Databricks Release Notes

The Microsoft SQL Server connector in Lakeflow Connect now supports SCD type 2. This setting, known as history tracking or slowly changing dimensions (SCD), determines how to handle changes in your data over time. With history tracking off (SCD type 1), outdated records are overwritten as they're updated and deleted in the source. With history tracking on (SCD type 2), the connector maintains a history of those changes. See Enable history tracking (SCD type 2).

The OLTP Database tab in the Compute section of the workspace is renamed to Lakebase Postgres. Use this tab to create and manage your Lakebase database instances.

See Manage projects.

Use the cell execution minimap to track your notebook’s progress at a glance. The minimap appears in the right margin and shows each cell’s execution state (skipped, queued, running, success, or error). Hover to see cell details, or click to jump directly to a cell.

For information about using the cell execution minimap, see Navigate the Databricks notebook and file editor.

Base environments are custom environment specifications for serverless notebooks that define a serverless environment version and a set of dependencies.

Workspace admins can now create and manage the base environments available in their workspace. They can also set a default base environment for all new serverless notebooks. This allows workspace users to quickly start working in a consistent, cached environment.

See Manage workspace base environments.

You can now create synced tables in Snapshot sync mode from Iceberg tables or foreign tables.

See Serve lakehouse data with synced tables.

Users can now connect Databricks to external Model Context Protocol (MCP) servers. This allows agents to access tools outside of Databricks.

See MCPs.

Spark Declarative Pipelines on Lakeflow has a legacy publishing mode that only allowed publishing to a single catalog and schema. The default publishing mode enables publishing to multiple catalogs and schemas. Migration from the legacy publishing mode to the default publishing mode is now generally available.

See Enable the default publishing mode in a pipeline

• Fixed cloning issue for large spaces: Spaces with a large number of instructions can now be cloned successfully.
• Fixed CSV download issue on Windows: Downloaded query result files now open reliably on Windows.

Set a default warehouse that will be automatically selected in the compute selector across the SQL editor, AI/BI dashboards, Genie Agents (formerly Genie Spaces), Alerts, and Catalog Explorer. Individual users can override this setting by selecting a different warehouse before running a query. They can also define their own user-level default warehouse to apply across their sessions. See Set a default SQL warehouse for the workspace and Set a user-level default warehouse.

You can now create governed tags to enforce consistent tagging across data assets such as catalogs, schemas, and tables. Using governed tags, admins define the allowed keys and values and control which users and groups can assign them to objects. This helps standardize metadata for data classification, cost tracking, access control, and automation.

See Governed tags.

Databricks Runtime 17.2 and Databricks Runtime 17.2 ML are now in Beta. These releases include SQL and API enhancements, new migration options, and reliability and performance improvements across the platform.

See Databricks Runtime 17.2 (EoS) and Databricks Runtime 17.2 for Machine Learning (EoS).

INSERT REPLACE USING and INSERT REPLACE ON are now generally available for Databricks Runtime 17.2. For details, see the Databricks Runtime 17.2 release notes.

OAuth token federation is now generally available. Token federation enables you to securely access Databricks APIs using tokens from your identity provider (IdP). You can configure token federation policies directly in the Databricks UI, or using the Databricks CLI or REST API.

See Configure a federation policy.

The Microsoft SQL Server ingestion connector in Lakeflow Connect is now generally available. See Configure Microsoft SQL Server for ingestion into Databricks.

You can now explicitly set the compression codec for a Delta table using the delta.parquet.compression.codec table property. This property ensures that all future writes to the table use the chosen codec. See Table properties reference.

For newly created synced tables, TIMESTAMP types in source tables are now mapped to TIMESTAMP WITH TIMEZONE in synced tables. In existing synced tables, TIMESTAMP types continue to map to TIMESTAMP WITHOUT TIMEZONE.

See Serve lakehouse data with synced tables.

You can now use automatic liquid clustering with Spark Declarative Pipelines on Lakeflow. Use automatic liquid clustering with CLUSTER BY AUTO, and Databricks intelligently chooses clustering keys to optimize query performance.

See Automatic liquid clustering, create_streaming_table, table, CREATE MATERIALIZED VIEW (pipelines), and CREATE STREAMING TABLE (pipelines).

You can now tag a database instance and a synced table with a budget policy to attribute billing usage to specific policies. Additionally, custom tags can be added to a database instance for more granular attribution of compute usage to teams, projects, or cost centers.

See Manage projects, Serve lakehouse data with synced tables, and Custom tags.