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Databricks releases, every cloud

48 releases of 1357

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  1. 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.

  2. New resource types for Databricks Apps

    Adds MLflow experiments, vector search indexes, UDFs, and Unity Catalog connections as resources.

  3. Configure compute size for Databricks Apps (Public Preview)

    Controls CPU, memory, and cost based on workload requirements.

  4. List MCP servers in Databricks Marketplace (Public Preview)

    Lets users install MCP servers to connect AI agents to external data sources.

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

    Replaces inherited serverless environment with configurable job environment.

  6. Serverless compute has been updated to version 17.3

    Updated from version 17.2, requires no configuration changes.

  7. Run the change, do not just read it. Hands-on labs in your own Databricks workspace, graded when you submit. Browse labs
  8. 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.

  9. Admins can now manage a workspace's serverless base environments (Public Preview)

    Defines custom environment specs for serverless notebooks, allows setting a default for new notebooks.

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