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 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 Java SDK Releases sdkdatadatabricksengineer ·
Databricks SDK Java v0.118.0: Canonical Bearer token handling
Databricks SDK for Java version 0.118.0 introduces a bug fix for canonicalizing Bearer tokens in Authorization headers. This change ensures consistent handling of tokens, which is crucial for authentication and authorization in API interactions. The update includes an API change with a new `getCanonicalTokenType()` method for the `Token` class. This release is for developers using the Java SDK.
patch - Databricks Java SDK Releases sdkaidatabricksdeprecationengineer ·
Databricks SDK Java v0.117.0: Enhanced AI agent detection and pagination
Databricks SDK Java version 0.117.0 enhances AI agent detection by recognizing the AI_AGENT environment variable, improving user agent reporting for recognized AI tools. It also introduces explicit factory methods for pagination strategies, deprecating the direct constructor. Bug fixes address issues with paginators silently dropping results on empty pages. These updates benefit developers using the SDK for interacting with Databricks AI features and data listing operations.
patch - Databricks Java SDK Releases sdkdatadatabricksengineer ·
Databricks Java SDK v0.116.0: Optional Client ID in OAuth
Databricks Java SDK v0.116.0 allows the client ID to be optional in OAuth token acquisition. This fix prevents NullPointerExceptions for users authenticating via browser OAuth flows where the IdP JWT lacks a client ID, enabling account-wide token federation by omitting the parameter when it's absent. The change impacts developers using the Java SDK for OAuth authentication.
patch - Terraform Databricks Provider Releases terraforminfradatabricksengineer ·
Databricks Terraform Provider v1.117.0: Fix External Location File Events
Databricks Terraform Provider version 1.117.0 fixes a bug in `databricks_external_location` where setting `enable_file_events` to `false` was ignored. This change ensures that when explicitly set to false, the provider now correctly sends this configuration to the Databricks API, preventing unexpected default behavior for file event monitoring. The fix targets users managing external locations via Terraform, ensuring their configuration for file events is respected.
patch - Databricks Java SDK Releases sdkinfradatabricksengineer ·
Databricks SDK Java v0.115.0 adds deployment mode fields
Databricks SDK Java version 0.115.0 introduces new fields related to deployment mode to the `Deployment` and `Version` classes within the bundle service. It also adds connectivity fields to the `Setting` class in the settingsv2 service. These changes enhance configuration and management capabilities for Databricks deployments.
patch - Databricks Python SDK Releases sdkdatabricksengineer ·
Databricks SDK for Python v0.114.0
Databricks SDK for Python v0.114.0 introduces new fields for deployment modes and collaboration platform connectivity. These updates enhance configuration options for Databricks deployments and integrations. The changes are available now and primarily affect developers working with the Databricks SDK.
patch - Databricks Go SDK Releases sdkinfradatabricksengineer ·
Databricks SDK for Go v0.141.0 Adds Bundle Deployment Options
Databricks SDK for Go version 0.141.0 introduces new fields to enhance bundle deployment configurations and settings management. These updates allow for more granular control over deployment modes and collaboration platform connectivity, impacting developers using the Go SDK for Databricks. The changes are available in the latest SDK release.
patch - Databricks Java SDK Releases sdkdatadatabricksengineer ·
Databricks SDK Java v0.114.0 Adds Token Management API and Workspace ID Changes
Databricks SDK Java v0.114.0 updates workspace addressing and introduces new token management capabilities. The change to workspace ID headers improves compatibility across different identifier formats. New fields and methods enhance control over job and pipeline deployments, as well as token creation and information retrieval, impacting engineers working with Databricks jobs, pipelines, and authentication.
patch - Databricks Go SDK Releases sdkinfradatabricksengineer ·
Databricks SDK for Go v0.140.0: Enhanced Workspace Addressing and API Features
Databricks SDK for Go version 0.140.0 introduces an improved workspace addressing mechanism using `X-Databricks-Workspace-Id` for API calls, enhancing the unification initiative. It also adds several new API fields and methods, including `UpdateTokenManagement` and fields for job and pipeline deployments, benefiting developers working with Databricks workspaces and configurations.
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 - Terraform Databricks Provider Releases terraforminfragcpdatabricksgaengineer ·
Databricks Terraform Provider v1.116.0 Adds Agent Bricks Permissions, Principal IDs
Databricks Terraform Provider v1.116.0 introduces management of Git credentials for service principals and permissions for Agent Bricks resources. This release addresses several bugs, including issues with metastore updates, UC object destruction, library handling, and vector search index timeouts. These updates are relevant for engineers and architects managing Databricks infrastructure via Terraform.
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 Go SDK Releases sdkinfradatabricksengineer ·
Databricks SDK Go v0.138.0 addresses config profile loading
Databricks SDK Go v0.138.0 fixes an issue where the configuration file loader would not set the profile name when using the legacy fallback. This change ensures consumers can correctly derive a per-profile identifier, preventing issues where writes under the 'DEFAULT' profile could not be found by subsequent reads. This fix is relevant for users of the Databricks CLI and any applications deriving profile identifiers from the SDK's configuration.
patch - Databricks Python SDK Releases sdkinfradatabricksengineer ·
Databricks SDK for Python: Ruff Integration and Formatting Updates
The Databricks SDK for Python has integrated Ruff for formatting and linting, replacing Black, isort, and autoflake. This change aligns the SDK's formatting with Databricks' internal guidelines and prepares for a unified internal repository. While the underlying code formatting logic is updated, there are no behavioral changes for users of the published SDK.
patch - Databricks Go SDK Releases sdkinfradatabricksengineer ·
Databricks SDK Go v0.137.0: Agent detection, SCIM optimization, API additions
Databricks SDK Go v0.137.0 improves AI agent detection in User-Agent headers and optimizes SCIM requests by excluding entitlements for faster workspace ID retrieval. This update also introduces new API fields for dashboards, apps, materialized features, and synced tables, benefiting developers working with Databricks services. These changes are now available in the latest SDK release.
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
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.