Databricks Release Notes

You can now deploy Databricks apps directly from Git repositories without uploading files to the workspace. Configure a repository for your app and deploy from any branch, tag, or commit. See Deploy from a Git repository.

The managed Google Ads connector in Lakeflow Connect allows you to ingest data from Google Ads into Databricks. See Google Ads connector.

For workspaces enabled for customer-managed keys (CMK) for managed services, the system catalog's data encryption settings can be configured to decrypt the statement_text and error_message fields in the query history system table. See Reading encrypted fields.

Using CREATE, ALTER, or the Lakeflow UI to modify ETL and ingestion pipelines (Lakeflow Spark Declarative Pipelines and Lakeflow Connect) is now GA. You can modify pipelines to apply row filters, columns masks, table and column tags, column comments, and (for materialized views only) table comments.
See ALTER STREAMING TABLE and ALTER MATERIALIZED VIEW. For general information about using ALTER with Lakeflow Spark Declarative Pipelines, see Use ALTER statements with pipeline datasets.

Mosaic AI Model Serving now supports Anthropic Claude Opus 4.6 as a Databricks-hosted model.

To access this model, use:

• Foundation Model APIs pay-per-token

  • Query reasoning models
  • Query vision models

• Batch inference workloads using AI Functions

Default SQL warehouse settings are now generally available

Workspace administrators can set a default SQL warehouse that is automatically selected in SQL authoring surfaces, including the SQL editor, AI/BI dashboards, AI/BI Genie, Alerts, and Catalog Explorer. Individual users can also override the workspace default by setting their own user-level default warehouse. See Set a default SQL warehouse for the workspace and Set a user-level default warehouse.

You can now view detailed annotations on the Running clusters chart in the SQL warehouse monitoring UI to understand why warehouses remain active. The Activity details toggle displays color-coded bars that show query activity, fetching queries, open sessions, and idle states. Hover over bars to see metadata, or click on fetching activity to filter the query history table. See Monitor a SQL warehouse.

You can now connect Databricks Assistant in agent mode to external tools and data sources through the Model Context Protocol (MCP). The Assistant can use any MCP servers that have been added to your workspace and that you have permission to use.

See Connect Databricks Assistant to MCP servers.

You can now directly select Databricks tables from the catalog explorer and import data as pivot tables in Google Sheets using the Databricks Connector. See Connect to Databricks from Google Sheets.