responses. This helps streamline the form-filling experience by hiding or showing fields only when necessary, which can improve user engagement and data relevance. - **Integration with Popular Apps:** forms.app integrates seamlessly with widely-used applications, including _Google Sheets, Slack, and Mailchimp_. This allows users to automate workflows, ensuring that form responses are quickly synced with existing platforms for efficient data management. - **Payment Collection Options:** With built-in payment gateways like _Stripe and PayPal_, forms.app simplifies the process of collecting payments directly within the form. This feature is particularly useful for e-commerce businesses or event organizers requiring secure and convenient payment processing. - **Template Library:** Offering a library of customizable templates for various purposes forms.app makes it easy for users to start quickly. Each template is designed to save time while maintaining a professional appearance, from survey forms to order forms. ### Why Choose forms.app? forms.app stands out as a user-friendly yet highly customizable form builder, making it an excellent choice for businesses and freelancers seeking a powerful, brand-aligned form solution. With affordable pricing, it’s a strong option for organizations of all sizes. ### Pricing: forms.app offers a straightforward pricing structure with a free plan and various paid tiers, allowing users to choose based on their specific form-building needs. However, some advanced features are only accessible in higher-tier plans. ![Best-5-Typeform-Alternatives-1](/images/blogs/best-5-typeform-alternatives/images/Best-5-Typeform-Alternatives-1.png) --- ## 2\. Jotfom Jotform is a popular form builder known for its extensive features and intuitive drag-and-drop interface. It supports a wide range of form types, from simple surveys to complex data collection. Despite a steeper cost for advanced plans, Jotform’s design versatility and automation capabilities make it a reliable choice for businesses looking to streamline their workflows. ### Standout Features: - **Extensive Template Collection:** Jotform offers over 10,000 templates across multiple industries and form types, from registration and feedback forms to lead generation forms. These templates are pre-designed, allowing users to create forms quickly while still tailoring them to their specific needs. - **Conditional Logic and Branching:** Jotform’s conditional logic feature helps create dynamic forms that adjust based on user input. This feature is ideal for creating complex forms where certain fields or questions are only relevant under specific conditions, optimizing the user experience. - **Payment Integration Options:** Jotform integrates with over 30 payment processors, including PayPal, Stripe, and Square. This feature enables secure transactions directly within the form, making it suitable for e-commerce sites, donation forms, and membership-based businesses. - **HIPAA Compliance and Data Security:** With options for HIPAA compliance, Jotform is ideal for healthcare providers and businesses handling sensitive information. Jotform’s focus on data security ensures that user data is protected, meeting industry standards for data privacy. - **Drag-and-Drop Form Builder:** Jotform’s intuitive drag-and-drop interface allows users to easily add and rearrange fields, customize layout, and style forms to their liking. This feature makes form building accessible to users without coding knowledge. ### Why Choose Jotform? Jotform is ideal for organizations in industries with strict data security requirements, such as healthcare or education. Its robust integration options, template variety, and focus on compliance make it a versatile tool for businesses of all sizes. ### Major Drawbacks - **High Subscription Costs**: Jotform’s higher-tier plans may be costly for small businesses or individual users, particularly those needing extensive functionality. - **Complex Navigation for New Users**: Despite its intuitive interface, the extensive range of features can feel overwhelming, creating a steep learning curve for first-time users. - **Limited Storage on Free Plans**: The free version has capped storage and form submission limits, often necessitating an upgrade for regular users. - **Slow Performance with Large Forms**: Jotform can experience lag and slow loading times, especially when handling complex, multi-page forms or high traffic. - **Basic Analytics**: Users may find the analytics features lacking compared to tools specifically designed for data-driven insights. ### Pricing: Jotform provides a tiered pricing model that includes a free plan, with paid plans offering additional features like increased submission limits and payment processing. Pricing may vary based on the required functionalities, making it essential for users to evaluate their needs**.** ![Best-5-Typeform-Alternatives-2](/images/blogs/best-5-typeform-alternatives/images/Best-5-Typeform-Alternatives-2.png) --- ## 3\. Cognito Forms Cognito Forms is well-suited for users needing powerful forms with calculations, payment options, and document generation. Its robust functionality supports various form needs but may require a learning curve for advanced features. Cognito Forms also provides customization and integrations, though its cost and limited offline access might restrict users seeking more comprehensive solutions for larger teams. ### Standout Features: - **Calculations and Form Logic:** Cognito Forms excels with its advanced calculation and logic capabilities, enabling users to create complex forms that automatically calculate totals, scores, or other values based on responses. This feature is particularly beneficial for financial services and academic assessments. - **Repeating Sections and Tables:** Cognito Forms supports repeating sections, allowing users to collect multiple data sets within a single form submission. This is ideal for surveys or forms requiring numerous entries under one submission, such as expense reports or order forms. - **Powerful Data Export Options:** Cognito Forms provides flexible export options, enabling users to download data in formats like Excel or PDF. This allows for easy analysis and report generation, especially for data-driven projects or detailed client reporting. - **Secure Document Uploads:** Users can collect file uploads from respondents, making Cognito Forms a reliable choice for forms that require additional documentation, such as job applications, contract submissions, or legal compliance forms. - **Built-In Payment Processing:** Cognito Forms integrates with payment platforms like Stripe and Square, allowing businesses to accept payments directly through forms. This feature is particularly useful for small businesses that rely on seamless payment processing for orders or appointments. ### Major Drawbacks - **Learning Curve for Advanced Functions**: While straightforward for simple forms, Cognito Forms’ advanced features may require time and additional training to master. - **High Pricing for Advanced Features**: Accessing advanced features, such as document generation, often requires higher-tier pricing, potentially making it less accessible for budget-conscious users. - **Limitations on Integrations**: Users requiring a wide range of integration options may find the available selection narrow, limiting its utility in complex environments. - **Limited Customization Options**: Form customization, especially around themes and brand-specific designs, can feel restrictive. - **Restricted Offline Access**: Cognito Forms doesn’t support offline access, which can be inconvenient for users needing field data collection without stable internet. ### Pricing: Cognito Forms utilizes a subscription-based pricing model with a free tier that includes basic functionalities, while paid plans unlock advanced features such as payment integrations and conditional logic. Users can choose a plan based on their needs, but scaling can lead to higher costs. ![Best-5-Typeform-Alternatives-3](/images/blogs/best-5-typeform-alternatives/images/Best-5-Typeform-Alternatives-3.png) --- ## 4\. Google Forms Google Forms is a straightforward, free tool ideal for creating simple surveys and quizzes. It offers basic customization and integrates seamlessly with Google Sheets for data analysis, making it a go-to for quick data collection. While it lacks advanced features like in-depth analytics and branding, it’s excellent for informal or personal use within the Google ecosystem. ### Standout Features: - **Simple and User-Friendly Interface:** Google Forms is known for its minimalistic and intuitive design, making it an accessible choice for beginners. Users can easily create and share forms without the need for advanced technical skills, making it popular for quick surveys or polls. - **Real-Time Collaboration:** Google Forms supports real-time collaboration, enabling multiple team members to work on a form simultaneously. This feature is useful for teams that need to gather feedback or brainstorm questions before deploying a form. - **Seamless Integration with Google Workspace:** As part of the Google Workspace ecosystem, Google Forms integrates seamlessly with other Google apps like Sheets and Drive, making it easy to organize and analyze response data within familiar tools. - **Unlimited Form Responses:** Google Forms allows users to collect an unlimited number of responses, which is advantageous for large-scale surveys or events with high participation rates. This feature is especially helpful for academic institutions or large organizations. - **Free for All Users:** Google Forms is entirely free to use, making it a great option for individuals, nonprofits, or small businesses with limited budgets who still need a reliable form solution. ### Major Drawbacks - **Limited Design and Branding Options**: Google Forms offers basic templates and color schemes but lacks customization features, limiting brand consistency for professional forms. - **Basic Functionality for Complex Needs**: While sufficient for simple surveys and quizzes, it lacks advanced question types, automation, and integrations, making it unsuitable for detailed data collection. - **No Built-In Analytics**: The platform lacks native analytics, relying on Google Sheets for data analysis, which may feel cumbersome for users seeking embedded reporting. - **Limited Security Features**: Without advanced access controls or dedicated compliance options, Google Forms may not be ideal for sensitive data collection. - **Dependence on Google Ecosystem**: It relies heavily on other Google apps, which may hinder collaboration for users in non-Google environments. ### Pricing: Google Forms is completely free to use, making it an attractive option for users who need basic form capabilities without the need for advanced features or integrations. However, users requiring more complex functionalities may need to look at other paid tools. ![Best-5-Typeform-Alternatives-4](/images/blogs/best-5-typeform-alternatives/images/Best-5-Typeform-Alternatives-4.png) --- ## 5\. SurveyMonkey SurveyMonkey is a widely used survey tool offering a range of question types, templates, and analytics. Known for its powerful survey capabilities, it allows detailed data insights and customizations, though advanced features require a premium plan. With some customization limitations and high costs for premium features, it’s a solid choice for professional surveys and feedback collection. ### Standout Features: - **Robust Survey Tools and Analytics:** SurveyMonkey offers extensive survey-building tools with built-in analytics, allowing users to gather and analyze insights with ease. The analytics dashboard includes real-time data, response trends, and export options for detailed reporting. - **Audience Targeting and Sample Pools:** SurveyMonkey provides access to a diverse audience pool, allowing users to target specific demographics for market research. This feature is particularly beneficial for businesses seeking targeted insights and reliable data. - **Advanced Question Types:** With SurveyMonkey, users can incorporate a wide variety of question types, including Likert scales, ranking questions, and open-text fields. This flexibility enables creators to design surveys that capture nuanced feedback. - **Survey Templates for Different Industries:** SurveyMonkey offers industry-specific templates tailored for different fields like education, healthcare, and business. These templates help users create relevant and effective surveys quickly. - **Mobile Optimization:** SurveyMonkey forms are mobile-friendly, ensuring that surveys are accessible on any device. This feature increases response rates and provides a positive user experience for mobile respondents. ### Major Drawbacks - **Pricing Constraints for Advanced Features**: Basic plans are often too limited, and key features like advanced analytics and export options require costly subscriptions. - **Complexity in Customization**: SurveyMonkey’s design options can be restrictive, especially for users seeking a fully customizable look and feel. - **Performance Issues in High-Traffic Scenarios**: Forms with high response volumes or multiple branches may experience lags, affecting user experience. - **Limited Free Plan Features**: SurveyMonkey’s free version restricts question types, responses, and survey visibility, quickly pushing users toward paid plans. - **Customer Support Constraints**: Comprehensive customer support, such as live chat or phone support, is only accessible on premium plans, creating delays for basic users needing assistance. ### Pricing: SurveyMonkey employs a tiered pricing structure that includes a basic free plan, with paid plans offering advanced features such as custom branding and detailed analytics. Users must assess their survey needs, as premium plans can become costly based on the features required. ![Best-5-Typeform-Alternatives-5](/images/blogs/best-5-typeform-alternatives/images/Best-5-Typeform-Alternatives-5.png) --- ## Conclusion In conclusion, choosing the right form-building tool is essential for effectively gathering data and engaging with your audience. Each platform has its unique strengths and drawbacks, making it crucial to assess your specific requirements, such as ease of use, customization options, and pricing.

How to build Dashboards from ClickHouse data

hello@draxlr.com (Draxlr) — Thu, 25 Jul 2024 00:00:00 GMT

[ClickHouse](https://clickhouse.com/) is a fast and highly efficient columnar database management system. Top 8 features of ClickHouse: 1. **High Performance**: Remarkable speed, capable of processing billions of rows and petabytes of data per second. 2. **Columnar Storage Format**: Efficiently stores data by columns, allowing for substantial compression and quicker data access. 3. **Scalability**: Supports both vertical and horizontal scaling to manage large datasets and high query loads in distributed clusters. 4. **Real-time Data Ingestion**: Enables immediate data processing and analysis, making it ideal for real-time applications. 5. **SQL Compatibility**: Utilizes a familiar SQL query language, making it user-friendly for those experienced with SQL databases. 6. **Advanced Compression**: Employs sophisticated compression algorithms to minimize storage requirements and enhance I/O performance. 7. **Distributed Query Processing**: Efficiently distributes data and executes queries in parallel across multiple nodes for improved performance. 8. **High Availability**: Ensures data consistency and system reliability through data replication and automatic failover mechanisms. --- ## Using Draxlr to build Dashboards from ClickHouse data > [Draxlr](https://clickhouse.com/docs/en/integrations/draxlr) is a no-code BI tool that allows you to create dashboards, receive data alerts, and embed dashboards on customer-facing websites. > Draxlr has partnered with ClickHouse to allow ClickHouse users to build dashboards and get data alerts. Here is a quick video walkthrough of how to build a dashboard from **ClickHouse data** in **Draxlr**: Ok, let's get started. ### Connecting ClickHouse with Draxlr In ClickHouse, you can have multiple organisations, and each organisation can have multiple projects. Each project has a Postgres Database. You can connect to a ClickHouse project database to generate insights into your data. 1. Login into Draxlr, visit the [Add Database page](https://app.draxlr.com/add-database), and select **ClickHouse**. ![Draxlr Select ClickHouse](/images/blogs/how-to-build-dashboards-from-clickhouse-data/images/select_clickhouse.png) 2. Select the **platform** where your ClickHouse data is hosted. For this guide, let us assume you select ClickHouse Cloud. ![Draxlr Select ClickHouse Hosting platform](/images/blogs/how-to-build-dashboards-from-clickhouse-data/images/select_host.png) 3. You will see a form to provide the database credentials. To get the database credentials login into your ClickHouse account and visit [https://console.clickhouse.cloud/](https://console.clickhouse.cloud/). ![Draxlr ClickHouse credential form](/images/blogs/how-to-build-dashboards-from-clickhouse-data/images/database_credential_form.png) 4. ClickHouse Cloud can have multiple services under an organization, select the service which you want to connect. ![Draxlr ClickHouse select service](/images/blogs/how-to-build-dashboards-from-clickhouse-data/images/select_service.png) 5. Click on **Connect** in the sidebar. ![Draxlr ClickHouse click connect](/images/blogs/how-to-build-dashboards-from-clickhouse-data/images/click_connect.png) 6. On the Connect popup, select **MySQL** and copy the credentials to the Draxlr screen and hit **Next**. ![Draxlr ClickHouse credential form filled](/images/blogs/how-to-build-dashboards-from-clickhouse-data/images/database_credential_form_filled.png) 7. You can now select the database schema; by default, it is **default**. ![Draxlr ClickHouse Schema](/images/blogs/how-to-build-dashboards-from-clickhouse-data/images/set_schema.png) 8. Now you can see the **list of tables**, and you can start exploring your data. ![Draxlr List Tables](/images/blogs/how-to-build-dashboards-from-clickhouse-data/images/list_tables.png) --- ### Generating Queries and Visualizing the data After connecting ClickHouse with Draxlr, you can create dashboards from your ClickHouse data. 1. Utilize Draxlr's no-code query builder to generate data with **filters, joins, sorting, summarizing**, and **grouping** options. 2. Visualize the resulting data using various graph options such as **bar, pie, line, area, tree-map, box-and-whisker**, and more. ![Draxlr ClickHouse Graph Options](/images/blogs/how-to-build-dashboards-from-clickhouse-data/images/graph_options.png) 3. Once your graph setup is complete, save the query and add it to a dashboard. ![Draxlr ClickHouse Add to Dashboard](/images/blogs/how-to-build-dashboards-from-clickhouse-data/images/add_to_dashboard.png) 4. **Your dashboard is now ready!** ![Draxlr ClickHouse - Dashboard View](/images/blogs/how-to-build-dashboards-from-clickhouse-data/images/dashboard_view.png) 5. Share the dashboard with your team and embed it in your customer-facing application. --- Building dashboards from ClickHouse data with Draxlr is a straightforward process that leverages the power of both platforms. ClickHouse’s exceptional speed and scalability, combined with Draxlr's user-friendly interface and powerful visualization tools, enable you to gain valuable insights quickly and efficiently. Whether you are managing large datasets or need real-time data analysis, this guide equips you with the knowledge to create and share dynamic dashboards that can enhance decision-making and drive business growth. \- **Vivek**

How to set up alerts for SQL data

hello@draxlr.com (Draxlr) — Thu, 20 Jun 2024 00:00:00 GMT

Setting up alerts for SQL data, such as order counts and other key metrics, is essential for maintaining the accuracy and integrity of your business operations. In this article, we will delve into the process of configuring alerts that monitor specific data points within your SQL database, enabling you to track changes, trends, and anomalies in real-time. By setting up these alerts, you can stay informed about critical data fluctuations, ensuring timely responses to potential issues and opportunities. Whether you are managing sales data, inventory levels, or customer interactions, this article will equip you with the necessary steps to effectively monitor and react to significant data changes within your SQL database. --- ## Key benefits of data alerts on your SQL data Implementing data alerts on your SQL data offers several key benefits: 1. **Proactive Issue Detection:** Alerts allow you to identify and address issues before they escalate, such as unexpected drops in order counts or spikes in transaction volumes, ensuring timely intervention and resolution. 2. **Enhanced Data Accuracy:** Continuous monitoring through alerts helps maintain data integrity by flagging anomalies or discrepancies, allowing for quick corrective actions to keep your database reliable. 3. **Improved Decision Making:** Real-time alerts provide immediate insights into critical metrics, enabling informed decision-making based on the most current data, which is crucial for strategic planning and operational efficiency. 4. **Increased Operational Efficiency:** Automated alerts reduce the need for manual data checks, freeing up time for more strategic tasks and improving overall productivity within your team. 5. **Better Resource Management:** By monitoring data usage and trends, alerts can help optimize resource allocation, ensuring that your database and related infrastructure are used efficiently and effectively. 6. **Enhanced Customer Experience:** Timely alerts about data trends and anomalies can lead to faster responses to customer needs and issues, improving service quality and customer satisfaction. 7. **Compliance and Risk Management:** Alerts can help ensure compliance with regulatory requirements by monitoring data changes and access patterns, reducing the risk of data breaches, and ensuring that sensitive information is protected. By leveraging these benefits, you can maintain a robust, responsive, and efficient SQL data management system that supports your business objectives. --- ## How to build alerts for your SQL data You can set up SQL data alerts on your database in multiple ways. Here we discuss two options to setup alerts on a platform: **writing custom code and using a tool like Draxlr**. Connect your Database ### 1. Writing Custom Code Creating custom SQL alerts through code provides a high degree of flexibility and control over the monitoring process. This method involves writing SQL scripts or stored procedures that check for specific conditions and then trigger alerts based on those conditions. Here are the key steps involved: - **Define the Criteria:** - Determine what data points you need to monitor. For example, you might want to track the count of orders, inventory levels, or customer sign-ups. - Set thresholds for these data points. For instance, you might want to be alerted if the order count drops below a certain number or if inventory levels are too low. - **Write the SQL Query:** Create a SQL query or stored procedure that checks the defined criteria. This query will run at regular intervals to monitor the data. ```sql CREATE PROCEDURE CheckOrderCount AS BEGIN DECLARE @OrderCount INT; SELECT @OrderCount = COUNT(*) FROM Orders WHERE OrderDate = CAST(GETDATE() AS DATE); IF @OrderCount < 100 BEGIN -- Send an alert (email, SMS, etc.) END END; ``` - **Schedule the Query:** Use a scheduling tool such as SQL Server Agent, cron jobs, or other task schedulers to run the query at regular intervals (e.g., every hour, daily). ```sql EXEC sp_add_job @job_name = 'OrderCountAlert'; EXEC sp_add_jobstep @job_name = 'OrderCountAlert', @step_name = 'Check Order Count', @subsystem = 'TSQL', @command = 'EXEC CheckOrderCount'; EXEC sp_add_schedule @job_name = 'OrderCountAlert', @name = 'DailySchedule', @freq_type = 4, @freq_interval = 1, @active_start_time = 090000; EXEC sp_attach_schedule @job_name = 'OrderCountAlert', @schedule_name = 'DailySchedule'; EXEC sp_add_jobserver @job_name = 'OrderCountAlert'; ``` - **Send Notifications:** Integrate your alert system with email, SMS, or other notification services to ensure that alerts reach the appropriate personnel promptly. --- ### 2. Using a Tool Like Draxlr For those looking for a more user-friendly and less time-consuming approach, using a tool like Draxlr can significantly simplify the process of setting up SQL data alerts. Draxlr is a powerful data analysis and monitoring tool that offers built-in alerting capabilities without the need for extensive coding. Here’s how you can set up alerts using Draxlr: - **Connect Your Database:** Log in to Draxlr and connect it to your SQL database. Draxlr supports various SQL database types, making it easy to integrate with your existing infrastructure. ![How to setup alerts for SQL data - SQL Databases](/images/blogs/alerts-for-sql-data/images/add-database.png) - **Create the query for which you want to set the alert:** In Draxlr, you can either write SQL queries, use the [Query builder](/features/query-builder/), or use [AI](/features/AI/) to get insights from your SQL data. Here is an example query built using the query builder where we are trying to get the count of signed-up users in the last 7 days. ![How to setup alerts for SQL data - New query](/images/blogs/alerts-for-sql-data/images/new-query.png) Once you have the desired results, you can save the query by adding a name and description. You can also choose to add the query to a [dashboard](/features/dashboard/). ![How to setup alerts for SQL data - Save query](/images/blogs/alerts-for-sql-data/images/query-save.png) - **Setting up Alerts:** You can trigger alerts via Slack or Email. Here we show an example of how to set up an alert on Slack. - First, you can select the Slack channel where you want to get the alert. ![How to setup alerts for SQL data - New alert](/images/blogs/alerts-for-sql-data/images/alerts-slack-channel-name.png) - Let's say you want an alert when the sign-ups in the last 7 days go above 6,000. With the below settings, Draxlr will check the query result every 6 hours (it can be customized to minutes) to see if the users count has gone above 6,000. ![How to setup alerts for SQL data - Save alert](/images/blogs/alerts-for-sql-data/images/alerts-save.png) - When the query result crosses 6000, a Slack alert will be sent to the selected channel. Example alert: ![How to setup alerts for SQL data - Save alert](/images/blogs/alerts-for-sql-data/images/alert-in-slack.png) - **Set Notification Preferences:** You can choose to get the alerts in a periodic manner or at a specific time of the day. There are options to set thresholds based on absolute value or relative change. Similar alerts can also be configured in emails. - **Monitor in Real-Time:** Draxlr continuously monitors your data and triggers alerts in real-time when the defined conditions are met. It is super-easy to manage and configure in a no-code fashion. Here is a short demo of the feature: By choosing either of these methods, you can effectively set up SQL data alerts tailored to your specific needs. Writing custom code offers maximum flexibility and control, ideal for complex or highly specific monitoring requirements. On the other hand, using a tool like Draxlr simplifies the process, making it accessible to users who may not have advanced coding skills, while still providing robust alerting and monitoring capabilities. --- ## Conclusion Setting up SQL data alerts is crucial for maintaining the integrity and accuracy of your business operations. Whether you opt for writing custom code or using a tool like Draxlr, these methods provide robust solutions for monitoring critical data points. By implementing these alerts, you can proactively detect issues, make informed decisions, and enhance overall operational efficiency. With real-time insights and timely notifications, your team can stay ahead of potential problems, ensuring a smooth and reliable database management experience. --- ## FAQs 1. **Why are SQL data alerts important?** SQL data alerts are essential for maintaining data integrity, detecting anomalies early, and ensuring timely responses to critical changes in your database. They help in proactive issue detection, improved decision-making, and enhanced operational efficiency. 2. **What are the benefits of using data alerts on my SQL data?** Data alerts offer several benefits, including proactive issue detection, enhanced data accuracy, improved decision-making, increased operational efficiency, better resource management, enhanced customer experience, and compliance and risk management. 3. **How can I set up SQL data alerts using custom code?** You can set up SQL data alerts by writing custom SQL scripts or stored procedures to monitor specific conditions, scheduling these queries to run at regular intervals, and integrating with notification services to alert the appropriate personnel. 4. **What is Draxlr, and how does it help with SQL data alerts?** Draxlr is a data analysis and monitoring tool that simplifies the process of setting up SQL data alerts. It offers built-in alerting capabilities without extensive coding, allowing you to connect your database, create queries, set up notifications, and monitor data in real-time. 5. **Can I customize the frequency and criteria for SQL data alerts?** Yes, both custom code and tools like Draxlr allow you to customize the frequency and criteria for alerts. You can set thresholds, define specific conditions, and choose how and when to receive notifications based on your needs. 6. **What types of notifications can I set up for SQL data alerts?** You can set up various types of notifications for SQL data alerts, including email, SMS, and integrations with communication tools like Slack. These notifications can be configured to alert you based on specific data changes, thresholds, or at regular intervals. 7. **Is it necessary to have advanced coding skills to set up SQL data alerts?** While advanced coding skills provide maximum flexibility and control, tools like Draxlr make it possible to set up SQL data alerts without extensive coding knowledge, making the process accessible to users of all skill levels. \- **Vivek**

