The Microsoft Fabric SQL Query Editor is the home for web-based SQL development in Fabric. It gives developers a workspace to explore warehouse data, write and run SQL, among many more capabilities.
That work rarely starts and ends with a single query. Developers navigate large schemas, author and refine SQL, inspect results, share findings, and connect validated work to downstream analytics and operational workflows. As warehouses and teams grow, each of those steps can introduce friction, from finding the right object, to managing an increasing number of queries or moving between different Fabric experiences.
The latest SQL query editor updates are focused on reducing that friction and making the development experience faster, more scalable, and more connected across Fabric.
A scalable editor for any size warehouse
Working with a warehouse becomes harder when the development tools do not scale with the environment.
Large schemas can make objects difficult to navigate, metadata-heavy environments can slow down authoring assistance, and large query results can become cumbersome to inspect in the browser.
The latest updates strengthen the core web SQL query editor experiences across Object Explorer, IntelliSense, and the results grid so developers can stay productive as their warehouse grows.
To learn more about the rich capabilities the SQL query editor offers, explore the SQL query editor documentation.
A faster, more capable data grid (Generally Available)
Running a query is only useful if developers can quickly understand the output.
Large or wide result sets can be difficult to inspect when the grid is slow or when values do not fit comfortably on screen, often pushing developers to export data just to review it.
The brand-new data grid improves performance and makes data and result previews easier to inspect directly in the web SQL query editor. Developers can resize columns for wide result sets, while expanded support for larger LOB data types makes it possible to review larger values directly in the grid, with many more improvements coming soon.
Figure: Animated GIF - Brand new results grid, now with support for resizing columns.
For additional information regarding the data grid, refer to the Data preview documentation.
Object explorer built for large databases (Generally Available)
Finding the right table, view, or schema should not become harder simply because a warehouse contains thousands of objects.
The redesigned object explorer is significantly optimized for performance when navigating large warehouse environments while keeping schema browsing responsive as the number of objects grows.
Developers can also pin frequently used tables, views, and schemas, reducing the need to repeatedly navigate through large object hierarchies during everyday development.
Figure: Animated GIF - New object explorer (left) vs. old object explorer (right) loading times for thousands of user objects.
To learn more, refer to the Object explorer documentation.
IntelliSense, redesigned for scale (Generally Available)
SQL authoring becomes increasingly dependent on good database context as schemas grow. Developers should not need to remember every table, column, or object name before they can start writing a query. With improved IntelliSense responsiveness in larger warehouse environments, developers can spend less time looking up object names and more time building and refining their queries.
Better query management for development workflows
The number of queries developers work with tends to grow alongside the warehouse.
Exploratory queries become reusable queries. Saved SQL queries accumulate across projects. Queries need to be shared for team review, revisited later, or cleaned up once they are no longer useful.
New query management capabilities make that ongoing work easier to maintain directly within the query editor.
Copy and share queries with their context (Preview)
Sharing SQL often means separately copying the query, capturing its output, and explaining which results came from which version of the logic.
The new copy query experience makes it easier to keep those pieces together.
Developers can copy a query together with its results or generate a link that opens the query directly in the tool of their choice. This makes reviews, validation, and collaboration easier while reducing the extra steps required to pick the work back up in another experience.
Figure: Animated GIF - Copy query experience in the SQL query editor.
More control with autosave (Generally Available)
Not every SQL editing session represents work a developer wants to preserve in the same way. Exploratory changes may be temporary, while active development may need to be continuously protected from accidental loss.
Developers can now toggle autosave on or off, giving them more control over how changes are preserved based on the way they are working.
Figure: Animated GIF - Autosave on/off configuration in SQL query editor.
Manage queries at scale (Generally Available)
Saved queries can quickly accumulate across ongoing development, investigation, and experimentation. Managing them one at a time becomes increasingly tedious as that collection grows.
