Databricks release notes

The following sections organize Databricks release notes by release type, including Databricks Runtime releases, platform releases, and feature-specific releases such as Databricks SQL, Lakeflow Declarative Pipelines, and serverless compute.

Databricks Runtime release notes

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
16.4 LTS 15.4 LTS 14.3 LTS 13.3 LTS See more…	16.4 LTS ML 15.4 LTS ML 14.3 LTS ML 13.3 LTS ML See more…	17.0 16.4 LTS 16.3 16.2 See more…	17.0 ML 16.4 ML LTS 16.3 ML 16.2 ML See more…

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 Databricks platform features, improvements, and fixes are found in the following articles:

For all platform release notes, see Databricks platform release notes.

The following 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.
Lakeflow Declarative Pipelines	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.
Databricks feature engineering	Create, read, and write feature tables. Train models on feature data. Publish feature tables to online stores for real-time serving.
Databricks on AWS GovCloud	Databricks platform features, improvements, and fixes on AWS GovCloud.

For information about the release process and upcoming features, see the following articles: