Databricks SQL release notes 2025

The following Databricks SQL features and improvements were released in 2025.

Databricks SQL version 2025.15 is now available in the Preview channel. Review the following section to learn about new features, behavioral changes, and bug fixes.

• Edit multiple columns using ALTER TABLE: You can now alter multiple columns in a single ALTER TABLE statement. See ALTER TABLE … COLUMN clause.

• Delta table protocol downgrade is GA with checkpoint protection: DROP FEATURE is generally available to remove Delta Lake table features and downgrade the table protocol. By default, DROP FEATURE now creates protected checkpoints for a more optimized and simplified downgrade experience that does not require any waiting time or history truncation. See Drop a Delta Lake table feature and downgrade table protocol.

• Write procedural SQL scripts based on ANSI SQL/PSM (Public Preview): You can now use scripting capabilities based on ANSI SQL/PSM to write procedural logic with SQL, including conditional statements, loops, local variables, and exception handling. See SQL scripting.

• Table and view level default collation: You can now specify a default collation for tables and views. This simplifies the creation of table and views where all or most columns share the same collation. See Collation.

• New H3 functions: The following H3 functions have been added:

  • h3_try_coverash3
  • h3_try_coverash3string
  • h3_try_tessellateaswkb

Legacy dashboards

• Official support for legacy dashboards has ended. You can no longer create or clone legacy dashboards using the UI or API. Databricks continues to address critical security issues and service outages, but recommends using AI/BI dashboards for all new development. To learn more about AI/BI dashboards, see Dashboards. For help migrating, see Clone a legacy dashboard to an AI/BI dashboard and Use dashboard APIs to create and manage dashboards.

The following updates were released during the week of April 3, 2025.

SQL editor

• Customize autoformatting options for all of your SQL queries. See Custom format SQL statements.

Visualizations

• Fixed an issue where Databricks SQL boxplot visualizations with only a categorical x-axis did not display categories and bars correctly. Visualizations now render as expected.

SQL warehouse

• CAN VIEW permission is now in Public Preview. This permission allows users to monitor SQL warehouses, including the associated query history and query profiles. Users with CAN VIEW permission cannot run queries on the SQL warehouse without being granted additional permissions. See SQL warehouse ACLs.

The following updates were released during the week of March 27, 2025.

Query profile

• Query profiles have been updated to improve usability and help you quickly access key insights. See Query profile.

The following updates were released during the week of March 20, 2025.

SQL warehouse

• You can now use the UI to transfer warehouse ownership to a service principal.

The following updates were released during the week of March 6, 2025.

Visualizations

• Dual-axis charts now support zoom: You can now click and drag to zoom in on dual-axis charts.
• Pin table columns: You can now pin table columns to the left side of the table display. Columns stay in view as you scroll right on the table. See Column settings.
• Fixed an issue with combo charts: Resolved misalignment between x-axis labels and bars when using a temporal field on the x-axis.

Databricks SQL version 2025.10 is now available in the Preview channel. Review the following section to learn about new features, behavioral changes, and bug fixes.

• In Delta Sharing, table history is enabled by default

  Shares created using the SQL command ALTER SHARE <share> ADD TABLE <table> now have history sharing (WITH HISTORY) enabled by default. See ALTER SHARE.

• Credential SQL statements return an error when there's a credential type mismatch

  With this release, if the credential type specified in a credential management SQL statement doesn't match the type of the credential argument, an error is returned and the statement is not run. For example, for the statement DROP STORAGE CREDENTIAL 'credential-name', if credential-name is not a storage credential, the statement fails with an error.

  This change is made to help prevent user errors. Previously, these statements would run successfully, even if a credential that didn't match the specified credential type was passed. For example, the following statement would successfully drop storage-credential: DROP SERVICE CREDENTIAL storage-credential.

  This change affects the following statements:

  • DROP CREDENTIAL
  • ALTER CREDENTIAL
  • DESCRIBE CREDENTIAL
  • GRANT…ON…CREDENTIAL
  • REVOKE…ON…CREDENTIAL
  • SHOW GRANTS ON…CREDENTIAL

• Use the timestampdiff & timestampadd in generated column expressions

  Delta Lake generated column expressions now support timestampdiff and timestampadd functions.

• Support for SQL pipeline syntax

  You can now compose SQL pipelines. A SQL pipeline structures a standard query, such as SELECT c2 FROM T WHERE c1 = 5, into a step-by-step sequence, as shown in the following example:

  FROM T
  |> SELECT c2
  |> WHERE c1 = 5
  

  To learn about the supported syntax for SQL pipelines, see SQL Pipeline Syntax.

  For background on this cross-industry extension, see SQL Has Problems. We Can Fix Them: Pipe Syntax In SQL (by Google Research).

