Note
Access to this page requires authorization. You can try signing in or changing directories.
Access to this page requires authorization. You can try changing directories.
The following sections organize Azure Databricks release notes by release type, including Databricks Runtime releases, platform releases, and feature-specific releases such as Databricks SQL, DLT, and serverless compute.
The following table provides links to release notes for the latest Databricks Runtime releases.
| Long-term support (LTS) runtimes | Machine learning (ML) long-term support runtimes | Latest Databricks Runtimes | Latest Databricks Runtimes for ML |
|---|---|---|---|
For a complete list of supported runtimes, version compatibilities, and available Beta releases, see Databricks Runtime release notes versions and compatibility.
Release notes for the latest Azure Databricks platform features, improvements, and fixes are found in the following articles:
- April 2025 release notes
- March 2025 release notes
- February 2025 release notes
- January 2025 release notes
For all platform release notes, see Azure Databricks platform release notes.
The following Azure Databricks features have their own dedicated release note articles:
| Feature | Description |
|---|---|
| AI/BI | A business intelligence product that includes dashboards for visualization and reporting, plus Genie for conversational analytics. |
| Databricks SQL | The collection of services supporting data warehousing and querying features on the Databricks Data Intelligence Platform. |
| Databricks dev-tools and SDKs | IDE extensions, plugins, command-line interfaces, SDKs, and SQL connectors and drivers. |
| Databricks Connect | Connect IDEs, notebook servers, and other custom applications to Databricks compute. |
| Databricks Asset Bundles | Databricks Assets Bundles are an infrastructure-as-code (IaC) approach to managing Databricks projects. |
| DLT | A declarative framework designed to simplify the creation of reliable and maintainable extract, transform, and load (ETL) pipelines. |
| Databricks feature engineering | Create, read, and write feature tables. Train models on feature data. Publish feature tables to online stores for real-time serving. |
For information about the release process and upcoming features, see the following articles: