Databricks Release Notes

Serverless jobs and pipelines configured in the UI are now set to Performance optimized by default. This aligns the UI with existing defaults across APIs, Terraform, and SDKs. For more information, see Performance mode in serverless jobs and Performance mode in serverless pipelines.

• Improved text widget editor: The dashboard text widget now has an enhanced UI for formatting text, adding links, and inserting images.
• Fixed performance regressions: Resolved issues that caused performance slowdowns, including pages failing to load and URL parameters applying incorrectly.

The Google Analytics Raw Data connector in Lakeflow Connect is now generally available. See Set up Google Analytics 4 and Google BigQuery for Databricks ingestion.

You can use Python custom data sources and sinks in your pipeline definitions in Spark Declarative Pipelines on Lakeflow.

For information about Python custom data sources, see the following:

• Load data from a Python custom data source.
• Create a sink.
• PySpark custom data sources.

The SAP BDC Connector enables secure, zero-copy data sharing between SAP BDC and a Unity Catalog-enabled Databricks workspace. Access and analyze SAP BDC data on Databricks, and share Databricks data assets back to SAP BDC for unified analytics across both platforms.

See Share data between SAP Business Data Cloud (BDC) and Databricks.

Spark Declarative Pipelines on Lakeflow now supports querying the event log for metrics about the progress of a stream. See Monitor pipeline streaming metrics.

New maintenance updates are available for supported Databricks Runtime versions. These updates include bug fixes, security patches, and performance improvements. For details, see Databricks Runtime maintenance updates.

Databricks Apps now supports adding a Genie Space as an app resource to enable natural language querying over curated datasets. See Add a Genie Agent resource to a Databricks app.

Use Databricks data to build canvas apps in Power Apps, flows in Power Automate, and agents in Copilot Studio by creating a Databricks connection in Power Platform.

See Databricks and Microsoft Power Platform integration.

Serverless GPU compute now supports on-demand H100 accelerators for single-node workflows and jobs. This feature is in Beta.

See AI Runtime.

Databricks Online Feature Stores, powered by Lakebase, provide highly-scalable low-latency access to feature data while maintaining consistency with your offline feature tables. Native integrations with Unity Catalog, MLflow, and Mosaic AI Model Serving help you productionize your model endpoints, agents, and rule engines, so they can automatically and securely access features from Online Feature Stores while maintaining high performance.

See Databricks Online Feature Stores.

MLflow metadata is now available in system tables. View metadata managed within the MLflow tracking service from the entire workspace in one central location, taking advantage of all the lakehouse tooling Databricks offers, such as building custom AI/BI dashboards, SQL alerts, and large scale data analytic queries.

For information, see MLflow system tables reference.

• Active filter bar: A new filter bar shows all active filters on a dashboard. Any filter (global or page-level) not set to All, as well as any cross-filtering, appears in this bar.
• Minimum and maximum aggregations for date fields: Date fields now support minimum and maximum aggregations.
• Improved histogram labels: Histograms now display both the start and end of each bin, instead of only the start.

Agent mode for Databricks Assistant is now in Beta. In Agent mode, the Assistant can orchestrate multi-step workflows from a single prompt.

The Data Science Agent is custom-built for data science workflows and can build an entire notebook for tasks like EDA, forecasting, and machine learning from scratch. Using your prompt, it can plan a solution, retrieve relevant assets, run code, use cell outputs to improve results, fix errors automatically, and more.

See Use Genie Code for data science.

C5 compliance controls provide enhancements that help you with compliance for your workspace. C5 is a German Federal Office for Information Security (BSI) standard that defines minimum security requirements for cloud service providers. See Cloud Computing Compliance Criteria Catalog (C5).

Delta Sharing providers can now share tables backed by default storage with any recipient, including both open and Databricks recipients—even if the recipient is using classic compute. Tables with partitioning enabled are an exception.

See Limitations.

Spark Declarative Pipelines on Lakeflow includes a legacy publishing mode that previously limited publishing to a single catalog and schema. Default publishing mode enables publishing to multiple catalogs and schemas. A feature, recently released as generally available, can help migrate from the legacy publishing mode to the default publishing mode. Due to an issue found after release, the migration feature has been rolled back to Public Preview status and functionality.

See Enable the default publishing mode in a pipeline

AI agents deployed to Model Serving endpoints can use on-behalf-of-user authorization. This lets an agent act as the Databricks user who runs the query for added security and fine-grained access to sensitive data.