• Make HTTP request using the http_request function

  You can now create HTTP connections and through them make HTTP requests using the http_request function.

• Update to DESCRIBE TABLE returns metadata as structured JSON

  You can now use the DESCRIBE TABLE AS JSON command to return table metadata as a JSON document. The JSON output is more structured than the default human-readable report and can be used to interpret a table's schema programmatically. To learn more, see DESCRIBE TABLE AS JSON.

• Trailing blank insensitive collations

  Added support for trailing blank insensitive collations. For example, these collations treat 'Hello' and 'Hello ' as equal. To learn more, see RTRIM collation.

• Improved incremental clone processing

  This release includes a fix for an edge case where an incremental CLONE might re-copy files already copied from a source table to a target table. See Clone a table on Azure Databricks.

The following features and updates were released during the week of February 13, 2025.

• Preview Unity Catalog metadata: Preview metadata for Unity Catalog assets by hovering over an asset in the schema browser. This capability is available in Catalog Explorer and other interfaces where you use the schema browser, such as AI/BI dashboards and the SQL editor.

  

• Filter to find data assets you can query Filter settings in Catalog Explorer's schema browser now includes a Can query checkbox. Selecting this option excludes objects that you can view but not query.

  

The following features and updates were released during the week of January 30, 2025.

SQL warehouse

A Completed query count chart (Public Preview) is now available on the SQL warehouse monitoring UI. This new chart shows the number of queries finished in a time window, including canceled and failed queries. The chart can be used with the other charts and the Query History table to assess and troubleshoot the performance of the warehouse. The query is allocated in the time window it is completed. Counts are averaged per minute. For more information, see Monitor a SQL warehouse.

SQL editor

• Expanded data display in charts: Visualizations created in the SQL editor now support up to 15,000 rows of data.

The following features and updates were released during the week of January 23, 2025.

Databricks SQL version 2024.50 includes the following behavioral changes, new features, and improvements.

• The VARIANT data type can no longer be used with operations that require comparisons

You cannot use the following clauses or operators in queries that include a VARIANT data type:

• DISTINCT
• INTERSECT
• EXCEPT
• UNION
• DISTRIBUTE BY

These operations perform comparisons, and comparisons that use the VARIANT data type produce undefined results and are not supported in Databricks. If you use the VARIANT type in your Azure Databricks workloads or tables, Databricks recommends the following changes:

• Update queries or expressions to explicitly cast VARIANT values to non-VARIANT data types.
• If you have fields that must be used with any of the above operations, extract those fields from the VARIANT data type and store them using non-VARIANT data types.

To learn more, see Query variant data.

• Support for parameterizing the USE CATALOG with IDENTIFIER clause

The IDENTIFIER clause is supported for the USE CATALOG statement. With this support, you can parameterize the current catalog based on a string variable or parameter marker.

• COMMENT ON COLUMN support for tables and views

The COMMENT ON statement supports altering comments for view and table columns.

• New SQL functions

The following new built-in SQL functions are available:

• dayname(expr) returns the three-letter English acronym for the day of the week for the given date.
• uniform(expr1, expr2 [,seed]) returns a random value with independent and identically distributed values within the specified range of numbers.
• randstr(length) returns a random string of length alpha-numeric characters.
• Named parameter invocation for more functions

The following functions support named parameter invocation:

• variant_explode
• variant_explode_outer
• inline
• inline_outer
• posexplode
• posexplode_outer

• Nested types now properly accept NULL constraints

This release fixes a bug affecting some Delta generated columns of nested types, for example, STRUCT. These columns would sometimes incorrectly reject expressions based on NULL or NOT NULL constraints of nested fields. This has been fixed.

The following updates were released during the week of January 15, 2025.

SQL editor

The new SQL editor (Public Preview) now has the following features:

• Download naming: Downloaded outputs are now named after the query.
• Font size adjustments: Quickly adjust font size in the SQL editor using Alt + and Alt - for Windows/Linux, or Opt + and Opt - for macOS.
• @Mentions in comments: Mention specific users with @ in comments. Mentioned users will receive email notifications.
• Improved tab switching: Tab switching performance is up to 80% faster for loaded tabs and 62% faster for unloaded tabs.
• See warehouse details: SQL Warehouse size is now visible in the compute selector without extra clicks.
• Edit parameter values: Use Ctrl + Enter for Windows/Linux, or Cmd + Enter for macOS, to run a query while editing a parameter value.
• Retain query results in version history: Query results are now stored with version history.

Visualizations

• New charts are now generally available: New charts with improved performance, enhanced colors, and faster interactivity are now generally available. See Visualization in Databricks SQL and Visualization types.