How to build SQL dashboards with AI

hello@draxlr.com (Draxlr) — Mon, 27 May 2024 00:00:00 GMT

A well-designed dashboard is essential for any business as it provides a centralized view of critical metrics and performance indicators, enabling informed decision-making. SQL dashboards, in particular, offer significant advantages by allowing real-time data access, customization, and detailed analytics. But with the integration of AI, they’re becoming even more powerful. AI-powered SQL dashboards can automate data analysis, enhance visualization, and provide deeper insights faster than ever before. Whether you’re a data analyst, a business person, or someone with limited technical skills, this guide will walk you through the process of building AI-powered SQL dashboards using Draxlr. --- ## Key benefits of SQL Dashboards #### 1. Real-Time Monitoring and Analytics - **Instant Updates**: Provides real-time data updates for quick decision-making. - **Real-Time Alerts**: Sends alerts based on specific conditions, allowing prompt action. #### 2. Customization and Flexibility - **Tailored Dashboards**: Customizable for different users or departments. - **Interactive Features**: Allows users to drill down, apply filters, and perform ad-hoc queries. #### 3. Enhanced Decision-Making - **Data-Driven Strategies**: Visualizes data for informed decision-making. - **Predictive Analytics**: Includes advanced analytics for forecasting and risk identification. #### 4. Performance Tracking and Reporting - **Goal Alignment**: Tracks progress towards business goals and KPIs. - **Automated Reporting**: Automates report generation and distribution. #### 5. Efficient Collaboration and Communication - **Shared Insights**: Promotes transparency and teamwork with a shared view of metrics. - **Visual Communication**: Uses charts and graphs for clear and effective communication. #### 6. Cost and Time Efficiency - **Resource Optimization**: Automates data management, saving time and resources. - **Reduced Costs**: Improves operational efficiency, leading to cost savings. --- ## How to use AI to build your SQL Dashboard with Draxlr Draxlr is a self-serve business intelligence (BI) tool that uses AI to simplify data analysis and visualization. It allows users to generate SQL queries through natural language inputs, create customizable dashboards, and visualize data in real-time. Draxlr integrates with various data sources and facilitates team collaboration through easy sharing and access controls, empowering organizations to foster a data-driven culture. Connect your Database ### Option 1: Generate specific SQL queries with AI You can provide Draxlr AI-specific instructions for an ad-hoc query, and it will generate the SQL query for that requirement. **Here are the steps:** 1. Click on the **AI** option in the navbar. ![How to generate SQL query with AI - Navbar](/images/blogs/build-sql-dashboard-with-ai/images/navbar.png) 2. Provide your instructions in the **Generate SQL with Draxlr AI** popup. ![How to generate SQL query with AI - Navbar](/images/blogs/build-sql-dashboard-with-ai/images/prompt.png) 3. Draxlr will generate the required SQL and show the result of the same in tabular format. You can choose to visualize it as a different type of graph, like a map. ![How to generate SQL query with AI - Map](/images/blogs/build-sql-dashboard-with-ai/images/map.png) To learn more about this feature, please read [How to Generate SQL Queries with AI](/blogs/how-to-generate-sql-query-with-AI/). ### Option 2: Generate the entire dashboard with AI Draxlr’s powerful AI-driven feature allows you to generate entire dashboards quickly and effortlessly, providing immediate insights into your SQL database. By simply providing a list of tables and a brief description of your dashboard requirements, Draxlr leverages its AI capabilities to create comprehensive dashboard items in a matter of seconds. This functionality is particularly beneficial for users who need rapid analytics without investing significant time or effort in manual setup. The AI-driven dashboard generation process involves understanding the structure and relationships within your database tables, and then intelligently designing visualizations that best represent your data. Draxlr’s AI considers various factors, such as key performance indicators (KPIs), data trends, and relevant metrics, to ensure the generated dashboards are both insightful and actionable. This feature is especially useful for: 1. **Quick Prototyping**: When you need to prototype dashboards for presentations or decision-making meetings, Draxlr’s AI can swiftly create visually appealing and informative dashboards, allowing you to focus on interpreting the insights rather than building the visuals. 2. **Exploratory Data Analysis**: For initial exploratory analysis, Draxlr provides a fast way to get a broad overview of your data. This can help identify patterns, anomalies, and areas of interest that may warrant deeper investigation. 3. **Time-Saving for Non-Technical Users**: Users who may not have extensive technical skills or SQL knowledge can still benefit from sophisticated data analytics. Draxlr democratizes data access, enabling anyone in the organization to generate meaningful dashboards without relying on specialized data teams. **A quick demo video:** **Here are the steps:** 1. Visit the dashboard page where you want to add dashboard items via AI. Click on **Generate with Draxlr AI**. ![How to generate SQL query with AI - Map](/images/blogs/build-sql-dashboard-with-ai/images/empty-dashboard.png) 2. On the popup, you can provide the tables for which you want the insights. You can also give a brief description of the dashboard. ![How to generate SQL query with AI - Map](/images/blogs/build-sql-dashboard-with-ai/images/dashboard-prompt-with-tables.png) 3. Draxlr AI will analyze the requirements, generate up to six dashboard items, and add them to your dashboard. ![How to generate SQL query with AI - Map](/images/blogs/build-sql-dashboard-with-ai/images/completed-dashboard.png) 4. You can choose to keep the dashboard items that fit your requirements; you can also re-run the process to generate more insights. --- ## Conclusion In today's data-driven world, the ability to quickly and effectively transform raw data into actionable insights is crucial for business success. Draxlr's AI-powered capabilities for generating SQL queries and entire dashboards revolutionize how organizations interact with their data, making sophisticated analytics accessible to all users. By leveraging AI to automate and simplify complex processes, Draxlr not only enhances decision-making and performance tracking but also fosters a culture of collaboration and continuous improvement. As businesses continue to evolve, tools like Draxlr will be instrumental in driving efficiency, innovation, and strategic growth. Start building your AI-powered SQL dashboards with Draxlr today and take your data analysis to the next level. Draxlr offers a free trial. [Sign up today](https://app.draxlr.com/register). --- ## FAQs 1. **What is the AI tool for SQL query optimization?** The article references Draxlr as an AI tool for SQL query optimization. It uses AI to help generate SQL queries and provides suggestions for SQL queries with issues. 2. **How to create a dashboard from SQL?** To create a dashboard from SQL using Draxlr, you can leverage its AI-driven feature by providing a list of tables and a brief description of your dashboard requirements. Draxlr will analyze the data structure and relationships to generate comprehensive dashboard items quickly. This process involves clicking on the AI option, providing instructions or tables, and letting Draxlr generate the necessary visualizations. 3. **Can AI create dashboards?** Yes, AI can create dashboards. Draxlr's AI-driven feature allows users to generate entire dashboards by simply providing a list of tables and a brief description of the required dashboard. The AI intelligently designs visualizations that best represent the data, making it easy to obtain actionable insights without manual effort. 4. **Can AI create a SQL query?** Yes, AI can create a SQL query. Draxlr enables users to generate specific SQL queries by providing natural language instructions to its AI. The AI interprets these instructions and generates the corresponding SQL query, which can then be visualized in various formats such as tables or graphs. 5. **What are dashboards?** Dashboards are visual tools that display key metrics and data in an organized, easy-to-read format, often using charts and graphs. They provide a quick overview of important information, helping users monitor performance and make informed decisions in real time. 6. **Why do we use dashboards?** We use dashboards because it transforms raw data into actionable insights, enhancing operational efficiency and strategic planning. \- **Vivek**

From Data to Dashboards: Leveraging Snowflake Data with Draxlr

hello@draxlr.com (Draxlr) — Tue, 12 Mar 2024 00:00:00 GMT

In today's data-driven world, the ability to efficiently analyse and visualise data is crucial for making informed decisions. Two powerful tools that stand out in this landscape are Snowflake and Draxlr. In this blog post, we'll dive into how you can connect your Snowflake database to Draxlr, unlocking a new level of data analysis and visualisation capabilities. ## Introduction to Snowflake Snowflake is a cloud-based data warehousing platform that stands out for its incredible scalability, ease of use, and flexibility. It separates compute and storage, allowing businesses to scale up or down as needed without impacting performance. Snowflake supports a multitude of data formats and integrates seamlessly with many BI and analytics tools, making it a popular choice for companies looking to leverage their data. ## Why connect Snowflake to Draxlr? Draxlr is a self-serve business intelligence tool known for its user-friendly interface and powerful data visualisation capabilities. Connecting Snowflake to Draxlr allows you to: Connect your Database - Leverage Draxlr's Intuitive Interface: Easily create dynamic dashboards and reports without extensive technical knowledge. - Enhance Data Analysis: Combine Snowflake's robust data warehousing with Draxlr's analytics features for deeper insights. - Real-time Decision Making: Access and analyse real-time data to make quicker, more informed decisions. ## How to Connect Snowflake to Draxlr Here is a quick video walkthrough of how to connect Snowflake database to Draxlr and build dashboards: #### 1. Gather Your Snowflake Credentials Before you begin, ensure you have your Snowflake account details handy, including your account identifier, username, and password. You'll also need to know your Snowflake database's name and warehouse. #### 2. Connecting Snowflake with Draxlr 1. If you’re new to Draxlr, [create an account or log in](https://app.draxlr.com/register) if you already have one. 2. If you have a new account, you'll be on the connect database page. If not, click on the **Select database > + Connect a Database** button on the navbar. Select **SnowFlake** as the database type and click next. 3. Fill in the connection form with your Snowflake database details (Account ID, username, password, database name and warehouse name), then click next. 4. Next, select the database you want to connect to. If you have a single database, it will be selected by default. Click next. ![Connecting to Snowflake](/images/blogs/building-dashboards-from-snowflake-with-draxlr/images/connect.png) ### Exploring your data Once you've successfully linked your Snowflake database with Draxlr, the real fun begins. It's time to unlock the potential of your data through comprehensive exploration and analysis. Here's how you can get started: - **Dive into analytics:** Navigate to the Explore section found on the navbar. Start by selecting any table listed on the left-hand side. This action will launch Draxlr's intuitive, no-code query builder. This powerful tool is designed for users of all skill levels, allowing you to filter, group, and aggregate your data effortlessly. Whether you're looking to perform simple data exploration or complex analytical tasks, the query builder is equipped to handle your needs. ![Query builder](/images/blogs/building-dashboards-from-snowflake-with-draxlr/images/query-builder.png) - **Embrace the Power of SQL:** For those who prefer the granular control and flexibility of SQL, Draxlr has you covered. Click on the raw query button to open up the SQL editor. This is where you can directly write SQL queries to interrogate your data further. Draxlr's SQL editor is enhanced with a built-in autocomplete feature, making it incredibly user-friendly. Whether you're a SQL veteran or a novice, this feature ensures that writing queries is not only efficient but also a surprisingly enjoyable process. ### Data Visualisation and Dashboards in Snowflake Harness the full potential of Draxlr's data visualisation and dashboard capabilities to bring your data to life. 1. Once you've obtained the desired results from your data exploration in Snowflake, look for the Graph button to initiate the visualisation process. Snowflake supports an extensive array of visualisation types, from bar charts and line charts to pie charts and more, allowing you to choose the best representation for your data insights. 2. After creating your visualisation, ensure it doesn’t remain a standalone piece. Utilise the **Save query** option to preserve your query for future reference or modification. Then, take advantage of the **Add to Dashboard** feature to incorporate your visualisation into a comprehensive dashboard. ![Visualising your data](/images/blogs/building-dashboards-from-snowflake-with-draxlr/images/visualization.png) ### Conclusion Connecting Snowflake to Draxlr opens up a world of possibilities for data analysis and visualization. By following the steps outlined above, you can seamlessly integrate these two powerful tools, enhancing your ability to make data-driven decisions and gain deeper insights into your business. Embrace the power of Snowflake and Draxlr to transform your data into actionable intelligence. \- **Jaskaran Singh**

How to generate SQL query with AI

hello@draxlr.com (Draxlr) — Mon, 29 Jan 2024 00:00:00 GMT

In the ever-evolving landscape of technology, the synergy between artificial intelligence (AI) and database management has become a revolutionary force. One particularly fascinating application of **AI** in this domain is the **generation of SQL queries** — an essential skill for data professionals and developers alike. Structured Query Language (SQL) is the cornerstone of database interactions, enabling users to retrieve, manipulate, and manage data effortlessly. However, crafting efficient and precise SQL queries requires a deep understanding of database structures, relationships, and syntax. This is where AI steps in, reshaping the way we approach and execute SQL query generation. In this article, we show how you can use **Draxlr AI** to not only generate SQL for you but also use the result to **generate visual insights** that can be used to **build dashboards**. --- ## How can AI help you with SQL? 1. **Time saver**: Using A.I. saves time on writing complex SQL queries or gives you a base query on which you can build further. In reality, it's quite common not to have every nuance of SQL syntax committed to memory. Crafting complex SQL queries can be time-consuming, and searching for the right syntax can be a hassle. Artificial Intelligence simplifies the process by allowing you to express your requirements in plain text. The AI seamlessly generates SQL code tailored to your specifications, providing either the precise query you need or a foundational snippet to expand upon. 2. **No-code** solution to get answers from your data: You don't have to rely on developers to generate SQL queries so that you can get data for your business. Whether you operate a business with a specialized development team or find yourself as a solo founder with limited technical expertise, leveraging AI can significantly aid in extracting answers and insights from your data. For instance, if you require a count of customers from the Texas region, a simple inquiry such as "Provide me with a count of customers from the Texas region" is all it takes for the AI to generate the necessary SQL. Draxlr then seamlessly employs that SQL to swiftly retrieve your data within seconds. This streamlined process ensures that, regardless of your technical background, obtaining meaningful information from your dataset becomes an accessible and efficient task. --- ## Using Draxlr AI to generate SQL #### Demo of Draxlr AI 1. **Connect your database**: AI requires your database schema structure to generate accurate SQL results. Draxlr provides the option to connect to various **SQL databases** like [Postgres](/databases/postgres/), [MySQL](/databases/mysql/), [MSSQL](/databases/sql-server/), [MariaDB](/databases/mariadb/), [Supabase](/databases/supabase/), [CockroachDB](/databases/cockroachdb/), [Databricks](/databases/databricks/), BigQuery, Redshift, and more. ![How to generate SQL query with AI - Connect database](/images/blogs/how-to-generate-sql-query-with-AI/images/add-database.png) 2. Once you have connected your SQL database, click on the AI option in the navbar. Draxlr AI has a chat interface with history. ![How to generate SQL query with AI - Chat](/images/blogs/how-to-generate-sql-query-with-AI/images/AI-page.png) 3. Enter a text message of the result you expect from SQL, and hit enter. In this example, we want to get the **order count grouped by employees' name**. ![How to generate SQL query with AI - Prompt](/images/blogs/how-to-generate-sql-query-with-AI/images/prompt.png) 4. AI will generate SQL along with a brief explanation based on your message. You can view the SQL query and execute it. The result from Draxlr AI comes back as: ```SQL SELECT CONCAT(FirstName, ' ', LastName) AS EmployeeName, COUNT(Orders.OrderID) AS OrderCount FROM Employees LEFT JOIN Orders ON Employees.EmployeeID=Orders.EmployeeID GROUP BY Employees.EmployeeID, FirstName, LastName ORDER BY OrderCount DESC ``` ![How to generate SQL query with AI - Query Result](/images/blogs/how-to-generate-sql-query-with-AI/images/query-result.png) 5. You can provide further prompts to get the desired result, like if **I want to see only employees with more than 50 orders**. ![How to generate SQL query with AI - Result](/images/blogs/how-to-generate-sql-query-with-AI/images/result.png) 6. You can also **customize** the result by exploring the SQL Query. You can add changes to the SQL query or change graph attributes to fit your requirements. ![How to generate SQL query with AI - Explore](/images/blogs/how-to-generate-sql-query-with-AI/images/explore.png) 7. There is option to add the insight to Dashboard, so you can keep track of the insight. 8. Additionally, you have the flexibility to **derive insights through AI without providing explicit requirements**. For instance, if you simply seek insights for a particular table, you can utilize the **Generate Insights** feature at the table level. This option swiftly produces a myriad of visualizations and statistics within seconds, offering a quick and effortless way to gain a comprehensive understanding of your data. With the help of AI, you can not only get instant answers from your data but also **visualise them as charts within minutes**. This saves a lot of back-and-forth with your development team, who otherwise can now focus on more important business features. This is especially useful for people in the sales or marketing domain, or CXOs who need data to drive important business decisions. --- ## Building dashboards with AI-generated SQL queries You can add insights generated via AI to a dashboard that will keep you updated about your business performance. Here is a **sample dashboard built with the help of AI**: ![How to generate SQL query with AI - Sample Dashboard built with AI](/images/blogs/how-to-generate-sql-query-with-AI/images/dashboard.png) With Draxlr's dashboard feature, you will utilise features like: - **Embedding Dashboard** into your customer-facing application with the current user context. - **Getting alerts** via email or Slack to stay updated on crucial business data changes. - **Share dashboards** with your team members or stockholders. - Get a **summary email of the dashboard** every day. - Use the live mode to stream the **dashboard on TV**. --- ## Conclusion In summary, leveraging AI for SQL query generation streamlines development, enhancing efficiency and accuracy. This automated approach reduces human error, saves time, and adapts to evolving database structures. As AI continues to advance, integrating it into query generation promises ongoing innovation, allowing developers to focus on high-level tasks while intelligent systems handle intricate query creation. This evolution represents a significant leap forward in the intersection of AI and database management, providing organisations with a powerful tool for agile and data-driven decision-making. \- **Vivek**

How to build dashboards from Nhost database

hello@draxlr.com (Draxlr) — Thu, 04 Jan 2024 00:00:00 GMT

In today's data-driven world, the ability to effectively analyse and glean insights from your data is crucial. For users of Nhost, integrating with Draxlr, a user-friendly drag-and-drop query builder and data visualisation tool, can be a game-changer. This blog post will guide you through connecting your Nhost database to Draxlr and performing analytics on it. ## Why integrate Nhost with Draxlr? **Nhost:** It offers a secure and scalable backend with GraphQL, making it a preferred choice for developers looking for an efficient database solution. **Draxlr:** This is an easy-to-use, self-service BI tool. It provides an intuitive interface for running queries, creating dashboards, and sharing insights with your team. Integrating Nhost with Draxlr allows you and your team to make informed decisions with data stored on Nhost's and Draxlr's robust analytics capabilities. Here is a quick video walkthrough of how to connect and build a dashboard from Nhost database data in Draxlr: ## Step-by-Step Guide to Integration ### Preparing Your Nhost Database Before connecting to Draxlr, ensure that your Nhost database is properly set up and accessible. 1. Make sure your Nhost database is operational and can be accessed externally. 2. Note down your database's host, port, username, and password. You'll need these for Draxlr. These can be found in your Nhost dashboard settings under the **Database > Connection info** section. ![Get Nhost credentials](/images/blogs/building-dashboards-from-nhost-database/images/nhost-connection.png) ### Connecting to Draxlr With your Nhost details in hand, it's time to set up Draxlr: 1. If you’re new to Draxlr, create an account or log in if you already have one. 2. If you have a new account, you'll be on the connect database page. If not, click on the **Select database > + Connect a Database** button on the navbar. Select **PostgreSQL** as the database type and click next. 3. Fill in the connection form with your Nhost database details (host, port, username, password, and database name), then click next. 4. Next, select the schema you want to connect to. If you have a single schema, it will be selected by default. Click next. ### Exploring your data With the connection established, you’re ready to dive into analytics: 1. Go to the Explore section on the navbar, then click on any of the tables on the left-hand side to open the no-code query builder. You can filter, group, and aggregate your data using the query builder. 2. You can also click on the raw query button to start writing SQL queries to explore your data. Draxlr's SQL editor has a built-in autocomplete feature that makes writing queries a breeze. ### Data Visualisation and Dashboards Draxlr provides powerful visualisation and dashboard capabilities. 1. After you've gotten the result of your exploration, you can click on the Graph button to visualise your data. Draxlr supports a wide range of visualisations, including bar charts, line charts, pie charts, and more. 2. Once you've created a visualisation, you can save your query using the **Save query** button. You can use the **Add to Dashboard** button to add your visualisation to a dashboard. ![Visualising your data](/images/blogs/building-dashboards-from-nhost-database/images/nhost-graph.png) ## Monitoring Your Data Monitoring allows you to make data-driven decisions quickly and steer your business in the right direction. You can use Draxlr's Slack and email alert systems to get alerts on your data's key metrics. Remember, while technology provides the tools, it's your curiosity and questions that will drive real insights. Happy data exploring! \- **Jaskaran Singh**

