Databricks Release Notes

You can now create a materialized view that is compatible with external Iceberg readers. This feature is in Public Preview. To enable it, reach out to your Databricks account team. See Create a materialized view compatible with external Iceberg readers.

Excel file format support is now available by default for all workspaces. You can ingest, parse, and query .xls, .xlsx, and .xlsm files directly using built-in support, without external libraries or manual conversions. See Read and stream Excel files.

Materialized view and streaming table refreshes that are orchestrated through a SQL task in a Lakeflow job now respect the job's Performance optimized setting. When the setting is turned off, the refresh uses standard performance mode, which consumes fewer DBUs.

For more about performance modes for scheduled refreshes, see Select a performance mode for scheduled refreshes.

Budgets, including budgets for Unity Gateway, are now generally available.

Budgets let admins define spending thresholds. For requests managed through Unity Gateway, including Genie interactions, admins can set additional per-user thresholds and overrides, with configurable actions such as email alerting and usage blocking.

See Create and monitor budgets and Manage budgets for Unity Gateway.

You can now use Private Link to privately connect to Databricks account-level resources, such as the account console, through a VPC interface endpoint instead of the public internet. See Configure inbound Private Link for account-level resources.

Okta support for automatic identity management is now generally available. Automatic identity management lets you sync users and groups from Okta into Databricks without SCIM provisioning. When enabled, you can search for users and groups directly in identity federated workspaces and add them to your workspace, and Databricks uses Okta as the source of record for group memberships.

See Configure Okta for automatic identity management.

Row filtering in Lakeflow Connect is now generally available. Row filtering allows you to ingest only the rows you need by applying conditions similar to a SQL WHERE clause, improving performance and minimizing data duplication. Row filtering applies during both the initial load and subsequent incremental updates, and is supported for SaaS connectors including Google Analytics, Salesforce, and ServiceNow, as well as all query-based connectors.

See Select rows to ingest.

• Dashboard relationships (Public Preview): Build complex multi-fact data models scoped to an AI/BI dashboard. See Dashboard relationships.

The Veeva Vault connector is now available in Lakeflow Connect in Beta. Workspace admins can control access to this feature from the Previews page.

The connector uses Veeva's Direct Data API to ingest all standard objects, custom objects, system tables, and audit trail tables from your Vault instance into Databricks. Authentication uses OAuth 2.0 M2M via Microsoft Entra ID.

See Veeva Vault connector.

The integrated CDC pipeline for MySQL is now in Beta. An integrated CDC pipeline ingests change data from MySQL into Databricks using a single pipeline, combining extraction and application into one pipeline update. Unlike the standard gateway-based architecture, an integrated CDC pipeline requires no separate ingestion gateway. See Create an integrated CDC pipeline for MySQL.

Model Serving now supports Google Gemini 3.1 Flash Image and Gemini 3 Pro Image as Databricks-hosted models. You can access these models using Foundation Model APIs.

Customers are responsible for ensuring their compliance with applicable terms.

Databricks SQL and Databricks Runtime now support the SQL:2016 MATCH_RECOGNIZE clause for row pattern recognition. Enable the Match Recognize preview in workspace preview settings. See MATCH_RECOGNIZE clause.

Lakebase is now available in ap-northeast-1 (Asia Pacific - Tokyo). For all supported regions, see Region availability. To get started, see Get a Postgres database.

Model Serving now supports Anthropic Claude Sonnet 5 as a Databricks-hosted model. Designed for coding, agentic workflows, and professional work at scale, Claude Sonnet 5 delivers near-Opus-level intelligence with Sonnet's cost efficiency and speed. You can access this model using Foundation Model APIs.

Customers are responsible for ensuring their compliance with applicable terms.

When you run a subset of a job's tasks, you can now expand your selection to include each task's dependencies. Add the + modifier to a task key to also run its upstream tasks (+my_task), its downstream tasks (my_task+), or both (+my_task+). Start a partial run from the UI, the REST API, the Databricks CLI, the SDKs, or Declarative Automation Bundles. See Run a subset of tasks and their dependencies.

Lakehouse Real-Time (Lakehouse//RT) is now in Beta. Lakehouse//RT is a serverless SQL warehouse type built for low-latency, high-concurrency workloads, delivering sub-second SQL read queries against Unity Catalog tables. Use it to serve analytical data to applications, run operational analytics, and power dashboards for hundreds to thousands of concurrent users. See Lakehouse Real-Time.

The SQL alert task in Lakeflow Jobs is now generally available. Use the task to evaluate a Databricks SQL alert as part of a Lakeflow Job. The task returns its evaluation state as a task output value, so downstream tasks can branch on the result. See SQL alert task for jobs.