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Databricks releases, every cloud

18 releases of 1321

Last updated

  1. Standard performance mode is now available for one-time runs in the Jobs API

    Sets performance_target to STANDARD. Supports Apache Airflow's DatabricksSubmitRunOperator.

  2. Environment version 6 is now available

    Supports serverless and standard classic compute.

  3. July 6, 2026

    Updates from Databricks Runtime 18, adds Spark Declarative Pipelines and IP address functions.

  4. Lakehouse Replay (Beta)

    Automatically replays serverless workloads against upcoming runtime releases. Requires no setup.

  5. Workspace admin setting for serverless notebook execution timeout

    Replaces manual override process, defaults to 2.5 hours.

  6. Version 18.2

    Adds CREATE OR REPLACE TEMP TABLE syntax and upgrades Snowflake JDBC driver to 3.28.0.

  7. Run the change, do not just read it. Hands-on labs in your own Databricks workspace, graded when you submit. Browse labs
  8. Faster package installs with %uv pip in serverless notebooks

    Requires environment version 5 or above. Replaces %pip for faster installs.

  9. Spark Declarative Pipelines on Lakeflow updates are retained for 60 days

    Retention period increased from 30 to 60 days, applying to UI and REST API updates.

  10. Version 18.1

    Supports DATETIMEOFFSET for Azure Synapse and adds schema evolution with INSERT statements.

  11. SQL alert task for jobs (Beta)

    Evaluates SQL alerts as part of workflows, enabling automated condition checks and notifications.

  12. Version 18.0

    Upgrades Redshift JDBC driver to 2.1.0.28, adds SQL window functions to metric views.

  13. Serverless environment version 5 is now available

    Includes CPU and GPU versions.

  14. New GCP regions support serverless compute

    Available in me-central1 and southamerica-east1 regions.

    AWS(not on this cloud)Azure(not on this cloud)GCP, read it on their docsSAP(unknown)
  15. You can now use custom base environments for Python, Python Wheels, and notebook tasks in serverless jobs

    Defined with YAML files, they support Python, Python wheel, and notebook tasks.

  16. Serverless workspaces are now available in new GCP regions

    Available in most GCP regions, see documentation for list.

    AWS(not on this cloud)Azure(not on this cloud)GCP, read it on their docsSAP(unknown)
  17. Serverless notebook tasks can now use jobs environment

    Replaces inherited serverless environment with configurable job environment.

  18. Serverless compute has been updated to version 17.3

    Updated from version 17.2, requires no configuration changes.

  19. The billable usage table now records the performance mode of serverless jobs and pipelines

    Records performance mode in product_features.performance_target column with values PERFORMANCE_OPTIMIZED, STANDARD, or null.

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Headlines, dates and product areas are Databricks' own, from the release notes published for each cloud, and every item links to the note it came from. The one-line summaries are ours. Not a Databricks product and not affiliated with Databricks, Inc.