How to build Dashboards from Cockroach DB data

hello@draxlr.com (Draxlr) — Mon, 30 Oct 2023 00:00:00 GMT

[Cockroach](https://www.cockroachlabs.com/) is a distributed SQL database that helps you run your database at scale, so you don't have to worry about your database's performance for your users across the world. Key highlights of Cockroach DB: - Scale indefinitely without the need for painful sharding. - No outages or downtime. - By rapidly deploying anywhere across the world, you can ensure low-latency data access and compliance with data regulations. - It allows you to achieve maximum fault tolerance by deploying across different clouds. - Use SQL to run your queries. - Awesome support from a team of experts. --- ## Using Draxlr to Build Dashboards from Cockroach Data > Draxlr is a no-code business intelligence solution that allows you to create dashboards, receive data alerts, and embed dashboards on customer-facing websites. > Draxlr connects with the Cockroach DB database and helps you get data answers without writing SQL. Here is a quick video walkthrough of how to build a dashboard from **Cockroach DB data** in **Draxlr**: Ok, let's go! ### Connecting Cockroach DB with Draxlr In Cockroach DB, you can have multiple clusters, and each cluster can have multiple databases. 1. Login to Draxlr, visit the [Add Database page](https://app.draxlr.com/add-database), and select **CockroachDB**. ![Draxlr Select Cockroach](/images/blogs/how-to-build-dashboards-from-cockroach-db-data/images/select_cockroach.png) 2. Select the source where your database is hosted. ![Draxlr Select Cockroach DB Source](/images/blogs/how-to-build-dashboards-from-cockroach-db-data/images/select_source.png) 3. You can now enter the database credentials of CockroachDB into Draxlr. - Let's consider that your Cockroach DB is hosted on CockroachLabs. You can visit their site and select the database that you want to connect with. - Click on the "Connect" button on the database; it will open a pop-up with options to connect to your database. ![Draxlr Connect Cockroach DB](/images/blogs/how-to-build-dashboards-from-cockroach-db-data/images/connect_cockroach.png) On the pop-up, you can select "Parameters only" in the "Select option/language" field, and you can see the required credentials for the database. ![Draxlr Connection String Cockroach DB](/images/blogs/how-to-build-dashboards-from-cockroach-db-data/images/show_connection_string.png) - You can now copy the credentials from CockroachLabs UI to Draxlr and enter the database password as well. Click on "Next" once you are done. ![Draxlr Enter Credentials Cockroach](/images/blogs/how-to-build-dashboards-from-cockroach-db-data/images/enter_credentials.png) 4. You can now select the database schema; by default, it is **public**. ![Draxlr Set Cockroach DB Schema](/images/blogs/how-to-build-dashboards-from-cockroach-db-data/images/set_schema.png) 5. Now you can see the **list of tables**, and you can start exploring your data. ![Draxlr List Tables](/images/blogs/how-to-build-dashboards-from-cockroach-db-data/images/list_tables.png) --- ### Generating queries and visualising the data After connecting Cockroach DB with Draxlr, you can build dashboards from your Cockroach DB data. 1. You can generate data with Draxlr's no-code query builder by using **Filters, Joins, Sort, Summarise, and Group by** options. 2. When you get the desired data output, you may **visualise** it using a variety of graph options, such as bar, pie, line, area, and others. 3. For example, here we showcase two queries that are built using the query builder. 4. Products with an order of more than 40 - We select the "orders" table and join the "order_details" and "products" tables. We then group the results by product's name and summarise by count of products. - We can also filter the products that have more than X orders. ![Draxlr Cockroach DB Query - Products with order more than 40](/images/blogs/how-to-build-dashboards-from-cockroach-db-data/images/query_1.png) 5. Suppliers count - We select the "suppliers" table and perform a count on the table. We then show the result in the form of a gauge chart. ![Draxlr Cockroach DB Query - Suppliers count](/images/blogs/how-to-build-dashboards-from-cockroach-db-data/images/query_2.png) 6. After you've finished configuring the graphs, you can save the query and add it to a dashboard. 7. **That's it; your dashboard is complete!** ![Draxlr Cockroach - Dashboard View](/images/blogs/how-to-build-dashboards-from-cockroach-db-data/images/dashboard_view.png) 8. You can **share the dashboard** with your team, **embed the dashboard** in your customer-facing application, and even set a schedule to receive a **snapshot of the dashboard** every day. 9. You can also set Slack or email alerts that trigger alerts when data changes in the queries that you have created. --- You can make smarter business decisions by using dashboards created from your data. You can also set up data alerts to get notified when your data changes by email or Slack. \- **Vivek**

How to set Slack alerts when your business data changes

hello@draxlr.com (Draxlr) — Tue, 18 Apr 2023 00:00:00 GMT

As a business owner, you've got a lot on your plate, from juggling a myriad of responsibilities to making tough calls left and right. But let's be real: What's the point of making decisions based on events that happened a week ago? It's like trying to drive forward while looking in the rearview mirror. If you're not keeping up with the latest developments, you might as well be driving blindfolded. And we all know that's not a good idea. That's why it's essential to stay in the loop in real-time. Of course, you can't be glued to your screen all day. So Slack notifications are here to save the day (and your sanity). ## The Value of Timely Business Updates through Slack Alerts 1. **Data on finger tips:** With Slack alerts, accessing crucial business data has never been easier. As a business owner, it's easy to access crucial metrics like the daily install count by simply reviewing the alerts that have been established. By adopting this approach, you can optimise your time and efforts while staying informed about critical data that can influence your business decisions. 2. **Recognise anomalies:** Suppose you typically receive a certain number of sign-ups per day, and suddenly that number drops by half. This drop in sign-ups is a clear indication that there is a software flaw in your product, and by identifying it through timely Slack notifications, you can quickly take corrective measures before the situation worsens. 3. **Power your decision-making with timely insights:** Alerts can provide crucial insights into customer behaviour and help you make better decisions faster. For instance, if you notice that a specific feature in your product is being used by the majority of your paying customers, it might be worthwhile to consider making that feature available to your free users as well. Without these timely alerts, you may miss out on valuable insights and delay decision-making, which can hinder your business's growth. 4. **Ensures team motivation:** If you provide your team members with access to Slack alerts, they'll be aware of the progress being made, which can inspire them to work with even more enthusiasm towards achieving the next milestone. 5. **Track progress in real-time:** When you track feature usage through alerts, you can identify progress and successes that might otherwise go unnoticed. Celebrating these milestones with your team can boost morale and help you determine where to focus your efforts. Whether you need to make further improvements to a successful feature or shift your focus to a different area of the product, measuring progress through timely updates can help you make informed decisions. But wait, there's more! I've already given you plenty of reasons why setting up Slack notifications is a smart move for any business owner. In the following sections, I'll show you two different ways to set up these alerts. The first option involves coding, while the second option is much simpler and more convenient, using Draxlr. --- ## How to setup a Slack alert via code? Suppose you want to keep track of the daily sign-ups for your app and only receive Slack notifications when the total number of users changes. Let's see some examples of how to achieve these in different frameworks. You will need a Slack Webhook URL; you can follow [this guide](https://api.slack.com/messaging/webhooks) to generate one. ### Ruby on Rails In Ruby, you can accomplish this by using a gem called [slack-notifier](https://github.com/slack-notifier/slack-notifier). This gem provides an easy-to-use wrapper for posting messages to Slack channels. **Steps to follow** 1. Add **slack-notifier** to your Gemfile and run `bundle install`. 2. Add a callback to the user model when the user is created or destroyed. ```ruby # Gemfile gem "slack-notifier" # app/models/user.rb ... after_create :send_slack_notification after_destroy :send_slack_notification def send_slack_notification Slack::Notifier.new(slack_webhook_url).ping("User count changed to #{User.count}") end ... ``` ### ExpressJS with Mongoose (NodeJS) You can accomplish this by using a package called [slack-notify](https://www.npmjs.com/package/slack-notify). **Steps to follow** 1. Add **slack-notify** to your package.json and run `npm install`. 2. Add a method to the user model and call it when the user is created or destroyed. ```javascript # User.js ... import SlackNotify from 'slack-notify'; const SLACK_WEBHOOK_URL = ''; const slack = SlackNotify(SLACK_WEBHOOK_URL); const userSchema = new mongoose.Schema({ // fields }) // user.sendUserCountNotification() userSchema.methods.sendUserCountNotification = async function () { const userCount = await User.countDocuments() await slack.send(`User count changed to ${userCount}`) } ... ``` ### Django You can accomplish this by using a package called [slack-sdk](https://github.com/slackapi/python-slack-sdk). **Steps to follow** 1. Add **slack_sdk** to your project requirements using `pip install slack_sdk`. 2. Add a signal listener for user object creation or deletion. ```python # /signals.py from django.dispatch import receiver from django.db.models.signals import post_save, post_delete from slack_sdk.web.client import WebClient from userman.models import User client = WebClient("") @receiver(post_save, sender=User, dispatch_uid="handle_user_create_signal") def handle_user_create(sender, instance, **kwargs): notify_user_count_slack() @receiver(post_delete, sender=User, dispatch_uid="handle_user_delete_signal") def handle_user_delete(sender, instance, **kwargs): notify_user_count_slack() def notify_user_count_slack(): user_count = User.objects.count() message = f"User count changed to {user_count}" client.chat_postMessage(channel="", text=message) ``` --- ## How to setup Slack alerts via Draxlr 1. Connect your Draxlr account with the Slack workspace by going to [Settings > Organization Settings](https://app.draxlr.com/organization-settings). 2. If you're looking to set up a Slack alert in a private channel, don't forget to give a warm welcome to your newest team member - Draxlr! Simply invite Draxlr to the channel by mentioning **@Draxlr**, and let it handle the rest. 3. You can now generate the same query as the usecase mentioned above using Draxlr. ![Draxlr generate query](/images/blogs/how-to-set-slack-alerts-when-your-business-data-changes/images/5.png) 4. Next, head over to the "Integrations" option in the navigation bar. ![Draxlr create alert](/images/blogs/how-to-set-slack-alerts-when-your-business-data-changes/images/1.png) 5. Enter the channel name where you want the notifications to appear. ![Draxlr test alert](/images/blogs/how-to-set-slack-alerts-when-your-business-data-changes/images/6.png) 6. Once it's done, you can now simply select the query that you want to receive notifications for and specify the frequency of alerts that you desire. ![Draxlr slack alert details](/images/blogs/how-to-set-slack-alerts-when-your-business-data-changes/images/7.png) 7. You will receive Slack notifications with a format similar to this. ![Draxlr slack alert notifications](/images/blogs/how-to-set-slack-alerts-when-your-business-data-changes/images/2.png) With just a few clicks, you can now receive important updates in real-time through Slack alerts. It's the perfect solution for business owners who need to stay informed about critical events without constantly checking. So why wait? Sign up for Draxlr today and experience the convenience of hassle-free Slack alerts for yourself!

5 KPIs Every Customer Dashboard Must Have: How to Build One

hello@draxlr.com (Draxlr) — Thu, 12 Jan 2023 00:00:00 GMT

A customer dashboard has the power to provide pivotal insights into a business, allowing decision-makers to adopt practices that will boost the growth of the company. Customer dashboards help you understand your customers’ activities in a new light, ultimately facilitating data-driven decisions. ![Graph Option](/images/blogs/top-5-kpis-every-customer-dashboard-must-have/images/kpi_dashboard.png) --- ## Let’s begin with understanding customer dashboards. Every business, irrespective of its size, has numerous data points. Using a customer dashboard, a business can gather this data from several sources and consolidate it in one location to get a broad picture of their customers’ experiences. Customer dashboards are helpful for practically any business seeking to benefit from its marketing efforts and data. If you use numerous marketing channels, you can integrate them into a unified dashboard so that all of your key performance indicators are in one place. Individuals can then easily assess those wide ranges of KPIs and metrics. This places your entire team on the same page and helps them understand what's working and what's not. **However, before we dive into the 5 KPIs every customer dashboard should have, let’s have a look at one of the most dependable ways of building a modern-day dashboard.** --- ## Draxlr: Build dashboards that make a difference! Draxlr is a platform that helps you build user-friendly dashboards without the hassle of writing code and subsequently empowers you to leverage untapped data points and then visualise as well as monitor them. Simply put, it makes visualisation, analysis, sharing, and monitoring of data easy with live dashboards. **Unlock the Hidden Potential of Your Business with Draxlr:** - Quickly build a dashboard from saved essential queries; add queries directly as items. - Easily edit customer dashboard elements such as labels, visuals, etc. - Unlimited number of dashboards to address the needs of every department and project - Leverage data monitoring features, including data modification alerts and API requests. - Use a custom periodic refresh to synchronise the dashboard with your database. - Analyse data through interactive visualization; scroll across, zoom in, and download as PNGs. - Get an instant overview of all your business metrics, arranged together on a single board. **Now that we have established how to build an effective dashboard, let’s understand the top five KPIs you must have in your Draxlr customer dashboard:** ### 1. Web Performance A website is the digital face of your business. Every marketing campaign you run usually aims at increasing web traffic. Directing your potential customers to land on your website is just half the work; the second half is to analyse your website’s performance and conversion rates. A few indicators to include could be **bounce rate, conversion rate, returning users, unique visitors, pages browsed, total visits, total sessions, and sign-ups**. These insights can be further utilised to engage customers proactively and boost conversion rates. - **The percentage of visitors that visit your website but leave before exploring a second page is known as the bounce rate.** - **The conversion rate of your website measures the percentage of visitors who perform a desired call to action, such as making a purchase, completing a survey, subscribing to a service, or taking a free trial.** - **The total number of times people have visited your website is referred to as a visit. The number of unique visitors will reveal how many new people have visited your website.** ### 2. Net Promoter Score (NPS) The Net Promoter Score is a metric for determining customer loyalty and the likelihood of customers recommending your goods or services. It is calculated by asking a simple question: **_On a scale of 1–10, how likely are you to suggest us to a friend?_** Customers respond to this query by critically reflecting on their overall interaction with your brand; this is one more reason why you should focus on building positive customer experiences. In today's trend-obsessed culture, companies all over the world use NPS as a key performance indicator. **After all, the best kind of marketing is the one done by satisfied customers!** ### 3. Email marketing KPIs Email marketing is far from dead. It is still one of the most effective methods for contacting prospects and increasing sales. If done correctly, it can successfully assist you in achieving your business goals. And that’s all the more reason why it must be a part of the customer dashboard and not something that continues silently on the side. The major performance indicators for email campaigns are open rate, bounce rate, conversion rate, unsubscribe rate, and click-through rate. ### 4. Customer retention **_Are your customers coming back to make another purchase? Or take that desired action? Are you fostering long-term relationships with your customers?_** Customer retention KPIs need to be integrated into the customer dashboard if you want to know how well your company is doing at retaining customers in the long run and generating recurring revenue from them. The benefits of strong customer retention go beyond repeated purchases; this performance indicator reflects the loyalty of your customers towards your brand. ### 5. Customer Satisfaction Score Customer satisfaction scores can demonstrate how satisfied customers are with the level of assistance provided, whether they bought a physical product like a shirt or a service like a [video editor](https://www.veed.io/tools/video-editor). You must not only market; you need to deliver as well! And with this KPI on your customer dashboard, you can measure how well your business has delivered on its promise. You can ask a direct question to determine this, such as, **_On a scale of 1-10, how satisfied are you with the recent purchase, service, or support?_** You must thoroughly assess the negative responses, analyse what went wrong, and work on improving them. --- ### Customer Dashboard: Need of the Hour Building a successful business in this digital era involves innumerable factors, including social networking, strengthening the client experience, maintaining client retention, and much more. The need for KPIs becomes imperative and irreplaceable. And for you to leverage those KPIs, a unified customer dashboard is simply paramount.

Postgres hstore vs. JSON: comparing different unstructured data types

hello@draxlr.com (Draxlr) — Fri, 25 Nov 2022 00:00:00 GMT

HStore and JSON, or JSONB, are two of the most popular unstructured data types supported in PostgreSQL databases, with HStore being one of the oldest ones introduced in Postgres 8.3, whereas support for JSON data types was added later in PostgreSQL 9.2. Over the years, Postgres has evolved to support wider use cases and has remained one of the best (if not the best) databases of choice to build apps. In this article, we’ll dive into various unstructured data types supported in Postgres and what’s common and what’s the difference between them. ### HStore Being the first unstructured data type supported in Postgres, HStore came with some limitations. First, it can only store data in string format. You cannot store numbers, booleans, or other types of values. Everything is stored as a string. Another limitation it has is that hstore in Postgres cannot store nested key-value pairs; you are only limited to one level. ### JSON JSON stands for Javascript Object Notation. It’s an open format for storing key-value pairs. JSON support was added to Postgres in version 9.2. With that, you can store full JSON objects in your database tables. The key-value pair data can have different data types like string, number, and boolean. Not only that, you can also store nested key-value pairs with JSON data type support in Postgres. In Postgres, JSON data is stored as an exact copy of the input text; that way, inserting the data into your tables is fast, but reading is slow. ### JSONB JSONB is an improvement over the traditional JSON data type, with the difference being the way JSON data is stored in JSONB. JSONB in Postgres stores the binary representation of the data. That helps it overcome the performance issues of the JSON data type. Since the data is stored in a decomposed binary format, that makes it significantly faster to read and process since no parsing is required, and also slower to input due to the added step of data conversion. Another advantage it has over the JSON type is that it also supports indexing. --- ## There’s more: JSONPath With the increase in usage and popularity of the JSON and JSONB fields, the 2016 SQL standard added a new standard/path language for navigating and traversing JSON data in Postgres. It’s a new approach to searching JSON data. It’s very similar to XPath for XML data, and it was added in PostgreSQL 12. --- So, these are all the ways to store unstructured data in a Postgres database. With the increase in complexity of the apps, the traditional SQL databases evolved to support these new data types and have become more versatile and an even better database of choice. I hope this article helped you understand the basic differences between the various unstructured data types supported in the PostgreSQL database. I will recommend checking out the links below to learn more about them. --- Postgres Data Types: [https://www.postgresql.org/docs/15/datatype.html](https://www.postgresql.org/docs/15/datatype.html) Postgres HStore Docs: [https://www.postgresql.org/docs/15/hstore.html](https://www.postgresql.org/docs/15/hstore.html) Postgres JSON Functions Docs: [https://www.postgresql.org/docs/15/functions-json.html](https://www.postgresql.org/docs/15/functions-json.html) \- [Jitendra Nirnejak](https://nirnejak.com/)

Postgres HStore data type and how to use it

hello@draxlr.com (Draxlr) — Wed, 16 Nov 2022 00:00:00 GMT

Hstore is one of the first unstructured data types supported in the PostgreSQL database. It was added in Postgres 8.3 as a way to help you store and access key-value pairs of data in your database. In terms of functionality, hstore is a bit limited compared to something like JSON. Hstore in Postgres only lets you store strings; it does not provide nesting. It’s sort of like a flat key-value pair datatype. If you're working with PostgreSQL and need powerful data exploration and insights, [Draxlr](https://www.draxlr.com/) simplifies querying and visualization without the hassle of complex SQL queries. Connect your Database --- ### Enable the PostgreSQL hstore extension. In some cases, the hstore might not be enabled for your Postgres database. To enable it, run the following command below: ```sql CREATE EXTENSION hstore; ``` --- ## Creating and Adding HStore Fields ### Create a table with the hstore data type. To create an hstore field, you just need to define the field as an `hstore` type while creating your table. ```sql CREATE TABLE products ( id serial primary key, title VARCHAR (255), attr hstore ); ``` ### Insert data into the PostgreSQL hstore column. To insert any data into the hstore field. You pass your key-value pairs as a string. With the ⇒ symbol defining the relation between the key and the value. Please refer to the example below to understand it. ```sql INSERT INTO books (title, attr) VALUES ( 'iPhone 14 Pro', '"type" => "phone", "company" => "apple", "price" => "999", ); ``` --- ## Update Since you can have multiple values or attributes inside a single field, the update option for the Postgres hstore fields needs more options. You may not always want to update the entire field or just parts of it. For that, a few operators and options let you manipulate your Postgres Hstore data. ### Add key-value pairs to existing rows. To add another key-value pair to an existing hstore field in Postgres, you can use the `||` operator to add the key-value pair and then use the `::` operator to convert the result into an hstore type. ```sql UPDATE products SET attr = attr || '"storage"=>"128"' :: hstore; ``` ### Update the existing key-value pair. To update an existing key-value pair, use a similar approach; just use the existing key, and it will automatically replace the old value with the new one. ```sql UPDATE products SET attr = attr || '"price"=>"1099"' :: hstore; ``` ### Remove the existing key-value pair. To remove a key from your Postgres hstore field, you can use the delete method provided by PostgreSQL. In the first argument, pass the hstore field and then the attribute or key you want to remove. ```sql UPDATE products SET attr = delete(attr, 'store'); ``` --- ## Query Since the hstore field can contain unstructured data and key-value pairs, we need some special operators to access this data. Let’s go through some of them to understand the different use cases and queries you can perform with them. ### **Query data from an hstore column** To get the value of the field, you can perform a simple query and mention the field. It’ll give you the raw data from the field. ```sql SELECT attr FROM products; ``` ### **Query value of a specific key - `->` operator** To get the value of a specific key, you can use the `->` operator; it’ll give you the value of that exact key. ```sql SELECT attr -> 'company' AS company FROM products; ``` ### Use value in the WHERE clause. The `->` operator can be used inside the WHERE clause as well. ```sql SELECT title, attr -> 'price' AS price FROM products WHERE attr -> 'price' < '1000'; ``` ### **Check for a specific key in the hstore column - `?` Operator** To know if a key exists inside an hstore field. You can use the`?` operator and mention the key you’re checking for. If the key exists, the expression will be true. ```sql SELECT title, attr->'company' as company, attr FROM products WHERE attr ? 'company'; ``` ### **Query rows that contain multiple specified keys -** `?&` operator To check against a set of keys, you can build an array of the keys and check against the hstore field with the `?&` operator. If all the keys in the array are in the Postgres hstore field then only it’ll be true. ```sql SELECT title FROM products WHERE attr ?& ARRAY [ 'price', 'company' ]; ``` ### Check for a key-value pair - `@>` operator. To check if a key-value pair exists in an hstore field, you can use the `@>` operator. ```sql SELECT title FROM products WHERE attr @> '"company"=>"apple"' :: hstore; ``` ### **Get all keys from an hstore column** To get all the keys present in a Postgres hstore field, you can use the `akeys` method provided by Postgres and pass the hstore field as an argument. ```sql SELECT akeys (attr) FROM products; ``` ### **Get all values from an hstore column** To get all the values present in a Postgres hstore field, you can use the `avals` method provided by Postgres and pass the hstore field as an argument. ```sql SELECT avals (attr) FROM products; ``` ### Convert hstore data to JSON. If you need to convert your hstore data to JSON, you can use the `hstore_to_json` method, which will convert the hstore key-value pairs into a JSON field. ```sql SELECT title, hstore_to_json (attr) json FROM products; ``` --- I hope you were able to find value in this blog and understand the basics of the hstore data type in the Postgres database. I will recommend you check the Postgres documentation to learn more about it. I’m also adding the link to all the supported hstore operators and functions; you can check that out too. ### Useful Links Postgres hstore documentation: [https://www.postgresql.org/docs/current/hstore.html](https://www.postgresql.org/docs/current/hstore.html) Postgres hstore operators and functions: [https://www.postgresql.org/docs/current/hstore.html#id-1.11.7.27.6](https://www.postgresql.org/docs/current/hstore.html#id-1.11.7.27.6) \- [Jitendra Nirnejak](https://nirnejak.com/)

