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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, Delta Live Tables, 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 |
|---|---|---|---|
| - 15.4 LTS - 14.3 LTS - 13.3 LTS - 12.2 LTS - See more… |
- 15.4 ML LTS - 14.3 ML LTS - 13.3 ML LTS - 12.2 ML LTS - See more… |
- 16.2 - 16.1 - 16.0 - See more… |
- 16.2 ML - 16.1 ML - 16.0 ML - See more… |
For a complete list of supported runtimes and version compatibilities, 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:
- February 2025 release notes
- January 2025 release notes
- December 2024 release notes
- November 2024 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. |
| Delta Live Tables | A declarative framework designed to simplify the creation of reliable and maintainable extract, transform, and load (ETL) pipelines. |
| Serverless compute | Run your Databricks workloads without configuring and deploying infrastructure. |
For information about the release process and upcoming features, see the following articles: