Databricks Releases
Databricks blog and Terraform provider releases. New features, breaking changes, security advisories and deprecations - each summarised in plain English and updated continuously.
Tracking 181 Databricks releases · Updated
- Databricks Java SDK Releases sdkdatabricks ·
Databricks SDK Java v0.123.0: API additions and breaking changes
Databricks SDK Java version 0.123.0 introduces several new API methods and fields across various services including policy compliance, Genie, bundles, and PostgreSQL. It also includes internal changes like a JDK 17 fallback script and some breaking changes, such as removing the 'name' field from the User object and making 'replicateWorkspaceAssets' no longer required. These updates affect developers using the Java SDK for Databricks interactions.
breaking patch - Databricks Go SDK Releases sdkdatabricksengineer ·
Databricks SDK for Go v0.152.0: Breaking change in query parameter serialization
Databricks SDK for Go v0.152.0 introduces a breaking change where query parameters listed in `ForceSendFields` are now serialized with their explicit values, including zero values. This impacts callers that previously relied on these parameters being silently omitted. The release also includes several bug fixes, particularly around `ForceSendFields` honoring query parameters, and numerous API additions across services like disaster recovery, dashboards, Postgres, and serving endpoints.
breaking patch - Databricks Python SDK Releases sdkinfradatabricksengineer ·
Databricks SDK Python v0.119.0 adds policy compliance and DR features
Databricks SDK Python version 0.119.0 introduces new features and API enhancements, including expanded policy compliance capabilities and disaster recovery support. These updates allow for more granular control over cluster enforcement and asset replication for disaster recovery scenarios. The release impacts developers using the Python SDK, particularly those working with workspace compute policies and disaster recovery configurations.
breaking patch - Databricks Go SDK Releases sdkinfradatabricksengineer ·
Databricks SDK Go v0.150.0: API additions, policy compliance, and breaking change
Databricks SDK Go version 0.150.0 introduces new methods and fields for managing cluster policy compliance, enhancing disaster recovery capabilities, and fixing a user agent bug. It also includes a breaking change related to workspace asset replication. This release affects developers using the Go SDK for Databricks automation and management.
breaking patch - Databricks Java SDK Releases sdkinfradatabricksengineer ·
Databricks SDK Java v0.120.0: API Additions and Breaking Change
Databricks SDK Java version 0.120.0 introduces new methods for managing Postgres data APIs and adds several fields to compute and database specifications. A significant breaking change modifies the `resourceId` field in the `Operation` class to be optional. These updates are relevant for developers using the Java SDK to interact with Databricks services, particularly those managing compute resources or database integrations.
breaking patch - Databricks Go SDK Releases sdkinfradatabricksengineer ·
Databricks SDK Go v0.145.0: Bug fixes and API enhancements
Databricks SDK Go version 0.145.0 includes bug fixes for tag policy routing and several API changes, including a breaking change to the `ResourceId` field in bundle deployments. These updates affect developers working with tags, bundle deployments, and data catalog connections. The release also introduces new fields for vector indexes and adds a new connection type.
breaking patch - Databricks Python SDK Releases sdkinfradatabricksengineer ·
Databricks SDK for Python v0.117.0
Databricks SDK for Python version 0.117.0 introduces new fields for Synced Tables and makes a breaking change to the `resource_id` field in `Operation`. It also includes significant bug fixes for token caching and lazy initialization of `WorkspaceClient.dbutils` to improve performance and stability, particularly in Spark Connect environments. The release also declares `urllib3` as an explicit dependency.
breaking patch - Databricks Java SDK Releases sdkinfradatabricksengineer ·
Databricks SDK Java v0.119.0: New API Packages and Breaking Changes
Databricks SDK Java version 0.119.0 introduces new API packages for AI Search and Bundle Deployments, alongside numerous field additions across various services. Notably, it removes the `com.databricks.sdk.service.bundle` package and its corresponding workspace client service, which are breaking changes. These updates primarily affect developers integrating with Databricks services via the Java SDK, requiring attention to the removal of the bundle service.
breaking patch - Databricks Python SDK Releases sdkaidatabricksengineer ·
Databricks SDK for Python v0.116.0: New APIs and Breaking Changes
Databricks SDK for Python v0.116.0 introduces new packages for AI Search and Bundle Deployments, along with numerous field additions across services like Catalog, ML, and Vector Search. This release also includes breaking changes with the removal of the `databricks.sdk.service.bundle` package and the associated workspace-level service. These updates primarily affect developers using the Python SDK to interact with Databricks services, offering expanded capabilities while requiring adjustments for bundled application deployments.
breaking patch - Databricks Go SDK Releases sdkinfradatabricksengineer ·
Databricks SDK for Go v0.142.0: New Services and Breaking Changes
Databricks SDK for Go version 0.142.0 introduces new packages for AI Search and Bundle Deployments, along with numerous new fields across various services like Catalog, ML, and Vector Search. This release also includes breaking changes by removing the `bundle` package and its associated workspace-level service. These updates affect Go developers utilizing the Databricks SDK, particularly those working with AI features, deployment pipelines, or relying on the deprecated bundle functionality.
breaking patch - Databricks Python SDK Releases sdkinfradatabricksengineer ·
Databricks SDK for Python v0.113.0
Databricks SDK for Python v0.113.0 introduces internal workspace addressing changes and adds new API methods for feature engineering and token management. It also introduces several new fields related to job deployments, pipeline tasks, and token settings, while removing two fields from the postgres service. These changes affect developers interacting with Databricks services programmatically, with some breaking changes requiring attention.
breaking patch - Databricks Go SDK Releases sdkinfradatabricksengineer ·
Databricks SDK for Go v0.139.0: Feature Engineering API and breaking changes
Databricks SDK for Go version 0.139.0 introduces new methods for managing Feature Engineering streams and adds parameters to job and pipeline configurations. This release also includes breaking changes with the removal of `CatalogId` and `SyncedTableId` fields from PostgreSQL catalog status types, which may require users to update their integrations. The update is relevant for Go developers working with the Databricks platform.
breaking patch - Databricks Java SDK Releases sdkdatadatabricksengineer ·
Databricks SDK for Java v0.113.0 adds feature engineering APIs, includes breaking changes
Databricks SDK for Java version 0.113.0 introduces new methods for managing streaming objects within the feature engineering service. It also adds several new fields to existing Job and Pipeline task configurations. However, this release includes breaking changes, removing the `catalogId` and `syncedTableId` fields from specific Postgres-related status objects, which may require consumers to update their code.
breaking patch - Databricks Java SDK Releases sdkinfradatabricksengineer ·
Databricks SDK for Java v0.112.0: API additions and breaking changes
Databricks SDK for Java version 0.112.0 introduces several new methods and fields across various services, including workspace, IAM, jobs, and ML. Notably, it includes breaking changes related to required fields in bundle operations, tag types for marketplace listings, and pagination for cluster events. These updates primarily affect developers building applications and integrations with Databricks using the Java SDK.
breaking patch - Databricks Python SDK Releases sdkinfradatabricksengineer ·
Databricks SDK for Python v0.111.0: Bundle package, Lakeview revert, Postgres undelete
Databricks SDK for Python version 0.111.0 introduces a new bundle package and workspace-level services for Lakeview and Postgres, alongside several field additions across various modules. These changes primarily affect developers using the Python SDK for interacting with Databricks services. Notably, two breaking changes are included regarding tag handling for marketplace listings and pagination for cluster events.
breaking patch - Databricks Go SDK Releases sdkinfradatabricksengineer ·
Databricks SDK Go v0.136.0: New features and breaking changes
Databricks SDK Go v0.136.0 introduces several new API methods and fields, enhancing functionality for Postgres workspace services and job management. It also includes breaking changes to the bundle operations and marketplace listing requests, requiring users to update their code for compatibility. This release primarily impacts developers using the Go SDK to interact with Databricks services.
breaking patch - Databricks Go SDK Releases sdkinfradatabricksengineer ·
Databricks SDK Go v0.133.0 Introduces New IAM and Feature APIs
Databricks SDK Go version 0.133.0 adds significant new functionality for managing IAM assignments at both account and workspace levels, alongside enhancements for feature engineering and job task configurations. These updates benefit developers and architects working with Databricks for ML and data engineering, introducing new APIs and fields to support more complex use cases. Notably, several breaking changes are included, requiring developers to update their code when integrating these new features.
breaking patch - Databricks Python SDK Releases sdkdatadatabricksengineer ·
Databricks SDK for Python v0.109.0: New IAM, ML, and Serving features
Databricks SDK for Python version 0.109.0 introduces new methods for managing workspace assignments and several new fields across IAM, ML, and serving functionalities. Notably, it includes breaking changes to the `ListFeaturesRequest` and `list_features` method, impacting how features are requested and listed. This release is primarily for Python developers working with the Databricks platform, particularly those involved in account and workspace management, feature engineering, and model serving.
breaking patch - Databricks Java SDK Releases sdkdatabricksengineer ·
Databricks SDK for Java v0.108.0 includes breaking API changes
Databricks SDK for Java version 0.108.0 introduces several breaking changes, including modifications to Feature API fields and method signatures, and the removal of a resource name field. These changes primarily impact Java developers using the Databricks SDK for feature engineering and serving, requiring code updates for compatibility. New enum values for GPU workload types have also been added, alongside other feature enhancements.
breaking patch - Databricks Python SDK Releases sdkdatadatabricksengineer ·
Databricks SDK for Python v0.108.0 Updates
Databricks SDK for Python v0.108.0 introduces new fields for job task configuration and adds new connection types for catalog integrations. It also includes a breaking change by removing an unspecified resource name field. This release impacts developers building and managing Databricks workloads using the Python SDK.
breaking patch
About Databricks release tracking on ReleaseBytes
Databricks platform releases, runtime versions and Terraform provider updates each have their own changelog. ReleaseBytes merges them into one feed with plain-English summaries, and its EOL tracker follows Databricks runtime support windows — including the Python and Spark versions each runtime pins.
Frequently asked questions
How often are Databricks release notes updated on ReleaseBytes? ›
Continuously. ReleaseBytes monitors the official Databricks release channels around the clock and publishes a plain-English summary of each announcement shortly after it lands.
What kinds of Databricks changes does ReleaseBytes track? ›
New features, enhancements, bug fixes, security advisories, breaking changes, deprecations and end-of-life announcements. Every item is tagged by type so you can filter to just the changes that need action.
How can I get alerts for new Databricks releases? ›
Set up a free email or Slack alert filtered to Databricks, subscribe to the weekly digest, or follow the RSS feed. Teams can also install the ReleaseBytes GitHub App or connect via MCP.
Where does the Databricks release data come from? ›
From the official sources: Databricks blog and Terraform provider releases. Every item links back to the original vendor announcement.