YugabyteDB or CockroachDB: which is better distributed SQL database?

hello@draxlr.com (Draxlr) — Fri, 04 Nov 2022 00:00:00 GMT

For any business to flourish, its database management system has to be durable, reliable, and consistent. From storing customer, order, and other details in an e-commerce business to storing investment and transaction details in a fintech company, data is the most vital point for any organization. With database management done correctly, the other crucial aspects of the business, like data analytics, sales, marketing, and ad campaigns, all of which heavily rely on data, simplify by a large amount, which directly impacts the growth and revenue of the company. NoSQL and SQL databases are the two major categories of databases to choose from, and they have their own pros and cons. Choosing the right kind of database, which is currently a tradeoff between scalability and consistency, is important. In a relational SQL database, scaling to keep up with large volumes of data is a weak point, while in a NoSQL database, data integrity and consistency are compromised. This is where a need arises for a third alternative that is scalable as well as consistent, and distributed SQL fits into this role quite well. A distributed SQL is a relational database that is deployed on a cluster of network servers, which are also referred to as nodes. Multiple instances of a database come together in distributed SQL, where each instance is solely responsible for the storage and maintenance of a particular dataset. The two most popular choices of database in the cloud-native distributed domain are [CockroachDB](https://www.cockroachlabs.com/docs/cockroachcloud/quickstart.html) and [YugabyteDB](https://www.yugabyte.com/yugabytedb/). For new businesses, CockroachDB vs. YugabyteDB can be an overwhelming choice, and hence, here is a brief introduction of both along with their salient features. ## YugabyteDB ![YugabyteDB](/images/blogs/cockroachdb-vs-yugabytedb-better-distributed-sql-databases/images/yb.png) Designed around the CAP theorem, this open-source cloud-native distributed SQL is compatible with PostgreSQL, which is currently one of the most popular database choices for relational databases. It also implies that any application that has PostgreSQL integration can be easily migrated to YugabyteDB. YugabyteDB brings a lot to the table, namely high availability, scalability, and consistency. For high availability, YugabyteDB makes use of replicas, whereas to achieve scalability, it uses partitions, which are also called tablets. Two layers primarily constitute the building blocks of YugabyteDB: DocDB, which is the storage engine and is abstracted from users, and the Query Layer, which is responsible for query execution and optimisation. **Features of YugabyteDB** ![Features of YugabyteDB](/images/blogs/cockroachdb-vs-yugabytedb-better-distributed-sql-databases/images/YB-Homepage-Diagrams-R4_1.gif) 1. **Easy Compatibility with Postgres** The first decision that any business wanting to migrate to YugabyteDB has to face is whether the transition will be smooth, and YugabyteDB’s ability to reuse any application’s PostgreSQL query layer is one of its strongest points. PostgreSQL is a popular choice of database in relational database systems, and hence YugabyteDB’s support of PostgreSQL makes it stand out. 2. **High Scalability** Unlike SQL databases, YugabyteDB claims to scale up to 300,000 database transactions per second, which means that the database has the capability to scale up and down according to the requirements of the application. The performance of the database can be increased by adding nodes. It can also store a maximum of 100 TB of data and facilitate concurrent connections, all of which make it a popular choice for distributed SQL databases. 3. **High Availability** Common issues faced with databases are failed backups and unexpected downtime. YugabyteDB is continuously available, which is possible through distributed backups and occasional data synchronisation activities across the nodes. The maintenance operations are also run without downtime, which is also a remarkable feature. Multi-region deployments and replication also ensure availability. 4. **Hybrid functionality and multi-cloud capabilities** With YugabyteDB, the application can be deployed anywhere, i.e., in private, public, or hybrid cloud environments. Giving the application the freedom to be run anywhere reduces costs and avoids vendor lock-in. ## CockroachDB ![CockroachDB](/images/blogs/cockroachdb-vs-yugabytedb-better-distributed-sql-databases/images/cd_main.png) Founded in 2015, this database gets its name from the resilience of cockroaches, who are impossible to take down, hence the name CockroachDB. Based on a strongly consistent key-value store, this consistently distributed database also follows the ACID properties (Atomicity, Consistency, Isolation, and Durability). It provides a SQL API for querying and performing other operations on data. **Features of CockroachDB** 1. **Geographic Zone Configurations** In setting up CockroachDB nodes, the control of where the data is stored is with the database administrator or the developer. The nodes or cluster of network servers in CockroachDB are tagged with attributes and hierarchical localities, which makes them easy to identify. It also ensures zero downtime for data migrations. 2. **Follower Reads (limited to the Enterprise version)** In this feature, any replica of a range can attend to a read request, which should, however, have been sufficient in the past. This is achieved by using the AS OF SYSTEM TIME clause in the CockroachDB. Through follower reads, the latency of the read operations goes down by a huge margin, which also promotes increased throughput. Here, the replica, which is geographically situated closest to the gateway, is allowed to serve the read request, considering it contains slightly historical data—at least 48 seconds in the past. 3. **Ability to use semi-structured data** The latest version of CockroachDB (>2.0) supports JSON and JSONB, which are semi-structured data types, which is one of the benefits provided by NoSQL databases. Many applications require the functionality of databases that can intuitively map to OOP languages without having to model the data in a completely normalised way. Moreover, by adding general inverted indexes to the JSON columns, the query operations become much faster, making the application run smoother and quicker. 4. **Better backup and restoration facilities** As the name itself suggests, CockroachDB is built for resilience, but that does not mean that the backups and restore facilities can be strung by a loose thread. Low-level node failures are not to be worried about, and the backup mechanism is built for situations wherein a cluster loses most of its nodes or other such situations. A full or incremental backup of an application’s database can be created through CockroachDB’s Backup [statement](https://www.cockroachlabs.com/docs/v20.2/sql-statements). The restore statement restores the cluster schemas from any cloud-based or local storage. The above-listed properties of CockroachDB and YugabyteDB can be present in both database choices and are not limited to any one database. Detailed comparisons of the performance of each database, along with comparing them alongside each other, i.e., CockroachDB vs. YugabyteDB, have been released by both companies. [Here](https://www.yugabyte.com/blog/yugabytedb-vs-cockroachdb-bringing-truth-to-performance-benchmark-claims-part-1/) are YugabyteDB’s two cents on the CockroachDB vs. YugabyteDB battle, whereas [here](https://www.cockroachlabs.com/guides/cockroach-vs-yugabyte/) can see CockroachDB’s take on it. The database you choose for your application can be either of the two, depending on many factors, primarily budget, business, scale, and security requirements. ## Setting up on Draxlr CockroachDB vs. YugabyteDB is a decision you would have to make, but Draxlr is here to make the next steps easy for you. No matter which of the two you choose, Draxlr supports both distributed database systems, which will ease your data analytics side of the business by making complex dashboards and setting up alerts on the data in no time. To begin with, you will need to register on Draxlr here: [https://app.draxlr.com/](https://app.draxlr.com/), after which you can select the manual setup option. This will then navigate you to the “Add database” page, where you can select either CockroachDB or YugabyteDB. Before moving forward, please note that your database should be allowed to accept connections from Draxlr’s IP listed on the connections page. ![Connect database with Draxlr](/images/blogs/cockroachdb-vs-yugabytedb-better-distributed-sql-databases/images/connect-database.png) **Setting up CockroachDB with Draxlr** ![Draxlr - Select Hosting Provider](/images/blogs/cockroachdb-vs-yugabytedb-better-distributed-sql-databases/images/Screenshot_from_2022-10-30_14-22-48.png) On the add database page, select CockroachDB, and then click on the Next button on the top right of the section. It will then ask you where your data is hosted, depending on whether you select Cockroach Labs or Other. You can provide connection details like your host, port, user, password, and database name. ![Draxlr - Add Database Credentials](/images/blogs/cockroachdb-vs-yugabytedb-better-distributed-sql-databases/images/Screenshot_from_2022-10-30_14-22-58.png) You can also provide an SSH host, port, user, and key file to be able to connect to the database via SSH. Once done, you can access your data via Draxlr. For more details on how to build dashboards from Cockroach DB data, you can check out [this article](/blogs/how-to-build-dashboards-from-cockroach-db-data/). **Setting up YugabyteDB with Draxlr** ![Draxlr - Whitelist Draxlr IP for YugabyteDB](/images/blogs/cockroachdb-vs-yugabytedb-better-distributed-sql-databases/images/whitelist-domain.png) If you want to move forward with YugabyteDB, click on the YugabyteDB icon, and then click on the Next button on the top right of the section. You will see a public IP address belonging to Draxlr, which should be added as a trusted source for your database. ![Draxlr - Add Database Credentials](/images/blogs/cockroachdb-vs-yugabytedb-better-distributed-sql-databases/images/Screenshot_from_2022-10-30_14-22-30.png) Once that is done, you can fill in the connection details like database host, port, user, password, and database name. You can also provide an SSH host, port, user, and key file to be able to connect to the database via SSH. That’s it! --- Choosing the correct database for your application can be an overwhelming decision, and hence, you should take your time and go through all the available options with the utmost attention. CockroachDB vs. YugabyteDB is a choice that only you can make for your application, but Draxlr is right here to take care of the next steps. From dashboard creation to slack and email alerting to supporting many database choices, Draxlr has got you covered! Happy indexing and transacting! (After choosing the correct database, of course.)

Common Table Expressions in MySQL: When and How to Use Them

hello@draxlr.com (Draxlr) — Wed, 12 Oct 2022 00:00:00 GMT

## What are CTEs? CTE is also known as the WITH clause or sub-query factoring. It is a named temporary result set that is used to manipulate the complex sub-query data. In simple terms, the code can be made reusable if the already calculated values are stored in a temporary space, eliminating the need to calculate them again. It can be used with SELECT, INSERT, UPDATE, DELETE, etc. Also, a CTE can reference other CTEs that are part of the same WITH clause, but those CTEs should be defined earlier. --- ## MySQL CTE syntax The structure of CTE includes: - name: name of the CTE to store the temporary result - column list: column to store the values in the CTE - query to define the CTE; the result of this query is stored in the CTE. CTE is always defined with the `WITH` keyword, followed by the CTE name and column name. Once the CTE has been declared, it can be referenced to make use of the results. The execution scope of CTE exists within the statement in which it is used. ```sql WITH cte_name (column_list) AS ( query ) SELECT * FROM cte_name; ``` The number of columns in the CTE arguments must be equal to the number of columns in the query. The query columns that define the CTE will be used if we haven't declared the columns in the CTE parameters. --- ## MySQL CTE example Let us assume ABC Company sells goods to its customers online. Here is the `orders` table representing their weekly sales. ![Orders Table](/images/blogs/common-table-expressions-in-mysql/images/1.jpeg) We have the order and the orderDetails table, with the order having basic information about the order and the orderDetails having a price breakdown of the order. We want to get the order's total order value. To write a query for this, we need to calculate the total order value and then join the two tables. Steps : - Create a CTE - `orderValue` from `orderDetails` table - Store average in the column `orderNumber, orderTotalValue` - Join CTE with the `orders` table and show the orders along with `orderTotalValue` ```sql WITH `orderValue` (`orderNumber`, `orderTotalValue`) AS ( SELECT `orderNumber`, SUM( `orderdetails`.`priceEach`*`orderdetails`.`quantityOrdered` ) AS `orderTotalValue` FROM `orderdetails` GROUP BY `orderNumber` ) SELECT `orders`.`orderNumber`, `orderTotalValue`, `orderDate`, `status` FROM orders INNER JOIN `orderValue` ON `orderValue`.`orderNumber`=`orders`.`orderNumber`; ``` **Output:** Entries of those customers who ordered more than the average order price. ![MySQl CTEs](/images/blogs/common-table-expressions-in-mysql/images/2.jpeg) --- ## MySQL CTE use case XYZ company has multiple electronics stores selling laptops, phones, desktops, etc. online. Here is the `sales` table representing the weekly sales of all of the stores under XYZ. ![Sales Table](/images/blogs/common-table-expressions-in-mysql/images/3.jpeg) We are supposed to show the entries of those stores having sales higher than the average sales for all the stores. Steps : - Calculate the total sales of each store. - Calculate the average sales for all the stores. - Show entries for all the stores whose total sales are greater than the average sales. ### Without using MySQL CTE 1. Find the total sales of each store. ```sql SELECT s.store_id, sum(s.cost) AS total_sales_per_store FROM sales s GROUP BY s.store_id; ``` Total sales of each store: ![Total sales of each Store](/images/blogs/common-table-expressions-in-mysql/images/4.jpeg) 2. Find average sales with respect to all stores. ```sql SELECT (avg(total_sales_per_store)) avg_sale_for_all_store FROM (SELECT s.store_id, sum(s.cost) AS total_sales_per_store FROM sales s GROUP BY s.store_id) x ``` Average sales of each store ![Average sales of each store](/images/blogs/common-table-expressions-in-mysql/images/5.jpeg) 3. Display data for all stores whose sales were better than the average sales across all stores. ```sql SELECT * -- Total sales FROM (SELECT s.store_id, sum(s.cost) as total_sales_per_store FROM sales s GROUP BY s.store_id ) total_sales -- avg sales JOIN (SELECT avg(total_sales_per_store) avg_sale_for_all_store FROM (SELECT s.store_id, sum(s.cost) AS total_sales_per_store FROM sales s GROUP BY s.store_id) x ) avg_sales -- condition ON total_sales.total_sales_per_store > avg_sales.avg_sale_for_all_store; ``` **Output:** ![Sales which were better than the average sales across all stores](/images/blogs/common-table-expressions-in-mysql/images/6.jpeg) ### Using MySQL CTE As it gets difficult to read this code, we can use Common Table Expressions to store the results of already calculated parameters. The total sales have been calculated twice, which can be avoided. We can use the CTEs to temporarily store the total sales in the `total_sales` CTE and create `total_sales_per_store` column. Similarly, average sales in the `avg_sales` CTE and `avg_sale_for_all_store` columns. After creating CTE, we can join `avg_sales` with `total_sales` and display all the entries that satisfy the condition of `total_sales_per_store` > `avg_sale_for_all_store` ```sql WITH total_sales AS (SELECT s.store_id, sum(s.cost) AS total_sales_per_store FROM sales s GROUP BY s.store_id), -- Total sales avg_sales as (SELECT (avg(total_sales_per_store)) avg_sale_for_all_store FROM total_sales) -- average sales SELECT * FROM total_sales JOIN avg_sales ON total_sales.total_sales_per_store > avg_sales.avg_sale_for_all_store; ``` **Output:** ![MySQL CTEs](/images/blogs/common-table-expressions-in-mysql/images/7.jpeg) --- ## MySQL Recursive CTE Syntax When a subquery in a common table expression (CTE) refers to the CTE name itself, the CTE is said to be recursive. The recursive CTEs are used for series generation and traversal of hierarchical or tree-structured data. The recursive CTE syntax consists of three elements: - **Initial query (anchor):** Base result set of the CTE structure. It is a non-recursive statement. - **Recursive query:** Query that references the CTE name; hence, it is called a recursive member. The recursive member is joined with the anchor member by a `UNION` or `UNION DISTINCT`operator. - **Termination condition:** ensures the recursion stops when the recursive member returns no row. ```sql WITH RECURSIVE ctename AS ( initial_query -- anchor UNION ALL recursive_query -- recursive member that references the CTE name ) SELECT * FROM ctename; ``` ### Steps to execute a recursive CTE - The anchor/initial query is executed first to store the base case’s result. This base result is used for the next iterations. - The recursive query is then executed until the termination condition. - The anchor member and the result from the recursive query are combined using the `UNION ALL` operator. --- ## MySQL recursive CTE example Consider a case to generate 10 order IDs starting from 1. Look at it as a while loop that keeps on executing until the termination condition. ```sql WITH RECURSIVE natural_sequence AS ( SELECT 1 AS order_id -- anchor member: our sequence starts from 1 UNION ALL SELECT order_id + 1 FROM natural_sequence -- recursive member: reference to itself WHERE order_id < 10 -- stop condition ) SELECT * FROM natural_sequence; -- executes CTE ``` ### Explanation - The non-recursive part gets executed. ```sql SELECT 1 AS order_id ``` It produces initial rows, which consist of one column named `order_id` and a single row with entry 1. ![Single Row 1](/images/blogs/common-table-expressions-in-mysql/images/8.jpeg) - The recursive part gets executed. It will add rows until `order_id` < 10. - When the `order_id` becomes 10, the condition becomes false, the recursion is terminated, and a series of 10 order ids are generated. ![Recursive CTEs in MySQL](/images/blogs/common-table-expressions-in-mysql/images/9.jpeg) *** ## MySQL recursive CTE use cases ### Case 1: Series Generation Consider ABC shop selling stationery items online. Here is the `sales` table representing the weekly sales of ABC. ![Sales Table](/images/blogs/common-table-expressions-in-mysql/images/10.jpeg) We are supposed to show the total sales for each day according to the dates. Lets first show total sales per day using `SUM` and the `GROUP BY` operator ```sql SELECT order_date,SUM(price) AS sales FROM sales GROUP BY order_date; ``` **Output:** ![Series Generation](/images/blogs/common-table-expressions-in-mysql/images/11.jpeg) The manager of ABC wants to have a look at the sales of the first week of February i.e. from 1st - 7th of February. Also whichever day has 0 sales, 0.00 should be allocated to that day. This kind of weekly sales data can be used to create charts/graphs to understand the sales of the shop. We can use recursive CTE here to generate dates from 1st Aug to 7th Aug and later merge them with the `sales` table. We are using the `COALESCE` operator to allocate `SUM` of prices to days having sales and 0 to the ones having no sales. Read more about `COALESCE` [here](https://www.w3schools.com/sql/func_mysql_coalesce.asp) ```sql WITH RECURSIVE dates(date) AS ( SELECT '2020-02-01' -- anchor UNION ALL SELECT date + INTERVAL 1 DAY -- recursive query FROM dates WHERE date < '2020-02-07' ) --termination condition SELECT dates.date, COALESCE(SUM(price), 0) sales FROM dates LEFT JOIN sales ON dates.date = sales.order_date GROUP BY dates.date; ``` **Output:** Representing the weekly sales of the ABC shop ![Weekly sales of the ABC shop](/images/blogs/common-table-expressions-in-mysql/images/12.jpeg) ### Case 2 - Hierarchical Data Traversal Recursive CTEs can be used to play around with hierarchal data like the understanding hierarchy of employees in a company or a family tree, etc. Such hierarchical structures can also inform strategies for [employee rewards and recognition](https://matterapp.com/blog/employee-rewards-and-recognition), as they help identify key contributors and streamline acknowledgment processes in complex organizations.
Consider Amazon LLC which has multiple products like Amazon Prime, Amazon Market Place, Amazon Alexa, Amazon Echo Dot, etc. These products have sub-products like Amazon Echo Dot comes under Alexa or Amazon Fresh and comes under the Amazon Marketplace. Below is the organizational structure of amazon ![Amazon](/images/blogs/common-table-expressions-in-mysql/images/13.jpeg) The `amazon` table represents the same where level indicating under which category the category falls. ![Amazon Table](/images/blogs/common-table-expressions-in-mysql/images/14.jpeg) We are supposed to show which category falls under which organization. As Amazon fresh comes under the marketplace, we are supposed to show ‘Amazon > Amazon Market place > Amazon Fresh to show the hierarchy. Let's create a recursive CTE `hierarchy` with columns `id`, `name`, `path`, and `level` ```sql WITH RECURSIVE hierarchy(id, name, path, level) AS ( SELECT id, company_name, CAST(company_name AS CHAR(100)), 1 FROM amazon WHERE level IS NULL UNION ALL SELECT am.id, am.company_name, CONCAT(h.path,' -> ',am.company_name), h.level+1 FROM hierarchy h JOIN amazon am ON h.id=am.level ) SELECT * FROM hierarchy ORDER BY level; ``` **Output:** Representing the hierarchy of organization in Amazon LLC. ![Hierarchal](/images/blogs/common-table-expressions-in-mysql/images/15.jpeg) *** ## Limitations of Recursive CTE Aggregate methods like `SUM()`, `GROUP BY`, `ORDER BY`, `DISTINCT`, and Window functions shouldn't be used by the recursive CTE. For CTE that is not recursive, this is not true. The CTE must only be mentioned once in the recursive `SELECT` part's `FROM`clause and never in a subquery. *** ## Conclusion - By utilizing temporary tables and the WITH clause, MySQL CTE can be used to prevent the need for complex queries. - It can also be used to chain CTEs in order to make the query simpler. - The CTE allows us to use it in place of the VIEW idea. It can also be used to quickly build recursive queries. - Recursive CTEs can be used to create series and navigate through data that is organized in a tree or hierarchy.

Postgres JSON data type and how to query it?

hello@draxlr.com (Draxlr) — Thu, 15 Sep 2022 00:00:00 GMT

Postgres has been a rock-solid database choice for many decades. There is a huge ecosystem of tools, plugins, and extensions that play very well with Postgres. Although there was this era when there was too much hype around these new database paradigms (ahem MongoDB 🙄), and SQL databases were considered **_NOT COOL_**. But soon people realized the value of having a strict schema and the simplicity of SQL queries to get results. Through all this, SQL databases kept evolving and adding new features that made them an even better choice for a large number of use cases. JSON and JSONB support were some of those features. Since version 9.2, Postgres has provided many operators and functions to handle and manipulate JSON data. Which gives you the flexibility to just dump some JSON data in a field that you can query later. So, let’s take a look at the Postgres JSON data type, how it works, and when to use it and when not to. 🧐 ## JSON? JSONB? What is it anyway? 🤷‍♂️ JSON stands for JavaScript Object Notation. It is an open standard format that consists of key-value pairs. JSON is mainly used to send and receive data from servers. In Postgres, the JSON data is stored as an exact copy of the input text. That makes the writing fast, but every time you have to read the field, the processing functions reparse the value, which makes the reading slow. **Postgres JSONB** overcomes this issue by storing the data in a decomposed binary format that makes it significantly faster to process since no parsing is needed, but slightly slower to input due to the added data conversion overhead. To take it further, Postgres JSONB also supports **indexing**. --- ## How to create JSON fields in Postgres 🔨 Creating a table with a JSON/JSONB field is very straightforward. You can create a table and define the data type of the field as JSON or JSONB. ```sql CREATE TABLE author ( id SERIAL NOT NULL PRIMARY KEY, name TEXT NOT NULL, email TEXT NOT NULL, social_profiles JSON NOT NULL ); ``` This will create a table with the following columns: - **id** - a primary key field - **name** - a field that can store text/string values - **email** - a field that can store text/string values - **social_profiles** - a JSON/JSONB that can store key-value pairs of data. --- ## Insert values in JSON fields in Postgres 📝 To add an entry in a table with a JSON field, you can add the values in quotes. ```sql INSERT INTO author(name, email, social_profiles) VALUES( 'Jitendra Nirnejak', 'hello@nirnejak.com', ' {"github": "https://github.com/nirnejak"}, {"codepen": "https://codepen.io/nirnejak"}, {"linkedin": "https://linkedin/com/in/nirnejak"}, {"nirnejak": "https://nirnejak.com/"} ' ) RETURNING (id) ``` --- ## How to Query JSON fields in PostgreSQL? 🔍 You can get your JSON fields just like any other field. To pull out specific keys and details from JSON fields, you will have to use the JSON operators and functions. ```sql // List social profiles of all the users SELECT social_profiles FROM author ``` ### Postgres JSON Operators Operators are stock functions for manipulating and accessing data types, and JSONB has its own special ones. You can use these operators to handle and manipulate the Postgres JSON data. They’ll help you filter and aggregate your data. So, let’s take a look at a few of them and what they do. - `->` and `->>` Operator This operator helps you get the JSON object field by its key; you can chain them to get data from deeper levels in your JSON object. The difference between `->` and `->>` is that the latter returns the result in text format. ```sql // Author's name, email, and github where the Twitter profile matches URL string SELECT name, email, social_links -> 'github' AS github FROM author WHERE social_links ->> 'twitter' = 'https://twitter.com/jeetnirnejak' ``` - `?` Operator This operator helps you check if a key exists in your JSON object. It also has some modifiers to check for an array of keys. `?&` will check if all the keys defined in an array are in the object, while `?|` will check if any of the keys defined in the array exist on the object. Below are a few examples of this. ```sql // Users with Github SELECT * FROM user WHERE social_links ? 'github' // Users with Github, Codepen and Linkedin SELECT * FROM user WHERE social_links ?& ARRAY('github', 'codepen', 'linkedin') // Users with Github, Codepen or Linkedin SELECT * FROM user WHERE social_links ?| ARRAY('github', 'codepen', 'linkedin') ``` - `@` Operator This operator helps you check if a JSONB element exists at the top level of the JSON object. You can check out the example below to understand it in detail. ```sql // Does the right JSONB element exists at the top level of the left element SELECT * FROM users WHERE social_links @> '{"github": "https://github.com/nirnejak"}'::jsonb // Does the left JSONB element exists at the top level of the right element SELECT * FROM users WHERE '{"github": "https://github.com/nirnejak"}'::jsonb <@ social_links ``` - `#>` and `#>>` Operator This operator is called the path operator and can help you get the object at a specified path. The difference between `#>` and `#>>` is that the latter returns the result as text. ```sql // Get the Github links of the users SELECT social_links #> '{github}' FROM users /* // Get the public Instagram profiles of the users as text { github: "", instagram: { personal: "", public: "" }, } */ SELECT social_links #>> '{instagram,public}' FROM users ``` These are the basic and most common Postgres JSON operators used to query JSON/JSONB data. You can see the detailed list of operators [here](https://www.postgresql.org/docs/9.5/functions-json.html). ### Postgres JSON Functions Along with a set of operators, Postgres also provides some functions to help you manipulate your Postgres JSON data. There are mainly two types of functions. Creation and Processing. - **Creation Functions** - These functions are available for creating JSON values. - **Processing Functions** - These functions are there to help you process and format your JSON data. **Here are some of the common Postgres JSON Functions:** - `to_json` or `to_jsonb()` This function helps you convert any value to a JSON or JSONB value. - `json_each()` or `jsonb_each()` This function will expand the outermost JSON/JSONB object into a set of key/value pairs. - `json_each_text()` or `jsonb_each_text()` This is similar to the above function. This function will expand the outermost JSON/JSONB object into a set of key/value pairs and return it as a text value. - `json_object_keys()` or `jsonb_object_keys()` This function will return the outermost keys present in the JSON/JSONB object. - `jsonb_pretty()` This function will return a formatted (prettified) JSON value. There are more Postgres JSON functions; to learn about them, you can follow [this link](https://www.postgresql.org/docs/9.5/functions-json.html). --- ## When should you use Postgres JSON fields? 👍 1. **Unpredictable data structures** - Postgres JSON or JSONB fields can be a good choice for storing data whose schema might be unpredictable. The fields might change frequently, and data coming in that you might not control and can’t fully predict. 2. **Lots of attributes that are rarely used** - Another really good use case for Postgres JSON and JSONB fields is to store attributes or data that is rarely used. It wouldn’t make sense to store them as a dedicated field since most of the time they will be empty. 3. **Replacing Entity-attribute-value model tables** - If you have a table that’s used to store key-value data, i.e., the first column is treated as the key and the second column is treated as its associated value, you can replace those with JSON/JSONB fields. 4. **Arbitrary fields with dynamic values** - If you need to store unknown fields and some data associated with them, you can use Postgres JSON or JSONB fields for that. Since those fields are unknown at the moment, you can’t create a table for them. Along with these, also consider how often you’ll be querying the data in the JSON/JSONB field, whether they have a default value, and how much control you want over the schema of the JSON object. In general, it can often be used to store data that’s not part of your schema, data you might not want to write migrations for, tags, temporary fields, configs, and settings. --- ## When should you not use Postgres JSON fields? 🙅‍♂️ 1. **Fields that (almost) always exist** - It’s not the best idea to store the fields in JSON or JSONB if they exist in most of the entries. For each column, both the key and the value are stored. So, if your field has millions of entries and most of them have few JSON or JSONB fields in common, it makes more sense to store them in a dedicated table field. 2. **Indexing Concerns** - JSONB fields aren’t easy to index. 3. **Disk Space Limitations** - Since it takes more memory to store the JSON/JSONB fields, you should keep this in consideration if you have disk space limitations. Along with these points, you should also consider if your fields need a default value. It might be hard to enforce it if your data is in the JSON/JSONB field. You should also consider how you’re going to do analytics on the fields. A JSON field without a strict schema might be harder to use for analytical queries. Before making a decision, make sure you’re clear on the pros, cons, and use cases. --- I hope this blog helped you learn about JSON and JSONB fields in Postgres enough to get started. To learn about Postgres JSON and JSONB in detail, you can follow the links below. You can also go through the Postgres documentation. There’s also an **HStore** data type field in Postgres, which is similar to JSONB, and I’m hoping to cover it in the upcoming blogs. Till then, Adios! ### Useful Links Postgres JSON Functions Documentation: [JSON Functions and Operations](https://www.postgresql.org/docs/9.5/functions-json.html) Postgres JSON Types Documentation: [JSON Types](https://www.postgresql.org/docs/current/datatype-json.html) \- [Jitendra Nirnejak](https://nirnejak.com/)

BigQuery vs. Snowflake: What are they and which one to choose?

hello@draxlr.com (Draxlr) — Wed, 07 Sep 2022 00:00:00 GMT

You must have often heard about tools like BigQuery and Snowflake in association with terms like data warehousing or business intelligence tools. So it's clear that those are the critical terminologies behind the software, as mentioned earlier. But how do they compare with each other? Which one is better in what way? This article will answer all of these questions and more by drawing conclusions and inferences from meticulous research. ## What are data warehouses? Let's dissect the word Data Warehouse; it consists of two simple words, 'Data' and 'Warehouse'. 'Data' simply means units of facts that can drive meaningful insight. On their own, these individual units aren't of much use, so to mitigate this problem, we store these units together to derive some meaningful insights. This organised collection of valuable data is called information. While objectively, 'Warehouse' means a large storage area where raw materials or manufactured goods are stored. Deriving from this simple analogy, a data warehouse is a central repository for storing all the data from multiple sources together in a single place. Tools like BigQuery and Snowflake are not only data warehouses but also provide additional features like serverless infrastructure, ML insights, version control, and much more. ## What is Snowflake, and what are its key features? Snowflake is a software-as-a-service (SaaS)-based warehouse solution that can run on popular cloud providers like AWS, Azure, GCP, and many others. It was developed in the cloud and for the cloud. As a result, it has essentially no management or operational overhead and comes with zero baggage. Since Snowflake is a native SaaS service, it takes care of all the backend infrastructure, allowing you to concentrate on what's essential: drawing conclusions and inferences from your data. Due to Snowflake's tremendous scalability, virtually endless concurrent searches are possible. Snowflake's capability to process enormous amounts of unstructured data has significantly accelerated intelligent data analysis, enabling features like "Schema on Read" to access data in structured and semi-structured formats without requiring prior modelling. It has many parts, but some key ones are highlighted below. ### Time Travel One of the unique Snowflake characteristics is time travel. Users may follow the evolution of data through time by using the time travel feature. All accounts have access to this Snowflake feature, which is free and enabled by default for everyone. Moreover, this allows you to access the historical data in a table. Snowflake software can configure transient databases, schemas, tables, and temporary tables' time travel retention periods to 0 or 1 day for Snowflake Enterprise accounts (and higher). Time travel can extend the retention duration to 0-90 days for permanent databases, schemas, and tables. ### Undrop As long as the dropped object hasn't been system-purged, Snowflake's UNDROP command can recover it. The object can be undropped to return to its previous state. Databases, schemas, and tables are examples of objects that can be undropped. Users can use it to restore data more than once, and each version that has been dropped can be undone in reverse chronological order, such that the most recent version is restored first. This will work if the current table with the same name is renamed prior to doing the undrop action. Thus, one of the most crucial and unique Snowflake features is the UNDROP command. ### Fail-Safe Fail-Safe is yet another crucial Snowflake feature. In the event of disasters like disc failures or other hardware problems, Fail-Safe ensures that historical data is protected. In the event of a catastrophe, Snowflake offers seven days of fail-safe protection for data that Snowflake can only recover. After the time travel window has passed, the fail-safe seven-day period begins. The total recuperation time for a table with 90 days of time travel will be 97 days. Snowflake only handles it in the event of major catastrophes. This feature is enabled by default, and the consumer cannot disable it. ### Sharing data between accounts With Snowflake's Secure Data Sharing functionality, you may collaborate with another Snowflake user on particular objects (like tables) from a database without having to replicate the data. As a result, neither more storage space nor higher storage expenses are needed to store the shared data for the data consumer. The setup is straightforward, and data consumers have rapid access to the information because data sharing occurs through Snowflake's metadata store. Snowflake's data marketplace connects data producers and users that want to share free or paid data. Consumers can easily access shared data in their accounts, which they can query and mix with data from other sources as necessary. Data exchange, which enables users to work with invited participants on data without becoming Snowflake clients, is another Snowflake use case. This makes use cases like data sharing between clients, vendors, and business partners simpler. Snowflake enables providers to generate reader accounts for data users who don't already have accounts. Consumers can access shared data with reader accounts without becoming Snowflake clients. ### Caching Results The Snowflake architecture includes caching at several levels to aid in accelerating your searches and cutting expenses. For instance, Snowflake stores the results for 24 hours in the cache when a query is executed. Therefore, granted that the underlying data hasn't changed, the results are already accessible if the same query is executed again by the same user or another account user. This is especially helpful when analyzing the data since it avoids replicating complex searches, saving time and reducing cost. ## What is BigQuery and its key features? BigQuery is a serverless, highly scalable data warehousing platform managed by Google that comes with a built-in query engine. The query engine is capable of running SQL queries on terabytes of data in a matter of seconds and petabytes in only minutes. As it is serverless, you get this performance without having to manage any infrastructure and without having to create or rebuild indices. It has many features, but some key ones are outlined below. ### Serverless As Google's BigQuery is serverless, Google does all resource provisioning behind the scenes. Hence, there is no need to manage your infrastructure, like in-house databases, servers, or query engines, and you can instead focus on data and analysis. Everything is provided and integrated into BigQuery itself. Moreover, it automatically upscales itself on the basis of the resources used, which implies there is no need to constantly observe or do vertical and horizontal scaling from time to time. Being serverless means your entire data lives in the cloud and can be accessed wherever and whenever you want, regardless of time and place. ### MultiCloud Capabilities BigQuery also comes with another valuable tool known as Omni, which allows one to analyze data across multiple clouds using standard SQL without leaving BigQuery's familiar interface. Its flexible, fully managed infrastructure allows data analysts or data scientists to have a completely seamless experience. Moreover, the cross-cloud transfer will enable you to aggregate data or train models across clouds from a single window. ### Built-in ML and AI integrations Besides aggregating all your data in a single place, BigQuery ML's integration with Vertex AI, along with pre-built and highly customizable TensorFlow models, makes it possible to train and run robust machine learning models on structured data in just a few minutes using only SQL. You may use these sophisticated tools to obtain additional insights and improve the predictive powers of your data, even if you have no prior experience with machine learning or artificial intelligence. ### Spreadsheet Interface Another prominent feature that BigQuery offers is connecting your database to Google Sheets and then querying it using your well-known sheet filters, formulas, etc. Without having to know SQL, users of Connected Sheets can examine billions of rows of real-time BigQuery data in Google Sheets. Users can effortlessly analyze big data using well-known tools, such as pivot tables, charts, and formulae, to quickly gain insights from massive data. ### Convergence of Data Warehouses and Data Lakes Using the Storage API, one can natively execute data science workloads using open source tools like Spark, TensorFlow, Dataflow, Apache Beam, MapReduce, Pandas, and sci-kit-learn on BigQuery. The Storage API has a significantly more straightforward architecture, requires less data movement, and does not require numerous copies of the same data. --- ## BigQuery vs. Snowflake: A Succinct Comparison ### Pricing: Snowflake's architecture keeps computation, storage, and cloud services together to optimise their independent performance. For computational resources, Snowflake has a time-based pricing model, in which users are charged on a per-second basis for execution time but not for the volume of data scanned during processing. Each compressed TB of data kept costs USD 23 in snowflake storage. Snowflake Standard Edition compute costs are $0.00056 per second for each credit used, whereas Snowflake Enterprise Edition compute costs are $0.0011 per second for each credit used. Data stored in the Snowflake will be charged as per the average monthly usage per TB, or to save storage costs, you can also pay upfront per TB. In terms of pricing, BigQuery has two options. Its on-demand model uses a query-based pricing model for computing resources. Users are billed at a rate of $5 per terabyte of processed data that their queries scan. BigQuery employs "flex slots," which are 60-second promises of dedicated query processing capacity, as a unit for a cost quotation. The flat-rate plans have 100 "flex slots" and start at roughly $2,920 per month. Additionally, Google charges $20 per terabyte every month for data storage. Although these both use different billing methods, they both primarily consider both computation and storage. However, BigQuery storage is marginally cheaper per terabyte than Snowflake storage. ### Security: Both Snowflake and BigQuery offer customer-managed keys and use AES encryption for data. Both use role-based systems to grant access to resources. Snowflake enables federated user access through Okta, Microsoft Active Directory Federation Services (ADFS), and the majority of SAML 2.0-compliant providers. Microsoft Active Directory allows federated user access for BigQuery. Both provide OAuth 2 for permitted account access and support multi-factor authentication (MFA) without disclosing or storing user login information. Granular permissions for schemas, tables, views, procedures, and other objects are available in Snowflake, but not for specific columns. BigQuery only offers permissions on datasets and not on individual tables, views, or columns. Virtual private networking is not supported by Snowflake natively, but it can be configured using AWS PrivateLink to link your Snowflake account to one or more AWS VPCs. With the help of the VPC Service Controls from the Google Cloud Platform, BigQuery enables you to set up a network security perimeter. Strong security safeguards that guard the confidentiality and integrity of your sensitive data are present in both Snowflake and BigQuery. Additionally, both technologies abide by industry-specific laws like HIPAA and PCI DSS. ### Usability: Beyond conventional reporting and dashboards, Snowflake has broader support for use cases. You can isolate diverse workloads to achieve SLAs thanks to its decoupled storage and computation design, which also supports high user concurrency. Snowflake does not offer interactive or ad hoc query performance because of slow data access, inadequate indexing, and poor query optimization. Additionally, Snowflake is unable to enable streaming or low-latency ingestion with intervals of less than one minute. Similar to Snowflake, BigQuery offers support for more use cases than just reporting and dashboards. You may isolate workloads by placing each workload in a different reserved slot. BigQuery additionally offers low-latency streaming, in contrast to Snowflake, Redshift, or Athena. BigQuery doesn't have the performance to handle large-scale interactive or ad-hoc queries. ### Scalability: Snowflake allows users to scale their computation and storage resources up and down independently. It consists of automatic performance tuning and workload monitoring in order to improve query times while the platform is running. It scales very well, both in terms of data volumes and query concurrency. The decoupled storage/compute architecture supports resizing clusters without downtime and, in addition, supports auto-scaling horizontally for higher query concurrency during peak hours. BigQuery handles the question of scalability entirely under the hood in the form of'slots'. As it is serverless, BigQuery automatically provisions additional compute resources on an as-needed basis in order to handle large data workloads. With no need to worry about the technological aspects, managing even petabytes of data in a matter of minutes becomes much more straightforward. ### Performance: Performance-wise, Snowflake often outperforms BigQuery for the majority of queries, but only marginally. In comparison to larger partitions, its micropartition storage strategy effectively searches for fewer data points. When compared to multi-tenant shared resource solutions, the decoupled storage and compute architecture's ability to segregate workloads allows data to avoid competition for resources, and the capacity to expand warehouse sizes can frequently improve performance. BigQuery performs similarly in benchmark tests, yet it regularly ranks last in most queries. We can do little to speed up BigQuery performance because it chooses how many resources the question will require on its own. ### Manageability: Neither Snowflake nor BigQuery imposes a significant administrative overhead. Each enables administrators to control user access, roles, and data security, yet performance optimisation occurs automatically. Each of them automatically scales in the background to match the needs of the moment as the data volume increases or the complexity of the queries increases. Snowflake, in particular, enables administrators to individually scale their computing and storage resources up and down, whereas BigQuery, which is "serverless," computes, stores resources, and has independent scaling capabilities. As a result, Snowflake's platform control ability and manageability are marginally higher when compared with BigQuery. --- ## Conclusion In a nutshell, Snowflake and BigQuery are both extremely versatile and put a lot of facilities on the table. Minuscule upkeep, along with a function of computational resources utilised paired with the volume of storage occupied, is used to determine the cost quotation for both of these solutions. BigQuery has the potential to be more popular among consumers who are already familiar with the Google Cloud ecosystem and just want to pay for minimal upkeep. On the other hand, Snowflake may be a better alternative for people who wish for a platform-independent data warehouse with more configuration ability. Moreover, both platforms have excellent usability, while Snowflake might be a tad bit simpler to use. The serverless aspect of BigQuery, in particular, makes it simple to launch rapidly. Both Snowflake and BigQuery have highly developed scalable capabilities along with strong security safeguards that guard the confidentiality and integrity of users’ sensitive data. Finding the option that makes the most sense for your data strategy is the first step in choosing one over the other. The use case is the most influential factor in determining the choice between BigQuery and Snowflake.

Common Table Expressions (CTEs) and their examples in PostgreSQL

hello@draxlr.com (Draxlr) — Wed, 07 Sep 2022 00:00:00 GMT

A Common Table Expression (CTE) is a name given to a temporary result set of a query that is required in the context of a more significant query. These temporary result sets are part of the query that can be referenced within the SELECT, UPDATE, INSERT, or DELETE statements. Common table expressions are not stored anywhere; they only exist for the duration until the more significant query is executed. The Common Table Expression enables us to write a complex query in a more readable and simplified format. **Syntax:** ```sql WITH cte_name (column_names, … ) AS ( CTE Query definition ) SELECT …. ( MAIN QUERY ) ``` Common Table Expression is defined using the WITH clause; there can be more than one CTE defined in one SQL statement, separated by commas. Postgres doesn’t allow you to have more than one **WITH** statement in one query. ```sql WITH cte_name (column_names, … ) AS ( CTE Query definition ), cte_name2 (column_names, … ) AS ( CTE Query definition ) SELECT …. ( MAIN QUERY ) ``` Common table expressions can be divided into two categories: non-recursive and recursive. ### Non-Recursive Common Table Expression Non-recursive CTEs can be used when we want to group data into a single name and return it as one result set. We can then use this result set in our queries. They have several advantages over regular queries, as they are used to simplify complex joins and subqueries. **Syntax:** ```sql WITH CTE_NAME (number) AS ( -- Regular query SELECT 1 ) SELECT * FROM CTE_NAME; ``` **Output:** ![Non-Recursive Common Table Expression](/images/blogs/common-table-expressions-and-its-example-in-postgresql/images/1.png) ### Recursive Common Table Expressions A recursive common table expression (CTE) is a CTE that references itself or recursively calls itself. By doing so, CTEs repeatedly execute and return subsets of data until the complete result set of data is returned. Recursive common table expressions have the anchor member and the recursive member within the simple query. **Syntax:** ```sql WITH RECURSIVE CTE_NAME (number) AS ( -- Anchor Member SELECT 1 AS number UNION ALL -- Recursive Member SELECT number + 1 FROM CTE_NAME WHERE number <= 5 ) SELECT number FROM CTE_NAME; ``` ![Recursive Common Table Expressions](/images/blogs/common-table-expressions-and-its-example-in-postgresql/images/2.png) --- ## Common Table Expression in Postgres The common table expression in Postgres uses the **WITH** clause, which allows assigning names to the sub-query and using the result set in the main query. PostgreSQL provides the **WITH** and **AS** statements that support the design of auxiliary queries, also known as CTEs (Common Table Expressions). This **WITH** query is evaluated only once per execution of the main query, even if it refers to it multiple times in the main query. **CTE Syntax in Postgres** ```sql WITH CTE_NAME (COL_LIST) AS ( CTE_QUERY ) STATEMENT; ``` **Analysing the above syntax:** - The **WITH** clause is used to define a temporary relationship using a temporary query. - **CTE_NAME** stores the result set from the temporary query - In the above query, **COL_LIST** is a list of columns in the result set. - **STATEMENT** is our main query, which uses the result set stored in **CTE_NAME** In the above CTE syntax, as we have seen, the column list is defined beside the CTE name. Postgres also allows defining this column list within a sub-query. ```sql WITH CTE_NAME AS ( CTE_QUERY AS COL_LIST ) STATEMENT; ``` --- ## Postgres CTE examples For understanding CTEs in Postgres in a better way. Let’s consider an example e-commerce shop that has a table called **orders** that stores sales data for each product. **For creating Orders Table** ```sql CREATE TABLE orders ( id SERIAL PRIMARY KEY, order_number INT NOT NULL, product_name VARCHAR(10) NOT NULL, order_date DATE DEFAULT CURRENT_DATE, total_price INT NOT NULL ); ``` ![Postgres CTE Example - 1](/images/blogs/common-table-expressions-and-its-example-in-postgresql/images/3.png) Now, using non-recursive CTEs, we will find the total price and count of each product that is sold using this order table. ```sql WITH CTE_NAME (product_name, total_count, total_price) AS ( SELECT product_name, COUNT(product_name), SUM(total_price) FROM orders GROUP BY product_name ) SELECT * FROM CTE_NAME; ``` **Understanding the above code** The above PostgreSQL code has a CTE under the name **CTE_NAME**, which selects three columns **product_name, total_count,** and **total_price** from the orders table, subquery used **GROUP BY** statement over **product_name** to find these columns and then used in the main query to get the following output. **Output:** ![Postgres CTE Example - 2](/images/blogs/common-table-expressions-and-its-example-in-postgresql/images/4.png) --- ## Recursive Common Table Expression in Postgres In recursive CTEs, the subquery within the CTE expression calls its own result set. They can be used for querying hierarchical data, such as tracking lineage or finding organisational structure in the data sets. Postgres uses the **RECURSIVE** keyword in order to define recursive CTEs. **Recursive CTE Syntax in Postgres** ```sql WITH RECURSIVE CTE_NAME AS( CTE_query_definition -- anchor member UNION [ ALL ] CTE_query CTE_NAME -- recursive member ) SELECT * FROM CTE_NAME; ``` **Analyzing the above syntax:** - Postgres uses the **RECURSIVE** keyword in order to define recursive CTEs. - An anchor member works like a base condition for the termination of these recursive calls. - One or more subquery definitions in CTEs are joined with the non-recursive term through the **UNION** or **UNION ALL** operator. - The recursive member calls its own result set until it gets the complete result set. **Example of a Factorial program Using Recursive CTEs in Postgres** ```sql WITH RECURSIVE CTE_NAME (line_number, factorial) AS ( VALUES (0, 1) -- base subquery UNION ALL SELECT line_number + 1, (line_number + 1) * factorial FROM CTE_NAME -- recursive subquery ) SELECT * FROM CTE_NAME LIMIT 5; ``` The following code uses recursive CTE to get factorial series with anchor values (0, 1) and the **UNION ALL** statement to join both anchor and recursive subquery together. In the main query, we have a LIMIT statement to define the number of rows to display. **Output:** ![Postgres CTE Example - 3](/images/blogs/common-table-expressions-and-its-example-in-postgresql/images/5.png) --- ## Postgres Recursive CTE Example Recursive CTEs are also good when we have hierarchical data in our table. To understand this, let us consider an example of an e-commerce store where we have different categories of products. ![Postgres CTE Example - 4](/images/blogs/common-table-expressions-and-its-example-in-postgresql/images/6.png) This flowchart shows the e-commerce store under which we have clothing and accessories sections, and clothing is the parent of men's and women's clothing. In the same way, grooming and footwear are children of accessories. **Creating an e-commerce table** ```sql CREATE TABLE ecommerce ( id SERIAL PRIMARY KEY, name VARCHAR(10) NOT NULL, level INT NOT NULL, parent_id INT NOT NULL ); ``` **Inserting values in the above table** ```sql INSERT INTO ecommerce (id, name, level, parent_id) VALUES (0, 'E Commerce store', 1, NULL), (1, 'Clothing', 2, 0), (2, 'Accessories', 2, 0), (3, 'Men Clothing', 3, 1), (4, 'Women Clothing', 3, 1), (5, 'Grooming', 3, 2), (6, 'Footwear', 3, 2); ``` **Output:** ![Postgres CTE Example - 5](/images/blogs/common-table-expressions-and-its-example-in-postgresql/images/7.png) Now let’s look at the query to fetch all children for the given id. For this, we will define a subquery with an anchor member with the root node id and union it with the recursive member in order to fetch all children's root nodes. ```sql WITH RECURSIVE CTE_NAME AS ( SELECT id, name, level, parent_id FROM ecommerce e1 WHERE e1.id = 1 -- root node id ( base query ) UNION SELECT e2.id, e2.name, e2.level, e2.parent_id FROM ecommerce e2 JOIN CTE_NAME ON e2.parent_id = CTE_NAME.id -- recursive subquery ( to get all children of the root node ) ) SELECT * FROM CTE_NAME; ``` **Output:** ![Postgres CTE Example - 6](/images/blogs/common-table-expressions-and-its-example-in-postgresql/images/8.png) --- ## Basic Guidelines For Writing a Good CTE Query - CTEs must be followed by a single Data Manipulation Language (DML) statements such as SELECT, INSERT, UPDATE, or DELETE that references the CTEs column - Multiple CTEs can be defined in one query using a comma - A CTE can refer to itself or other CTE in the same query - Only one **WITH** Clause can be defined in a single query - Recursive CTE might result in a loop, to avoid this loop **LIMIT** statement can be used --- ## Conclusion CTEs can be used instead of views and join which allows us to simplify for readability and maintainability of code. CTEs are a powerful tool, that enables solving some problems in a very simple and elegant way. Recursive CTEs are used in places where tables have hierarchical data or we need to track lineage within data.

Redshift vs. BigQuery: Comparing the Best Data Warehouses

hello@draxlr.com (Draxlr) — Thu, 11 Aug 2022 00:00:00 GMT

All organisations spread across multiple domains have one point of management in common: data. Be it an e-commerce industry that needs to keep track of umpteen customers and their buying preferences, or the healthcare industry that needs to store information about the maximum amount of medicines and its consumers, Physically storing huge volumes of data is a thing of the past, and all the companies are now pacing towards cloud-based technologies to take care of data storage, analysis, and overall management. We need a data warehouse to accomplish the same. A data warehouse is a centralised data storage system that collects data from various sources and supports analytics and other business intelligence activities. The data stored in the warehouse comes from a variety of heterogeneous resources, ranging from log files and events to relational databases. In a data warehouse, data is not real-time; hence, the focus is on analytical queries and the strategic use of data for business decisions. Fast-paced and growth-oriented businesses rely on this data for reports, analysis, and dashboards to extract insights and monitor their performance. The two major providers of cloud-based data warehouses in current times are Redshift and BigQuery. Redshift belongs to Amazon, whereas BigQuery is owned by Google. Let us do a deep dive into comparing these two and learning more about each of them, and then we will cover an in-depth analysis of Amazon RedShift vs. Google BigQuery so that you can draw a constructive conclusion. ## Redshift Redshift is an impressive, fully managed data warehousing solution by Amazon that can store data ranging from a few gigabytes to a petabyte or more, depending on the business requirements. The key highlights of Redshift include parallel processing and data compression, which allow it to process as many as a billion rows simultaneously. Not just that, it also takes care of the security and other aspects of the data at no additional cost. Data is stored in a set of computed resources called nodes. These form groups of clusters in Redshift, which can be further divided into slices to help attain deeper insights into the data. ![amaz.png](/images/blogs/redshift-vs-bigquery-comparing-the-best-data-warehouses/images/amaz.png) ### Key features of Redshift 1. **Something for everyone** No matter what your business requirements are, they are most likely to be met by Redshift. Amazon Redshift Serverless can be used to run and scale analytics within a short period of time, whereas Query Editor v2 brings the ease of SQL into the world of data engineers and data analysts. Though you can visualize query results through the query editor and create tables and ad data visually, Moreover, the Redshift Data API makes accessing and updating data as easy as hitting an API and supports major programming languages such as Python, Go, Java, Node.js, PHP, Ruby, and C++. 2. **Data Analysis made simple** Redshift makes it easy to query as well as export data to and from your data warehouse. Using ANSI SQL, open file formats like JSON and CSV can be queried directly in S3. Exporting the data can be done through the UNLOAD command in Redshift by specifying the format of the output file. Apart from that, Redshift provides AWS service integration and data sharing among different AWS accounts across different regions. It also supports advanced analytical processing. 3. **Scalability without compromising performance** Using technologies like Advanced Query Accelerator and RA3 instances, Redshift optimizes its query speed to a great extent. Queries in Redshift also make use of machine learning and result caching to deliver faster results. 4. **Top-notch security** Amazon uses end-to-end encryption and network isolation to offer the best security to its consumers. Not only that, it integrates with AWS CloudTrail, through which an organization can audit all Redshift API calls. ## Google BigQuery Another great enterprise data warehouse alternative is Google BigQuery. This data warehousing solution is fully managed as well, along with being scalable and serverless, which means you do not have to set up or manage any infrastructure. The range of features offered by BigQuery at a cost-effective model is super impressive and attracts many consumers. Let us dive deep into the salient features of BigQuery. ![google.png](/images/blogs/redshift-vs-bigquery-comparing-the-best-data-warehouses/images/google.png) ### Features of BigQuery 1. **Multi-cloud Functionality** Without charging an extravagant amount of money, BigQuery allows us to analyze data present in more than one cloud. It achieves this by decoupling its storage and compute components. 2. **ML Integration** Google BigQuery has built-in ML integration, which can be used to create and execute machine learning models with the help of simple SQL queries. This eliminates the need to build ML solutions and provides the ability to programme and execute ML models using spreadsheets and existing BI tools. 3. **Speed and simplicity** BigQuery uses a BI engine that ensures high concurrency and quick response times. It can easily process up to billions of rows in a very short time. 4. **Automated Data Transfer** Data transfer into BigQuery is a simple and automated process that does not involve coding. Retry mechanisms can also be introduced in cases of issues or any possible server errors. You can also load data from other data warehouses, like Redshift and TeraData, into Google BigQuery. --- ## Comparison Both of the data warehouses by Amazon and Google are efficient and robust and are backed by a lot of great consumers, but is one of these better than the other? Which one of the two ultimately wins the battle of Amazon RedShift vs. Google BigQuery? Well, it depends entirely on your business requirements. Let us do a detailed BigQuery vs. RedShift comparison for you to take a call on which warehouse suits your needs better. #### **RedShift vs. BigQuery in Performance** In terms of performance, BigQuery vs. RedShift is a close call. BigQuery’s pricing is based on the volume of data that you process, whereas RedShift is limited by the node you're running on, which is what you should consider when looking for performance. Since both of these are backed by tech giants, there is no considerable difference in performance that can be highlighted. Depending on the kind of queries that will be run, you can try both out. Redshift and BigQuery both offer free trial periods where customers can evaluate performance, but there is a limit on available resources during those periods. #### **RedShift vs. BigQuery in Scalability** Scalability is limited by three factors: continuous ingestion, tightly coupled storage and compute resources, and a lack of dedicated resources. Scaling in BigQuery is structured and well-planned, as it automatically assigns resources when required. With the on-demand pricing model of BigQuery, it has complete control over the assignment of resources that it provides, whereas in a reserved-slots pricing model, control over resources is more limited, which aids in scalability. Even with RA3, Redshift cannot distribute the workload across clusters. To support query concurrency, it can scale up to 10 clusters, but Redshift can only bear a maximum of 50 queued queries across all clusters by default. #### **RedShift vs. BigQuery in Manageability** Even though both of the warehouses are serverless, Redshift needs you to have a basic understanding of nodes and clusters, both creating and allocating them if required. Moreover, Redshift also requires periodic management activities, like cleaning up or vacuuming tables to delete entries. For BigQuery, we need no such knowledge. You can simply create a project on GCP, enable BigQuery on it, and run a query. As specified above, Google automatically scales in the background to meet scalability needs. #### **RedShift vs. BigQuery in Usability** If we consider usability, BigQuery is a clear winner, as BigQuery's SQL-like user interface helps engineers and analysts get their tasks done without requiring any additional skills. It abstracts the details of the hardware, database, and other configurations. Redshift, on the other hand, requires a basic knowledge of warehousing concepts, nodes, clusters, and their management, which could take up to a few weeks to get a hang of. #### **RedShift vs. BigQuery in Security** In terms of security, both companies have a robust system. Amazon RedShift uses [Amazon IAM](https://aws.amazon.com/iam/) for identity, whereas Google BigQuery uses [Google Cloud IAM](https://cloud.google.com/iam/). Both warehouses support encryption and include VPC and SSL connections. A point worth mentioning here is that Google has impressive B2B identity management with [OAuth](https://developers.google.com/identity/protocols/OAuth2), which means that an organization can give identity controls to third-party systems to allow them to perform specific operations without exposing their entire ecosystem to them. In the same manner, an organization can grant a specific set of permissions to a user for a specific cluster in Redshift. #### **RedShift vs. BigQuery in Pricing** On a macroscopic level, both services have two types of pricing: pay-per-use or on-demand pricing and reserve or flat-rate pricing. Google BigQuery [charges](https://cloud.google.com/bigquery/pricing) for storage, inserts, and queries individually, whereas in RedShift, you are [charged](https://aws.amazon.com/redshift/pricing/) for each node in clusters. For BigQuery, storage costs around $20 per TB per month, and the queries cost around $5 per month. In the case of RedShift, the cheapest node available is around $0.25 per hour. Note that for RedShift, the price covers both storage and processing. If you are looking to perform everyday data warehouse operations, RedShift will be the ideal choice for you, but in cases of extremely variable workloads and data mining operations, BigQuery would be a better choice. --- Both BigQuery and RedShift are excellent choices for businesses to assist them in their analytics and growth. Google BigQuery vs. Redshift is a tight call, but the above factors should help you make an informed decision. Whichever of the two data warehouses you choose, Draxlr has support for that, which can solve the rest of your worries. After registering on Draxlr here, [https://app.draxlr.com/](https://app.draxlr.com/), you can choose the manual setup option, and you will land on the add database page. ![Screenshot from 2022-08-03 23-40-39.png](/images/blogs/redshift-vs-bigquery-comparing-the-best-data-warehouses/images/connect-db-with-draxlr.png) ### Redshift with Draxlr On the database page, you can choose Redshift and then provide the connection details like your host, port, user, password, and database name. Alternatively, you can also provide the RedShift Database URL. After the connection is successful, you can access all your data through Draxlr. ![AWS RedShift with Draxlr](/images/blogs/redshift-vs-bigquery-comparing-the-best-data-warehouses/images/redshift-with-draxlr.png) ### BigQuery with Draxlr If you want to move ahead with BigQuery, you can select BigQuery and then provide the JSON key file and your connection name. After the connection is successful, you can access all your data through Draxlr. ![Google BigQuery with Draxlr](/images/blogs/redshift-vs-bigquery-comparing-the-best-data-warehouses/images/big-query-with-draxlr.png) Choosing the right data warehouse, especially if it is backed by major tech giants like Amazon Redshift and Google BigQuery, is one of the most crucial steps in building an organization. It needs a lot of research and insights, as the growth and analytics of the company depend on the warehouse. The above points were an attempt to take you a step closer to your decision, and remember that no matter which of the two you choose, Draxlr is right here to back you up! We support both of these data resources, along with a lot of other categories of databases, which you can check out [here](https://app.draxlr.com/register/). I hope you make the right decision!

Data Lake vs. Data Warehouse

hello@draxlr.com (Draxlr) — Wed, 03 Aug 2022 00:00:00 GMT

In today's data-driven world, businesses produce colossal amounts of data and desire to thoughtfully use it to acquire customer and business insights that will help them gain a competitive advantage. Data that has been collected often comes from a variety of source systems in order to better understand the dynamics of the core business, consumers, and market. Due to the ever-growing volumes of data, businesses constantly need to come up with new and more effective ways to combine and transform various sources of information in order to make better and more informed decisions. The two most common solutions for storing large amounts of data are data lakes and data warehouses. In this blog post, we'll examine data lakes vs. data warehouses in a more detailed manner. --- ## Data Lake: Explained! A data lake is a massive storage repository or facility where a substantial volume of structured, semi-structured, and unstructured data is stored in its unprocessed or raw form. With no set restrictions on account size or the size of the file, the data lake offers a place to store all sorts of data in their original formats. In data lakes, the data is stored using a flat architecture method, allowing users to query or process data whenever it is necessary. A data lake offers a practical solution for businesses that need to gather and retain colossal amounts of data but do not always need to process, manage, and analyse it all at once. ### Characteristics of the Data Lake #### 1. Centralization One of the fundamental concepts of a data lake design is the centralised storage of segregated data. It is simpler to control and administer because of this centralization, and it also makes it easier to innovate or experiment using different data sets without disturbing the status quo. #### 2. Collect, store, and manage data at any scale. Any size of data collection and storage is possible with data lakes. Nowadays, cloud-based object storage services offer almost infinite space at extremely low prices in terms of the three V's of big data. If necessary, with data lakes, data for your company can be gathered quickly and efficiently using streaming technology in real-time. These platforms also handle the challenging work of revealing value in unstructured data, including the automated transcription of audio recordings, thereby supporting a variety of contemporary data sources. #### 3. Data Traceability In a data lake, all of an organisation's or enterprise's data is stored and managed throughout every stage of its lifecycle—from data definition and access through storage and processing to analytics and application. ### Data Lakes: Use Cases #### 1. Machine learning With the use of data lakes, businesses will be able to provide a variety of insights using data, such as historical data reporting and machine learning. These ML models are further used to predict outcomes and recommend a number of recommended actions to get the best conclusion. #### 2. Business Intelligence With data lakes, you can significantly increase the speed of reports, dashboards, and ad hoc inquiries. You can also utilise current BI tools on top of data lakes without sacrificing performance or the quality of the data. #### 3. Analytics Data lakes make it possible for different roles inside your organisation, such as data scientists and data engineers, to access data using their preferred analytics tools and platforms. You can use data lakes to seamlessly implement BI and analytics without migrating your data to a different analytics solution. #### 4. Data indexing and cataloguing Data lakes let you store both relational and non-relational data, including data from social media, IoT devices, operational databases, and line-of-business applications. Through data crawling, manipulation, cataloguing, and indexing, a data lake also enables you to know what data exists within the lake. --- ## Data Warehouse: Explained A data warehouse is a centralised repository that enables the storage, analysis, and interpretation of filtered and organised data in order to assist in improved decision-making. Data warehouses frequently receive data from relational databases, transactional systems, and other sources. Furthermore, a data warehouse is intended to facilitate and promote business intelligence (BI) activities, particularly analytics. The data present in data warehouses is accessible to data engineers, data scientists, and decision-makers via business intelligence (BI) applications, SQL clients, as well as other advanced analytics applications. ### Characteristics of Data Warehouses #### 1. Subject-Oriented In order to satisfy the business needs of individual department-specific users, data warehouses are subject-oriented databases that often give information on a topic such as sales inventories or supply chains rather than overall business operations. In other words, the data in a subject-oriented data warehouse is organised based on user-defined themes, where a topic is a group of linked facts that pertain to a particular business user. #### 2. Integration The integration capability of data warehouses brings together information from several sources into one central warehouse. Any department within a business can access the data warehouse, and the data can be easily organised into spreadsheets or tables for analysis and research purposes. Additionally, it has the ability to link with other corporate software and phone systems, giving employees direct access to information without switching between programs. #### 3. Time Variant Data is maintained in data warehouses at various time intervals, including weekly, monthly, and yearly. The historical perspective that the data warehouse offers is one of its key characteristics. According to the factors of time, it keeps the enormous volume of data from all database sources saved. In other words, a data warehouse is a time-variant database that helps business managers analyse the company's operations and compare them to those of other time periods, such as the year, quarter, month, week, and date. #### 4. Non-volatile The data present in a data warehouse is non-volatile in nature. It means that the previous data is not altered or changed once the new data is fed. Since it is non-volatile, the data is read-only, and users can refresh the data at predetermined intervals. This aids in the analysis of historical data and the comprehension of historical events. --- ### Data Warehouse: Use Cases #### 1. Strategic reporting The storage of data for reporting needs is excellent with data warehouses. They are ideal for ad-hoc reporting since they are optimised for high-performance queries. Data warehouses are frequently used to combine data from several source systems to acquire a comprehensive picture of the data or to determine how specific elements are influencing various facets. #### 2. Performance Evaluation Data warehouses can be used to assess group performance throughout the company. Users can delve deeper into the team data to build custom dashboards or reports that display the performance of the team in relation to particular criteria. Customer support, sales, and marketing teams can each be evaluated using metrics produced from the data warehouse, such as usage trends, lifetime value of customers, and acquisition sources. #### 3. Natural Language Processing Several firms frequently employ NLP to improve their customer service since it provides quick data analysis and gives opportunities for growth in support, sales, and marketing. Huge amounts of structured and unstructured data can be stored in a data warehouse and then evaluated using NLP tools. When these insights are analysed, company staff or automated responses—such as live chat support or suggestions based on prior experiences with customers—are provided in real-time. --- ## Data Lake vs. Data Warehouse: A Succinct Comparison #### 1. Nature of the Data Data lakes gather information from many sources in its original or raw form and make it accessible for any potential future use. In contrast to this, data warehouses contain structured and semi-structured data that has been cleaned, pre-processed, and is available for strategic analysis based on predetermined business needs. Due to their less organised and unfiltered nature, data lakes often need substantially more storage space than data warehouses. Additionally, unprocessed raw data is pliable and suitable for machine learning. It may be easily evaluated for any purpose. However, the risk of all that unstructured data is that, in the absence of adequate data quality and data governance mechanisms, data lakes might occasionally turn into data swamps. In contrast, data warehouses only hold processed data, so each piece of information there has already been put to use within the company. This implies that data that might never be needed is not wasting storage space. #### 2. Purpose Data lakes offer inexpensive storage for massive amounts of information from numerous sources. By allowing data with any structure, costs are reduced using data lakes since the data is more adaptable and scalable and is not required to fit into a certain schema. However, structured data in a data warehouse is more straightforward to review because it is cleaner and comes in a uniform format to query from. Since they restrict data to a schema, data warehouses are especially useful for analysing historical data for certain data decisions. Only processed and clean data that has been employed for a particular purpose is stored in data warehouses. A data warehouse has the benefit of not wasting storage space on data that may never be used. Data lakes, on the other hand, store the unprocessed data in data lakes, sometimes for the sole purpose of storing it and occasionally for specified future use. #### 3. Users When speaking about the user base, both data lakes and data warehouses have their own set of audiences and applications. For example, data professionals frequently use preprocessed data in data warehouses to produce high-end visualisations and reports. Businesses looking for a well-organised and specifically designed infrastructure for data analytics will appreciate it. Since unstructured data in data lakes typically requires organisation before being put to use, highly tech-savvy individuals such as data scientists or engineers work with data lakes. Data scientists use unstructured data from data lakes in order to find patterns and important information that can be used to improve products and services based on artificial intelligence. #### 4. Security The database administrators create models that only allow authorised individuals to access the data warehouse. For the purpose of preventing data flow failures, such security models also shield databases and schemas from unauthorised intrusion. Organisations must adhere to a number of international data privacy regulations, which include the security and management of data warehouses. Regarding a data lake, the same cannot be said. A data lake, unfortunately, does not fit this description. Data Lake security is compromised by the need to grant access to numerous individuals, applications, and even outside parties. Positively, as the importance of compliance requirements for each and every type of data grows, better security controls may be enforced on data lake infrastructures, thereby ensuring a high level of security. #### 5. Pricing Since data lakes are not performance-centric, they are on the cheaper end of the pricing scale. The goal of setting up a data lake is to preserve enormous amounts of data that have not yet been assigned a specific use. Years may pass before data from data lakes is used for cognitive computing or data warehousing. Data lakes are therefore optimised to lower the cost of keeping unprocessed data. In contrast, data warehouses are made to accommodate various analytics requirements within enterprises. They must provide intensive performance in order to facilitate the development of high-quality data for insights and analysis. Due to this, data warehouses are more expensive than data lakes. However, if businesses have a great plan, the ROI from data warehouses can be enormous. Many businesses use manual labour for ETL procedures, which raises the cost of managing data warehouses. However, enterprises can streamline the entire data warehousing process to lower operational expenses by employing no-code ETL solutions. --- ## Conclusion This blog post offered a comprehensive overview of the two most common database storage options available today: data lakes and warehouses. This data warehouse vs. data lake blog gave a succinct summary and comparison of both methods. Additionally, the blog provided the criteria for evaluating each storage method. The choice of which data storage system to use depends on striking a careful balance between requirements, the value gained from data analysis, and infrastructure, storage, and processing expenses. Organisations that need to be extremely agile and handle smaller amounts of data may opt for the data lake route. A data warehouse is an option for those in sectors where data volumes are much higher and where data needs to be cleaned up to be most relevant to the widest possible audience. To offer the most flexibility, some people might pick both. Overall, the decision between a data warehouse and a data lake purely depends on the company's objectives and available resources.