Bulk query management makes it easier to select and manage multiple queries at once, helping developers clean up old work and keep their query collections organized as projects evolve.
Figure: Animated GIF - Bulk management capabilities for queries.
Import and export .sql files (Generally Available)
SQL development often extends beyond a single tool or environment. Developers may already have queries stored as .sql files or need to move work between Fabric and other parts of their development workflow.
The SQL query editor now supports importing .sql files directly for editing, sharing and execution, as well as exporting queries as .sql files for use elsewhere.
This makes it easier to bring existing SQL into Fabric, preserve work in a portable format, and move queries between tools without manually copying and pasting code.
Figure: Animated GIF - Export queries as .sql files from the SQL query editor for development in other tools.
Extending SQL into analytics, semantics, and operations
SQL development often produces the starting point for work that continues elsewhere.
A developer may validate warehouse data in SQL and then need to analyze it with another engine, connect it to a semantic model, or use the result as part of an ongoing monitoring workflow. Moving between these experiences can interrupt the development flow and create additional steps between understanding the data and doing something with it.
New integrations make those transitions more direct from the SQL query editor.
Analyze warehouse data across OneLake (Generally Available)
Different analytical problems often call for different tools.
A developer may begin by exploring warehouse data with SQL but later need Spark for broader data processing or KQL for another analytical scenario. Traditionally, moving between engines can also introduce additional data movement or setup.
Directly from the SQL query editor, developers can now create Eventhouse endpoints or notebooks that work with the same warehouse data using KQL or Spark. This makes it easier to choose the engine that best fits the task while staying connected to the same data in OneLake. Learn more about OneLake analytics in the Eventhouse endpoint documentation.
Figure: Animated GIF - Create Notebooks and Eventhouse Endpoints directly from the SQL query editor for Spark and KQL based analysis on warehouse data.
To learn more about OneLake analytics, refer to the Eventhouse endpoint documentation.
Connect SQL to the semantic layer (Generally Available)
Validated SQL and warehouse data frequently become the foundation for downstream reporting and BI.
Without a direct path into semantic modeling, developers and BI teams often must leave the web SQL Query Editor and start that workflow separately, even when they are working from the same underlying warehouse data.
Developers now have the option to create a Direct Lake over OneLake semantic model directly from the SQL query editor, making it easier to move from exploring and validating warehouse data into building a semantic model without extra navigation or setup.
Figure: Animated GIF - Create Direct Lake semantic models from the SQL query editor.
To learn more about creating semantic models on warehouse data, refer to the Power BI semantic model documentation.
Turn SQL queries into operational workflows with Fabric Activator (Preview)
Developers often rerun the same SQL queries to monitor changing conditions and catch issues that need attention. That creates repetitive work for developers and operators who need to continually inspect business conditions or workload signals.
The new Fabric Activator integration, now in preview, makes those SQL queries in warehouse more operational. Developers can define conditions based on query results and trigger follow-up actions when those conditions are met. Instead of repeatedly running SQL to look for an issue, the query can become part of an ongoing workflow that surfaces when attention or action is needed.
Figure: Animated GIF - Creating an alert on a SQL query.To learn more, refer to the alert rule documentation.
A more complete web SQL query editor experience
These updates are designed around a simple idea: developers should spend more time working with their data and less time working around their tools.
Faster navigation and authoring reduce friction in large warehouse environments. Better query management makes ongoing SQL development easier to maintain. Deeper Fabric integrations reduce the distance between writing a query and using that work across analytics, semantic models, and operational workflows.
Together, these capabilities make the SQL query editor a more complete development experience for working with warehouse data, from finding the right object and writing SQL to validating results and carrying that work forward across Fabric.
Ready to get started? Refer to the SQL query editor documentation.
This is just the start of a series of rich investments to make the Microsoft Fabric SQL Query Editor an even more powerful and productive environment for web-based SQL development in Fabric, with many more capabilities coming soon. Stay tuned!
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