Microsoft SQL Server Database to Dashboards

hello@draxlr.com (Draxlr) — Tue, 02 Aug 2022 00:00:00 GMT

Across all domains, be it e-commerce, fintech, or any domain out there, analysing data and drawing conclusions from it is a crucial factor in the growth of any organization. To explore data in meaningful ways, we need to study and make business decisions based on it, not forgetting to do a periodic review of the progress to be in line with how far we have come. Through data analysis, you can be in touch with your customers’ preferences and track your performance against competitors. Now that we have established how valuable data is as an asset to any organisation, let us dive deeper into how this data is stored and managed and how to make valuable derivations. Data is usually stored in databases, and what database you want for your organisation depends greatly on the kind of data you would be storing. On a macroscopic level, databases can be relational or non-relational. A relational database consists of tables with rows and columns. For example, in an e-commerce business, a typical client order entry database would contain a table that defines a customer with columns for name, address, phone number, and all the other information needed. Microsoft SQL is a relational database management system, or RDBMS, developed and marketed by Microsoft. With enhanced performance and low ownership costs, this is one of the favourites of organisations when choosing their database. Before you fret about not knowing how to work with databases, SQL queries, and Microsoft SQL dashboards, we need you to breathe. Sit back and relax, because Draxlr is here to do most of the work on your behalf. ## Connecting a Microsoft SQL server with Draxlr Before analysing data and creating Microsoft SQL dashboards, you would have to connect your database with Draxlr. The first step would be to register on the website **[here](https://app.draxlr.com/add-database/)**. Depending on the kind of database your organisation has, you can choose from a variety of database types, and you have to select a SQL server. ![Add Database](/images/blogs/microsoft-sql-server-database-to-dashboards/images/add-database.png) To connect your database with Draxlr, you can provide connection details like your host, port, username, password, and database name. In cases of doubt, you can reach out to your engineers to help you with the connection details. You could also schedule a free call with our team from the settings option on the top right corner, and we would be happy to resolve any queries you might have in the process. Once your connection is verified and ready to use, Draxlr will fetch the tables from your MS SQL database, and you can begin performing the operations listed below. A point to note here is that if your database is private, you will need to get Draxlr’s IP whitelisted so that it can connect to it. ![Add Database Credentials](/images/blogs/microsoft-sql-server-database-to-dashboards/images/add-credentials.png) ## The next step: deep diving into insights Now that the MS SQL database connection is ready, we can use filters, joins, and other actions on the data to get insights from it. Let us briefly understand the various operations that can be performed on the data: ### 1. Filtering: With the huge amount of data that organisations store, filtering comes in very handy because it separates out the necessary data and gives the required output. For example, in an e-commerce company that receives thousands of orders every month, we can simply apply a filter to the month and check how many new orders have come in for that particular month. ![Add Filter](/images/blogs/microsoft-sql-server-database-to-dashboards/images/add-filter.png) On Draxlr, you have to select the table on which you want to do the filter operation (which could be the orders table in the above example) and choose the column on the basis of which you need to filter. You can also save this query and use it along with other queries to build Microsoft SQL dashboards. ### 2. Sorting and ordering To see the number of orders placed in an ascending or descending manner, you can use sorting. To do the same on Draxlr, all you need to do is select which column you want to sort and in which order you want to sort it, and hit execute. ![Graphs](/images/blogs/microsoft-sql-server-database-to-dashboards/images/graphs.png) ### 3. Joining Databases have data spread over multiple tables to ensure consistency and reduce redundancy. Hence, we need to combine two or more tables in order to draw conclusions. For example, if you want to see which city placed the maximum number of orders in a day, you would have to join the customer table, which will fetch the city of the customer, and the order table, which will get the orders placed by the customers. Joins might sound scary, but you need not worry, as with Draxlr, you don't have to worry about writing the correct SQL queries. There are seven kinds of joins, which you can read briefly about in the **[blog here](https://www.draxlr.com/blogs/querying-your-data-without-writing-SQL-queries/)**. Now you just need to select the kind of join you want and the tables you want to join, and you're good to go! ![Add Join](/images/blogs/microsoft-sql-server-database-to-dashboards/images/add-join.png) ### 4. Summarising If you are asked about the total customers your organisation has acquired to date, you can use the Summarise feature on Draxlr to get the count of any particular column in a table. Moreover, if it is a numeric field, like the amount of the order placed in the orders table, you can perform different mathematical functions like getting the average of a column, the minimum, the maximum, and the total sum of all the entries for any column. ![Add Summarization](/images/blogs/microsoft-sql-server-database-to-dashboards/images/add-summarization.png) ### 5. Grouping If you want to know how many customers placed an order for item X and how many placed an order for item Y, you can simply group them on the basis of the order. On the platform, choose the table along with the column on the basis of which you want to group the data, and the results will be shown to you. Note: The best part about all the above queries is that they can be performed separately as well as together. You could apply sort and filter to your summarise and group queries, or you could join two tables and then summarise them. ## Building Graphs Database tables are easy and quick to store data, but to present—well, not so much. That is where the graphs come into the picture. They are a great pictorial representation of the data and are quite helpful in presentations and business analytics. If you select any of the above queries, then by default, the results are displayed in a tabular format, but right next to it, you can see a Graphs option. On selecting that, you get to pick from a variety of graphs. The available types of graphs are bar charts, pie charts, line charts, area charts, scatter charts, and radar charts. To understand these charts better, you can learn more about them on this **[blog](https://www.draxlr.com/blogs/setup-slack-alerts-for-sql-data-change/)**. ![Pie Chart](/images/blogs/microsoft-sql-server-database-to-dashboards/images/pie-chart.png) Graphs are great tools and come in handy in places where you need to represent data to a large number of people. They make understanding the data easier and are helpful in deriving conclusions based on the data. Not only can you represent data in graphs, but you can also give a title to your graphs, set precision, and in cases where any fields are monetary, you can format the values as currency where the currency type can be chosen. ## Building Dashboards A Microsoft SQL dashboard is basically a visual representation of multiple graphs. To create a dashboard, you have to navigate to the Dashboards section and select Add. On selecting the same, you will see all your saved queries listed, and you can choose from those to add to the dashboard. The visual of the dashboard is customizable into graphs, tables, or computed cards, and you can select the one that fits your requirements. You can do the same for multiple queries and create a dashboard. Not only that, but you can also create multiple dashboards if that is what you need. ![Dashboard](/images/blogs/microsoft-sql-server-database-to-dashboards/images/dashboard.png) These queries are updated, and results are fetched from the database at certain intervals. However, if you want updated results at any time, you can refresh it manually to see the latest data. ## Cool things to do with dashboards and data Once you have added the items to the dashboard, you can share the dashboard with anyone in multiple ways. If the dashboard is private, it can be shared with a link that is PIN-protected. Not only that, but you can also embed the Microsoft SQL Dashboard on your website or application. The embeds are domain-protected, which means that only the domains you allow can embed your dashboard. ![Dashboard Item Options](/images/blogs/microsoft-sql-server-database-to-dashboards/images/dashboard-options.png) Moreover, you can also set up alerts on your data, which can be super helpful for monitoring data. In each dashboard item on the top right corner, there is a toggle to open a menu. It has all the options for a dashboard item, and from there, you can select **Set Alerts.** To begin with, you will have two options, so you can decide whether you want the alerts via Slack or emails. For Slack, you can select a channel to get updates on your dashboard item. If you have a private Slack channel, you need to add Draxlr to that channel. As for when you want to receive updates, there are two options. You can receive an alert when any value is changed, or you can receive updates on specific days at a specific time. The updates will be sent to you as a screenshot of the dashboard item. For computed cards without a target, you will receive the direct values in alerts. In the case of tables, you will receive the data in CSV format. ![Set Alerts](/images/blogs/microsoft-sql-server-database-to-dashboards/images/dashboard.png) Working around databases can be overwhelming to start with, but with [Draxlr](https://app.draxlr.com/add-database/), the task becomes an easy nut to crack. You can write SQL queries as effortlessly as selecting the correct operation and hitting execute, and not only that, if your curious mind wants to dig deeper and see and modify raw queries, that is possible as well. Building and managing Microsoft SQL dashboards was never this easy, was it? Head over to Draxlr and explore the innumerable things you can do with your data! Working around databases can be overwhelming to start with, but with [Draxlr](https://app.draxlr.com/add-database/), the task becomes an easy nut to crack. You can write SQL queries as effortlessly as selecting the correct operation and hitting execute, and not only that, if your curious mind wants to dig deeper and see and modify raw queries, that is possible as well. Building and managing Microsoft SQL dashboards was never this easy, was it? Head over to Draxlr and explore the innumerable things you can do with your data!

Setup Slack alerts for SQL data changes

hello@draxlr.com (Draxlr) — Fri, 22 Jul 2022 00:00:00 GMT

Data analysis is one of the most critical factors that lead to success for any organization. In simple terms, data analysis is drawing conclusions on the basis of your current data and using those conclusions to optimize your organization’s performance, speed, and efficiency. Constantly analyzing your current business model can help reduce costs by identifying more efficient ways of doing business. For example, in any e-commerce company, analytics can help in understanding customer trends and satisfaction with regard to new product launches. This way, you can focus on products that are performing well by using your resources to optimize and improve them, rather than focusing on products that are not well accepted by your customers. That being said, getting insights into your data doesn't mean you have to leave your current role and spend all your free time learning to write correct SQL queries or code. Tools like Draxlr readily provide you with a full analysis of your data, which in turn helps you focus on the business side of data analytics. It involves finding patterns in your data sets, making decisions and predictions on the basis of the current data, and re-evaluating the decisions and the patterns at specific time intervals. When you focus on making better data-driven decisions, it shows up in your business, and that’s the key for any organization to progress. Setting up periodic alerts on your data is also an effective way to keep track of how the business is performing, and the alerts can easily be configured to be sent via email or directly on Slack. There are a lot of ways in which data can be presented for any company to make decisions based on it. As per your convenience, you could present your data in various types of graphs, which makes it easier to understand and explain. Not only graphs but also dashboards make life easier for any organization. Let us dive deep into how we can achieve this without actually using any SQL on our data, with the help of this simple tool. Along with that, we will also learn to set up Slack alerts easily so that we do not have to worry about looking at the data every other minute. _**Note:** Before moving ahead, you can read up [here](https://www.draxlr.com/blogs/querying-your-data-without-writing-SQL-queries/) on how to connect your database with Draxlr and the various operations you can perform on your data._ --- ## **Building Graphs** Graphs and charts are successful visual tools because they present the data quickly and clearly. They also highlight trends and comparisons in a straightforward way, which also makes them super helpful in making business decisions. After you have successfully connected your database with Draxlr, you can perform queries on the database, as explained in depth here. By default, the results are displayed in tabular format, but right next to them, you can see a Graphs option. ![Graph Option](/images/blogs/setup-slack-alerts-for-sql-data-change/images/00-graph-option.png) Once you click on that, you get to select from a variety of graphs. Below is a brief explanation of all the types of graph options available:
#### Bar Chart In this graph, you select the X-axis and the Y-axis dimensions, which are basically the columns you want to depict. It represents data in the form of rectangular bars, with their height proportional to the values that they represent. ![Bar Chart](/images/blogs/setup-slack-alerts-for-sql-data-change/images/01-bar-chart.png) #### Pie Chart It is also referred to as a circle chart, which is divided into slices to demonstrate numerical proportion. The full circle always adds up to 100, and it uses different colours to show different percentages of values. ![Pie Chart](/images/blogs/setup-slack-alerts-for-sql-data-change/images/02-pie-chart.png) #### Line Chart This type of graph connects a series of data points with a continuous line. It is mostly used to visualize a data point’s behaviour over time. ![Line Chart](/images/blogs/setup-slack-alerts-for-sql-data-change/images/03-line-chart.png) #### Area Chart It is an amalgamation of bar charts and line charts. It is used to illustrate the change in a value or multiple values against a particular constant factor, which is usually time. ![Area Chart](/images/blogs/setup-slack-alerts-for-sql-data-change/images/04-area-chart.png) #### Scatter Chart In this category of graphs, data points are plotted on a horizontal and a vertical axis in order to represent how much one value affects another. It is common practice to plot the independent variable on the X-axis and the dependent variable on the Y-axis. ![Scatter Chart](/images/blogs/setup-slack-alerts-for-sql-data-change/images/05-scatter-chart.png) #### Radar Chart Radar charts are used in places where we need to compare two or more data points on different features. In simpler terms, it is used to show multiple data points and variations between them. ![Radar Chart](/images/blogs/setup-slack-alerts-for-sql-data-change/images/06-radar-chart.png) You can pick the chart that suits your organization and its needs. In presentations, pie charts and bar graphs might help the team understand the data better, and to understand how sales have been in the past year, line charts can be of use. Not only can you choose a suitable graph, but you can also give a title to your graph. Moreover, you can also set precision, and in the case of monetary fields, you can choose to format the values as currency, wherein you have a lot of currency options to choose from. --- ## **Building Dashboards** Graphs are important, but what is even more important is for them to make sense—that is where the dashboard comes into the picture. You can combine various graphs into one dashboard so that all the data needed can be accessed right from the dashboard itself. For example, if you want various data points on the orders that your company receives every month, you would need graphs for orders received from certain cities, certain categories, and the peak time at which most orders are placed for you to boost marketing at that time. ![Dashboard](/images/blogs/setup-slack-alerts-for-sql-data-change/images/07-dashboard.png) #### Get started To get started, you can save the queries that you want to show in your dashboard. Then add a new tab under your Dashboards section. You can then add a new dashboard item that will fetch your saved queries in either table or graph format. You can also specify the refresh interval, which is, by default, 24 hours. The refresh interval is the time period after which the query again hits your database and fetches new and updated information. ![Add Dashboard Item](/images/blogs/setup-slack-alerts-for-sql-data-change/images/08-add-dashboard-item.png) #### Diving deep into the dashboard After you have imported quite a few queries, you can also perform operations like filter and share on your dashboard. For example, if you have a saved query that shows you orders placed from every city, you can add a filter for a particular city in the dashboard. Not only this, but what makes Dashboards a star product of Draxlr is the fact that it is so easy to share! You can generate a link that can either be used publicly or with a pin, in cases where you want to keep your dashboard private. You can also generate an embed link that you can embed into any HTML page. ![Create Filter](/images/blogs/setup-slack-alerts-for-sql-data-change/images/09-create-filter.png) #### Dashboard Options On every individual graph that shows up on the dashboard, you can perform a lot of operations. You can set up alerts, export the data as CSV, or just share that particular graph as a link. You can also refresh the query at any time at your convenience. Apart from that, you can edit and also remove the query if it has become irrelevant. ![Dashboard item Options](/images/blogs/setup-slack-alerts-for-sql-data-change/images/10-dashboard-item-options.png) --- ## Setting up alerts As an organization, there are umpteen things that need looking after. There is no human way for someone to monitor the data all the time and report cases of anomalies or discrepancies. For example, if you’re at a movie on a Sunday at 7 PM and a fraudster somehow decides to spam your orders and places 1,00,000 fake orders, it could cause a huge toll on your database instance, which might or might not be prepared for such a mishap. On the contrary, if some figures are organically skyrocketing, it is important for you to know about it so that you can put more effort in that direction. For all the good and bad reasons, alerting is an excellent feature that can help you keep your worries at bay and focus on building a better product. ![Set Alerts](/images/blogs/setup-slack-alerts-for-sql-data-change/images/11-set-alerts.png) You can receive alerts on Slack, email, or both. To receive alerts on Slack, you would have to connect your Slack with Draxlr. You do not have to change any SQL data for these operations; all you have to do is set it up, sit back, and focus on other essential things. For receiving email notifications, you can provide an email (or multiple emails) to which a notification will be sent whenever the condition you have specified for alerting occurs. Read on to find out about the conditions under which you can set up alerts: **Data Changes**: In this particular condition, whenever any of the data goes through any changes, a notification will be sent out via Slack, email, or both, as per your specifications. **Specific Time**: If you are someone who would want a daily report at a fixed time every day with all the data from the graph or the table, then this setting is for you. It would give you a fetch of your query at a time you specify, and on the days you select, you have the freedom to choose all the days of the week or any particular days. **Threshold-based**: In cases where your data reaches a particular checkpoint, you will receive an alert. For example, you would be notified if your orders crossed 10,000 entries a day. --- Data is a lot of unprocessed information, and for it to make sense, you have to make observations and draw conclusions on the basis of the data. Data-driven organizations are always a step ahead of their competitors because they know what their customers want and they focus hard on building it. By using graphs and dashboards, analysis becomes a lot easier, and the organization can focus on the next steps, which are planning and execution. Setting up alerts is crucial as well; it is humanly impossible to monitor data at all times, so remaining updated with your database through Slack or email alerts takes a lot of pressure off the business. However, that is not all. It is important to set up alerts and be updated with the latest figures so that you can modify the business plans accordingly. Setting up slack and email alerts and being in constant touch with data is as important as analyzing it. Building a product is not easy, but with Draxlr taking the heavy burden of analyzing data and alerting you when needed off your shoulders, you would definitely feel more independent and focused on improving your organization.

Build dashboards and set up Slack alerts on the PlanetScale database.

hello@draxlr.com (Draxlr) — Sun, 17 Jul 2022 00:00:00 GMT

PlanetScale is no doubt one of the most revolutionary database platforms. It provides one of the best developer experiences when it comes to deploying and managing MySQL databases. And it does so without compromising on performance, user experience, or feature set. The database branches and deploy requests in PlanetScale provide a safe way to make schema changes in your MySQL database. The web interface is intuitive, and if you need more control over your databases, you also have a robust CLI that gives you more control over how you are managing your database. All of this comes with the ability to scale your database to handle millions of reads and writes. What makes this possible is the underlying technology, which is Vitess. Vitess is the engine that makes it possible to scale to this enormous scale. But it comes with some compromises, like not having foreign-key constraints. This brings some problems when you try to use your PlanetScale MySQL database with some of the BI tools. At the moment, [no major BI tools](https://github.com/planetscale/discussion/discussions/186) (Metabase, Redash, Tableau, and PowerBI) properly support PlanetScale, except Draxlr. We are thrilled to announce that Draxlr now fully supports PlanetScale databases. You can query and visualise your PlanetScale MySQL data on Draxlr to build dashboards, setup Slack, and email alerts. Let’s see it in action. --- ## Connect your database Head out to [https://app.draxlr.com/](https://app.draxlr.com/) and register yourself; once the registration is done, you’ll be asked to choose a setup type. If you have your database credentials and know how your data is structured, you can click on the Manual option, and if you think you’ll need assistance setting up your database, you can click on the Assisted option and schedule a call with us. If you choose the manual setup option, you will be taken to the Add Database page. ![Add Database](/images/blogs/build-dashboards-and-setup-slack-alerts-on-your-planetScale-database/images/01-add-database.png) On the page above, select the **PlanetScale** option, then enter your database credentials. To get credentials for your Planetscale database, go to your Planetscale dashboard and select the database you want to connect to Draxlr. Then hit the connect button on the top right; it’ll open a popup with your database credentials. Then select General in the **Connect with** dropdown. Then you’ll have the credentials for your database. Except for the password, which you will have to generate each time you open this popup. ![PlanetScale Connect Button](/images/blogs/build-dashboards-and-setup-slack-alerts-on-your-planetScale-database/images/02-connect-button.png) Just fill it in on the Draxlr app and hit the **Test Connection** button. If the connection is successful, the database will be automatically connected to Draxlr. In a few seconds, you should see the tables page with a list of all the tables in your database. Click on any of the tables or hit the **Explore** button on the top right, which will take you right to the **Explore Section** of the Draxlr. ## Explore Data Explore is one of the two major sections of Draxlr (Dashboard being another one). In Explore, you can query your database to find the answers you are looking for and save them as **saved queries**. Currently, there are two ways to get those answers. Either you can dive into **raw SQL queries** or you can use our intuitive **Query Builder**, which lets you quickly find answers without writing a single line of code. Even if you can write raw SQL queries, the elegance of the query builder makes the process simpler and easier. ![Explore](/images/blogs/build-dashboards-and-setup-slack-alerts-on-your-planetScale-database/images/03-explore.png) ## Query Builder Draxlr provides a UI to filter, sort, and summarise your data. As you can see below, the query builder has multiple operations, and you can choose them to get the answer you need from your database. You can apply a complex filter and order data by a field or multiple fields. You can even summarise your data and group the results based on some metric. Once you have your answers, you can save them as a query. Which you can use later to build dashboards and set up alerts. ![Query Builder](/images/blogs/build-dashboards-and-setup-slack-alerts-on-your-planetScale-database/images/04-query.png) ## Graphs and charts Once you have applied your filters and summarizations, you can visualise your data in graphs and charts. Draxlr has powerful options to build charts. You can select from a set of supported graph options. You can select fields for each axis (multiple for the y-axis). You can add labels and titles and update orientation. In the case of grouped results, you can group the results in different ways. Not just that, you can also choose to **format** your results in the graph in a certain way (**precision**, **currency**, etc.). Once you have built your graphs, you can save or update them in your query, which you can use later to build the dashboard. ![Visualize Data](/images/blogs/build-dashboards-and-setup-slack-alerts-on-your-planetScale-database/images/05-visualize-data.png) ## Raw Query Although the query builder provides quite a lot of options to get the answers you are looking for, there are times when you just want the raw power of SQL queries. To write SQL queries, you can switch to raw query mode in Draxlr. If you are already in the query builder, you can hit the button above Execute that says **Show Raw SQL Query**. That will take you to the raw query mode with the code editor. You will get the SQL for the query that you have built using the Query Builder. You can update the query and see the results on the right side. If you want to start from scratch, you can deselect the table you are on, then hit the **Raw SQL** button above the table list. The code editor fully supports SQL; it can provide code highlights, formatting, downloading, and even automatic completion. You can save the queries that you have created using the Raw Query editor as well to use later for the dashboard and slack/email alerts. ![Raw Query](/images/blogs/build-dashboards-and-setup-slack-alerts-on-your-planetScale-database/images/06-raw-query.png) ## Dashboards The dashboard is another major section of the app. Once you have the answers you need and have saved your queries, you can go ahead and start building your dashboard. Just head out to the dashboard section and hit the **Add** button. It’ll open a modal that has a list of all your queries. You can select the query you want to add to the dashboard. Select the view (**table**, **graph**, or **computed card**) and the refresh interval. Since these queries can be quite intensive, we only execute them and update the results at certain intervals. This makes sure your database is not overloaded. If at any point you feel like you need the latest data, you can just go and refresh it manually. Once you have selected all the options, you can click **Add Dashboard Item**. And it will add the item to the current dashboard. ![Add Dashboard Item](/images/blogs/build-dashboards-and-setup-slack-alerts-on-your-planetScale-database/images/07-add-dashboard.png) Once the item is added, you can move and resize it to customise your dashboard. Not just that, you can create multiple dashboards for different use cases. And each dashboard can be shared with a different or the same set of users. You can share your dashboard with a link (PIN protected). Or you can embed it on your website or app. The embeds are domain-protected. So that only the domains you allow can embed your dashboard. ![08-dashboard.png](/images/blogs/build-dashboards-and-setup-slack-alerts-on-your-planetScale-database/images/08-dashboard.png) ## Slack and email alerts On each dashboard item, you have options to share and set up alerts. On the top right corner of each dashboard item, there is a toggle to open a menu, which has all the options for a dashboard item. You can go ahead and click on **Set Alerts**. Clicking on it will open a modal that has all the alert options. ![Dashboard Item Options](/images/blogs/build-dashboards-and-setup-slack-alerts-on-your-planetScale-database/images/09-dashboard-options.png) First, you have the option to choose the places you want to receive alerts. You can select the emails you want to send the updates to. It can be users that are part of the organisation, or it can be users from outside the organization. Another option is via Slack Alerts. You can select a slack channel to get updates on your dashboard item. Keep in mind that to receive updates on a private slack channel, you need to add Draxlr to that channel. If Draxlr wasn’t previously added to the channel, you can add it and refresh the list of channels on the popup (click on the refresh icon). Second, you can select when you want to get updates. Here you again have two options. First, you have the option to get updates when any value is changed. So every time your dashboard item is refreshed, we check if the values have changed from the last time. If it has, we will send you an update. The other option is to receive the updates on specific days at a specific time. You can select the days of the week you want to get updates on and the time of the day. In this case, you can get an update on the dashboard item on the selected days at that time. You will receive a screenshot of the dashboard item. In the case of the computed card without a target, you will receive the direct values. And in the case of a table, you will receive data in CSV format. ![Slack and Email Alerts](/images/blogs/build-dashboards-and-setup-slack-alerts-on-your-planetScale-database/images/10-slack-and-email-alerts.png) --- We are very thrilled to add the support for PlanetScale and are excited to see how you guys use it, to build your Business Intelligence dashboards and set up Alerts. If you have any doubts or need help with anything you can schedule a free call with our team to learn more on [this link](https://calendly.com/draxlr/support/). \- **[Jitendra Nirnejak](https://nirnejak.com/)**

Querying your database without using SQL queries

hello@draxlr.com (Draxlr) — Thu, 23 Jun 2022 00:00:00 GMT

If you Google the importance of data for any business, you will realize that it is indeed the most valuable asset for any organization. Right from customer acquisition to customer retention and value addition, you need to know which direction needs more work and which one is already showing results. Data is the backbone for any strategic planning that you would necessarily do for your organization, and that is where the SQL queries come into the picture. Don't fret if coding and querying are not your forte, because we are here to solve that for you. Who said you needed to know these in order to deep dive into data—not us! The more information you have, the better you know your customers, and the more you can focus on building a better product. However, with great data comes great responsibility—managing it, sorting it, analyzing it, and optimizing it for your use. If you’re wondering about the possible operations that can be done on data, let us summarize a few of the crucial ones below for you. Nonetheless, there’s good news before we move ahead: with Draxlr, you can let them handle your data, and you can just focus on results! Sounds great, doesn't it? ### Pre-requisites Before moving on, you would need to connect your database with Draxlr. Once you register on the website, you can choose from a variety of database types—MySQL, PostgreSQL, MariaDB, Databricks, and many more. After the same, you can provide the connection details, like your host and name, or you could also link the connection string (you can ask the developers in your team to help you get it). You might need to whitelist the IP of Draxlr in cases where you have a private connection to the database. Once your connection is tested and ready to use, Draxlr will fetch the tables from your database, and you can begin performing the operations listed below. ### Filtering To put things in a simple way, filtering means separating the data on the basis of something. In a database consisting of data from fifty students, you could filter by gender, birthday month, height, marks, and whatnot. Not only does filtering make the analysis easy, but it also allows us to do operations on the basis of filter groups rather than on a single record, which saves a lot of time. For example, you could filter all students who scored greater than 80% marks and send them a nice motivating email. You can perform a lot of operations on the filter, as you can see in the image below. All you have to do is select a table, choose a column on the basis of which you want to filter, and choose what filter you want to use—that's it! ![draxlr_filter.png](/images/blogs/querying-your-data-without-writing-SQL-queries/images/draxlr_filter.png) ### Sorting and ordering your data In the same class of fifty students, if you had to find out the top three students, the traditional way would be to go through all the records of the students and figure out the same. This method is prone to errors, tiring, and unreliable. With Draxlr, sort and order are as simple as any other SQL query, and you don't even have to worry about how to build the query; it is as simple as a few clicks. To sort and order a table, you need to know two things: which column you want to sort and in which order (ascending or descending). Once you select those from the drop-down, this is how your query would look: ![draxlr_sort.png](/images/blogs/querying-your-data-without-writing-SQL-queries/images/draxlr_sort.png) Now all you have to do is click on execute and sit back. Draxlr will take care of the data processing, and you will see results in a few seconds. ### Combine data from multiple tables Sometimes, querying just one SQL table doesn't suffice. Due to integrity and consistency reasons, data is usually split into two or more tables so that it doesn't repeat or cause any problems when updated or deleted. To be clearer, if you store the same field in two or more tables, you would have to update both of them when needed, and if you forget to update any one of them, the data will be inconsistent. In the class of students, we might also need to store the parents’ details. For the same, we create a separate parent table and store their numbers, addresses, and all other information. We connect this table to the student table with a foreign key, i.e., every primary key (the ID of the students’ table) will also be a key (foreign key) in the parent table. This way, you can easily find out which parent record belongs to which student by looking at the IDs. With Draxlr, you need not worry about how to join the two tables or how to create this complicated SQL query. With a few clicks, you can easily select the two tables that are linked by the foreign key and select what kind of join you want to use to join them. Below is a brief explanation of the kinds of joins present on Draxlr. We can assume that we are joining Table A with Table B. - **Inner Join**: This join selects records that have matching values in both tables. No extra records will be fetched. - **Right Join**: This join fetches all records from the right table (Table B) and the matching records from the left table (Table A). If there are no matching records, it will return all records from Table B and none from Table A. - **Right Outer Join**: This join in a SQL query is used to fetch all the matching records from both the tables involved in the join as well as all the non-matching records from Table B. This is virtually the same as Right Join. - **Left Join**: This join fetches all records from the left table (Table A) and the matching records from the right table (Table B). If there are no matching records, it will return all records from Table A and none from Table B. - **Left Outer Join**: This join in a SQL query is used to fetch all the matching records from both the tables involved in the join as well as all the non-matching records from Table A. This is virtually the same as Left Join. - **Full Outer Join**: This join selects all the records where there is a match in the left (Table A) or right (Table B) records. - **Cross Join**: This join returns the Cartesian product of rows from the tables. It combines each row from Table A with each row from Table B. Simply put, it returns all the records from both tables. Now that you are well versed in the different kinds of joins, you can easily put them to use. When you use Draxlr, you need not worry about the syntax or the execution; all you need to select is the kind of join you want and the tables that you want to join, and you’ll be served the results! Below is how you can execute the same. ![draxlr_join.png](/images/blogs/querying-your-data-without-writing-SQL-queries/images/draxlr_join.png) ### Summarize your data Summarizing the data is as important as analyzing it. To know the number of unique records, or simply the records in the database, you can choose to summarize them. And yes, you don't have to count each record—with Draxlr, it is as simple as selecting the table, selecting the column from which you want to fetch the count, and hitting execute! You can look at the image below as a reference: ![draxlr_summarize.png](/images/blogs/querying-your-data-without-writing-SQL-queries/images/draxlr_summarize.png) Apart from counting the total number of entries, you can also summarize them in other ways. For all your numerical fields, you can perform additional operations like getting the average of a column, the minimum, the maximum, and the total sum of all the entries for any column. ### Group results To explain briefly, this operation involves grouping the data on the basis of some parameter. In a class of students, if you want to know how many students have opted for science or math, you can group the students by the subject and get the number of students that have opted for it. On Draxlr’s platform, you need to select the table and the column on the basis of which you want to group the data. ![draxlr_group.png](/images/blogs/querying-your-data-without-writing-SQL-queries/images/draxlr_group.png) ### Wait, there's so much more! Now that you know that performing the above complex database queries is actually so simple with our tool, what if we tell you that there's more? Not only can you perform these queries individually, but you can also use more than one SQL query at a time. You could apply sort and filter to your summarize and group queries, or you could join two tables and then summarize them. You can use the below query as an example of how you can easily combine two or more operations. Go crazy with data—with Draxlr, there is no stopping you! ![draxlr_multiple_query_1.png](/images/blogs/querying-your-data-without-writing-SQL-queries/images/draxlr_multiple_query_1.png) Not only that, in case you need to use the results in a client presentation or in a team catchup, you can also convert the tables into graphs. Multiple options for graphs are available, and you can choose according to your ease and comfort. Not only does this make the data look more presentable, but it also enhances the ease of understanding and analyzing it. ![draxlr_graph.png](/images/blogs/querying-your-data-without-writing-SQL-queries/images/draxlr_graph.png) To add icing to the cake, you can export all your results to your local system anytime. Moreover, if your team needs some metrics on a daily basis to track progress on a feature, you can create a SQL query, share it with your team, and also save it. This way, you would not have to go through the trouble of writing the same query each time. You can create a dashboard for you and your team with all the important queries that could be present in any of the graph formats that you use. In a nutshell, with Draxlr, you spend a lot less time managing data and a lot more time analyzing it, which is a very crucial step for any business to grow. Complex SQL queries can get messy on their own, but with Draxlr, they become as simple as mixing and building blocks. If you’re still wondering how to go about executing the above, you can check out the free demo at Draxlr, which pretty much sums up all the operations they perform. Data is the future for any company looking for customer retention and acquisition because, as it is rightly said, figures don't lie. Check out Draxlr to get answers from your data while you focus on making those numbers skyrocket. Happy analyzing!

Embed and Dashboard, customise graphs, and manage your profile.

hello@draxlr.com (Draxlr) — Thu, 10 Mar 2022 00:00:00 GMT

**Here are some of the latest features that we released for Draxlr for the month of March 2022.** - Share Dashboard with PIN - Embed Dashboard Items - Select fields - Graph Customization - Manage Profile - Free Pro Trial for 14 Days --- ## 1. Share Dashboard with PIN Now on Draxlr, you can share your entire dashboard as a URL. The dashboards are private by default. You can set your dashboard as public and share it with your team or users. The shared dashboards are protected by a PIN, which you can change, update, and share with your team. Here's a video showing how it works. ## 2. Embed Dashboard Items The embedding feature is finally out. You can embed your dashboard items on your website or blog. You can either copy the embed link, which can be used on platforms like Notion and Dropbox, or directly embed your dashboard items. or alternatively, you can copy the entire embed code and add it to your website or webapp. Here's a video showing how it works. ## 3. Select fields While building your queries, you now have the option to select the fields you want to show or hide. By default, all the fields are shown. You can hide the fields you don't need by deselecting them from the show menu on the Query Builder. When you join multiple tables, you still have the option to select the fields from all the tables. The select list changes to a nested dropdown where you can choose the fields you want to show for each table in your database. Here's a video showing how it works. ## 4. Graph Customization Along with selecting or changing graph types, you can now also change the x-axis label, y-axis label, and colour of the graph. This makes it easy to change the long query-generated graph labels to the actual axis labels you want to show. You can change the colour of your graphs to make it easier to distinguish different metrics or customise them based on your organisation's design language. Here's a video showing how it works. ## 5. Manage Profile Previously, it was difficult to manage or change user-level settings without adding a database. Now the user-level and database-level settings are separated. Making it easier to change those without adding a database to Draxlr. In your profile settings, you can update your password, which was previously not available. You can see the details of your plan and upgrade to a new plan. From the profile, you can easily navigate to your database from the navbar and continue exploring your database. ## 6. Free Pro Trial for 14 Days All the new users now get a free 14-day trial of the Pro plan. You can use it to test the Pro features and see how they work. After the trial period ends, you won't be charged for the pro plan and will be downgraded to the free plan. On the free plan, the tables are limited to 5. You can upgrade to the Premium and Power plans at any time. --- The Draxlr team is always at work to improve the product and make it even better. If you have any questions or suggestions, please feel free [schedule a call with us](https://calendly.com/draxlr/support?month=2022-03) with us or email us at [hello@draxlr.com](mailto:hello@draxlr.com). \- **[Jitendra Nirnejak](https://nirnejak.com/)**

Draxlr 2.0 | Improved Onboarding, new SQL editor, and more

hello@draxlr.com (Draxlr) — Mon, 07 Feb 2022 00:00:00 GMT

Draxlr 2.0 not only introduces an entire range of new features but also optimises the implementation of all the features and makes the user interface more simplified and fluid. Let’s take a quick overview of all the new inclusions in Draxlr 2.0: 1. **Improved Onboarding Flow:** Getting started on Draxlr 2.0 is easier and faster. 2. **Update Query:** Option to update existing saved queries. 3. **New SQL Editor:** greater control over raw query building with SQL code formatting. 4. **XLSX data export:** Export your queries to Excel files. 5. **Revised data visualisation:** updated UI and implementation 6. **Charts & Graphs interaction tools:** Take a granular view of your insights with interactive tools. 7. **Download PNG:** Download data visualisations as PNG files. 8. **Updated Dashboard Building Flow:** Build dashboard elements directly from saved queries. 9. **Multiple Dashboards:** Add multiple dashboard tabs. 10. **Dashboard Element Options:** Get a shareable public URL, CSV download, embedding, and API options. 11. **Updated alerts and notifications flow:** Get data-change alerts in emails and Slack channels. We always considered Draxlr to be a no-code business intelligence platform (although it does support raw SQL queries) that could be easily used by all business professionals. A platform that could allow anyone to analyse data, build visual insights from it, track, monitor, and share the insights—all of it without writing a single line of code. The Draxlr 2.0 updates aim to expand the platform’s utility and convenience while maintaining its core ideologies. For more detailed information on Draxlr 2.0 features, feel free to visit all the **feature pages**. For detailed step-by-step instructions on how to use all the features and the platform in general, check out the **[Draxlr documentation](/docs/)**. And if you want to have a one-on-one conversation with our tech crew, feel free to **schedule a call** with us. --- ## What’s new in Draxlr 2.0? ### 1. Get started instantly with the Draxlr 2.0 onboarding flow. The onboarding flow of Draxlr 2.0 focuses on getting you started with your analytics as soon as possible. On signing up, Draxlr 2.0 offers you two different profiles: developer and product/marketing. The onboarding flow for each profile is pre-configured according to the requirements of the respective profiles. - **Developers Profile:** This leads you to the database-adding page. - **Product/Marketing profile:** This leads you to the page where you can schedule a call with our tech crew. The payment processes also come much later in the onboarding flow (which earlier came at the very beginning) on Draxlr 2.0. ### 2. Build on top of saved queries and update them. Previously, on Draxlr, you could build a query and save it, but not overwrite an existing query. On Draxlr 2.0, the **Save Query** option further offers two separate options—**Update Query** and **Save a new Query**. - **Update Query:** Overwrite an existing saved query. - **Save a new query:** Create a new saved query. ### 3. SQL Editor with Code Formatting Draxlr excels at providing [no-code business analytics tools](/features/query-builder/); however, it does cater to developers and data analysts as well. Draxlr’s **Raw Query** mode allows you to write raw SQL queries to explore and analyse your data. In Draxlr 2.0, the **Code Editor** comes with a **Format SQL** button that enables SQL code formatting. Efficiently switch between writing thorough raw queries or no-code tool-based query-building. ### 4. Export your query or data as Excel files. The earlier version of Draxlr allowed users to export their queries and data as CSV files only. In Draxlr 2.0, you can export your raw data and your query—either as **CSV** files or as **xlsx** files. The **Export Data** button pops out two selectable options — **CSV** and **Excel (xlsx)**. ### 5. Updated UI and implementation for insight visualisation In Draxlr 2.0, the available visuals are bar charts, pie charts, and line graphs. In the updated UI, the data visualisation icons have been replaced with pressable options that instantly convert your query into visuals. ### 6. Interact with your visuals with zoom, pan, and select tools. The **bar charts** and **line graphs** in Draxlr 2.0 have multiple new interaction tools. The visuals are overlaid with small interaction tool icons–Zoom, Pan, Box Select, Lasso Select, Zoom In, Zoom Out, AutoScale, and Reset axes. - The **Zoom** tool can be used to zoom into your insight visuals. - The **Pan** tool can be used to pan across your insight visuals; it works best on a zoomed-in image. - The **Box Select** tool can draw a boxed selection on your visual, wherein only the area entirely within the box gets highlighted. - The **Lasso Select** tool can draw a lasso selection on your visuals, wherein only the area inside your lasso path gets highlighted. - The **Zoom In** button can directly zoom into the visual. - The **Zoom Out** button can directly zoom out on the visual. - The **AutoScale** button can automatically resize the view of the visual according to your screen. - The **Reset Axes** button can bring your visual back to the default view of the platform. ### 7. More advanced options for visual downloading On Draxlr 2.0, you can download graphic images of your visualisations as PNG files. But the update is best reflected in the ways you can download your visualisations. On Draxlr 2.0, you can download your visuals just the way they appear on your screen—even after using the tools of interaction. For example, if you have zoomed into your bar chart or line graph, you can use the **Download plot as PNG** button to download the zoomed view as a graphic image. Draxlr 2.0 also updates the UI for downloading visuals. It has replaced the old download plot option with the new **Download Plot as PNG (**camera-like icon**)** button; downloading is available for all visuals. ### 8. An optimised flow for faster dashboard building In the earlier version of Draxlr, users had multiple ways to add insights to the [dashboard](/features/dashboard/), which seemed a bit overwhelming and even unnecessary at times. Draxlr 2.0 implements only one optimised way of building your dashboards. On the Draxlr 2.0 dashboard, with the **Add Dashboard item** button, you can directly add one of your existing saved queries as a visual card to your dashboard. While adding a saved query, you get all the options to select the visual type, refresh interval, and card label. ### 9. Add multiple dashboard tabs. One of the most useful new features in Draxlr 2.0 is the availability of multiple dashboards. On Draxlr 2.0, you can maintain multiple dashboards (with separate dashboard tabs). Represent separate issues, projects, departments, and data questions with separate dashboards. You can add multiple dashboard tabs to your dashboard window with the **Add New Tab** button; you can also rename each tab at any time. ### 10. More dashboards options All dashboard cards provide settings to access different [sharing features](/features/embedding/). The previously found **Edit** and **Remove** options remain on Draxlr 2.0, along with a few new inclusions. - The **Edit** option allows you to edit element labels, change queries, change insight visuals, and change refresh intervals. - The **Remove** option removes the visual card from the dashboard. - Use the **public URL** option to get a shareable link to the dashboard element. - The **Embed** option allows you to embed the dashboard element in a website or application. - The **API** option to make API requests. The dashboard cards also hold the interactive tool buttons—Zoom, Pan, Box Select, Lasso Select, Zoom In, Zoom Out, AutoScale, and Reset axes. ### 11. Updated alerts and notifications flow In Draxlr 2.0, you will find a bell icon on each dashboard element. The **bell icon** provides the settings for [insight monitoring](/features/alerts-and-automations/) via **email** and **Slack.** The setting-up flow on Draxlr 2.0 has been simplified. - **Email Alerts** option allows users to search for emails or manually add emails. - **Slack Alerts** option allows users to select the preferred Slack channel. Draxlr 2.0 will send screenshots of updated tables or insight visuals as alerts. --- ## Why Draxlr 2.0? The core intentions behind updating to Draxlr 2.0 are introducing relevant functionalities, improving the convenience of use, and smoothing the user experience. ### 1. Relevant Functionalities As technology evolves, so do the needs and practices of users. This means that for an application to remain relevant to user requirements, it needs to keep incorporating the latest technology trends. Draxlr 2.0 updates its insight sharing, insight monitoring, dashboard building, and query writing features to maintain relevant functionalities on the platform. ### 2. Convenience of Use Since convenience of use is a major focus point on Draxlr, the 2.0 update will simplify the steps and processes of performing an action. Draxlr 2.0 updates the onboarding flow, insight visualisation flow, and dashboard building flow. ### 3. User Experience Draxlr 2.0 updates its User Interface components and, in effect, improves the experience of platform exploration, settings tweaking, and feature utilization. The Draxlr 2.0 UI provisions aim to instill a smoother user experience on the platform. --- Draxlr 2.0 includes new features that further simplify data analysis, metrics monitoring, and insight sharing. And these new features and functionalities come with an improved flow for convenience and a fluid UI for a smoother experience. If you have any doubts about Draxlr 2.0 and its features, check out the **features in detail**, go through the **[documentation](/docs/)**, or **schedule a call** with our technical crew for a demonstration. \- **Anubhav**