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 Go SDK Releases sdkinfradatabricksengineer ·
Databricks SDK Go v0.135.0 Adds Bundle Package and TLS Config
The Databricks SDK for Go version 0.135.0 introduces a new bundle package and a workspace-level bundle API, enhancing deployment capabilities. It also adds an MtlsConfig field to the ML AuthConfig, improving secure authentication for machine learning workloads. These updates are primarily relevant to Go developers utilizing the Databricks SDK for infrastructure and ML operations.
patch - Databricks Java SDK Releases sdkinfradatabricksengineer ·
Databricks SDK for Java v0.111.0
Databricks SDK for Java has released version 0.111.0, introducing a new package for bundle operations and a corresponding client service. Additionally, it includes an update to the ML AuthConfig with a new mtlsConfig field. These changes are primarily relevant for developers working with Databricks bundles and ML authentication within their Java applications.
patch - Databricks Go SDK Releases sdkinfradatabricksengineer ·
Databricks SDK Go v0.134.0 adds new job and settings fields
The Databricks SDK for Go has been updated to version 0.134.0, introducing new fields for managing pipeline refresh and operational email settings. These additions provide more granular control over job refresh behaviors and custom email recipient configurations within Databricks. The update is primarily relevant for Go developers interacting with Databricks jobs and settings via the SDK.
patch - Databricks Java SDK Releases sdkdatabricksengineer ·
Databricks SDK Java v0.110.0 adds pipeline and settings fields
Databricks SDK Java version 0.110.0 introduces new fields for pipeline parameters and tasks, enabling more granular control over full refreshes, flow refreshes, and checkpoint selections. It also adds custom recipient options for operational emails within settings. These changes primarily affect developers integrating with Databricks Jobs and Settings APIs using the Java SDK.
patch - Databricks Python SDK Releases sdkdatadatabricksengineer ·
Databricks SDK for Python adds job pipeline refresh fields
Databricks SDK for Python version 0.110.0 introduces new fields for managing pipeline refresh behavior within job configurations. This enhancement provides more granular control over how data pipelines are refreshed, affecting users who develop and manage automated data workflows on Databricks. The update is available now as part of the SDK release.
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 Java SDK Releases sdkdatabricksengineer ·
Databricks SDK Java v0.109.0 adds new methods and fields
Databricks SDK Java v0.109.0 introduces new methods for managing workspace assignments and adds several new fields across various services. These updates enhance programmatic control over account and workspace configurations, including disaster recovery, job submissions, and network policies. The release affects developers using the Java SDK to interact with Databricks services.
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 - Terraform Databricks Provider Releases terraforminfradatabricksdeprecationengineer ·
Databricks Terraform Provider v1.115.0: Bug Fixes and Deprecations
This release of the Databricks Terraform provider addresses several critical bugs affecting various resources when used with account-level configurations. Specifically, issues with decoding state, resolving workspace IDs, and handling OAuth configurations are resolved, ensuring greater stability for users managing Databricks resources via Terraform. Additionally, the provider deprecates the `provider_config` field for account-level settings, signaling a move towards more streamlined configuration management. These fixes benefit users managing complex Databricks environments, particularly those leveraging account-level configurations or migrating to newer provider versions.
patch - Databricks Java SDK Releases sdkdatabricksengineer ·
Databricks SDK Java v0.107.0: New APIs, Breaking Changes, and CLI Fixes
Databricks SDK Java v0.107.0 introduces new APIs for workspace management, vector search, and job configurations, alongside several breaking changes in field requirements and removals. It also fixes a critical bug in the Databricks CLI profile fallback mechanism and enhances token refresh behavior. These updates primarily affect developers using the Java SDK for Databricks automation and integration.
breaking patch - Databricks Python SDK Releases sdkinfradatabricksengineer ·
Databricks SDK for Python v0.107.0 Enhances APIs and Fixes Auth
Databricks SDK for Python v0.107.0 introduces new API methods for supervisor agents and vector search endpoints, along with several new fields across various services like pipelines and settings. The release also includes a fix for the CLI's auth token command to ensure fresh tokens are used, addressing potential stale token issues. These changes are primarily for engineers and architects working with the Databricks platform.
breaking patch - Databricks Go SDK Releases sdkinfradatabricksengineer ·
Databricks SDK Go v0.132.0: New Features and Breaking Changes
Databricks SDK Go version 0.132.0 introduces several new fields and enum values across various services, including pipelines, catalog, and vector search, enhancing connector options and metadata capabilities. Notably, it includes breaking changes by removing MinQps fields from vector search endpoint creation and patching, and modifying Description fields in supervisor agents and tools to be non-required. These updates primarily affect developers using the Go SDK to interact with Databricks services, requiring attention for any code relying on the removed or changed fields.
breaking patch - Terraform Databricks Provider Releases terraformdatabricksengineerdatabricks-unity-catalog ·
Databricks Terraform Provider Adds Resource Adoption and Drift Prevention
Version 1.114.2 of the Databricks Terraform provider enables adopting pre-existing PostgreSQL branch and endpoint resources with a new argument. This update also prevents Terraform drift for external locations by suppressing differences in server-populated fields. These changes benefit users managing Databricks infrastructure with Terraform, particularly those integrating with PostgreSQL or encountering configuration drift.
patch - Databricks Java SDK Releases sdkinfradatabricks ·
Databricks SDK Java v0.106.0: New Features and Breaking Changes
Databricks SDK Java version 0.106.0 introduces new packages for disaster recovery and temporary volume credentials, alongside several API additions and field updates across various services. Some changes are backward-incompatible, notably the removal of the `connection` field from the `Tool` class and making the `description` field optional for `SupervisorAgent`. These updates primarily affect developers using the Java SDK to interact with Databricks services.
breaking patch - Databricks Python SDK Releases sdkinfradatabricksengineer ·
Databricks SDK for Python v0.106.0: New Features and Breaking Changes
Databricks SDK for Python version 0.106.0 introduces new workspace and account-level services, including temporary volume credentials, enhanced knowledge assistant capabilities, and disaster recovery features. It also adds numerous fields to existing services for data pipelines and Postgres management, alongside a breaking change in how connections and tools are handled in the supervisor agents service. This release affects developers using the Databricks SDK, particularly those interacting with workspace services or relying on the supervisor agents' connection fields.
breaking patch - Terraform Databricks Provider Releases terraforminfradatabricksengineer ·
Databricks Terraform Provider v1.114.1 Rolls Back to v1.113.0
Databricks Terraform Provider version 1.114.1 has been reverted to version 1.113.0 due to issues discovered in the latest release. This rollback ensures stability for users managing Databricks resources via Terraform. Users who have already upgraded to 1.114.1 should consider downgrading to 1.113.0.
patch - Terraform Databricks Provider Releases terraforminfradatabricksengineer ·
Databricks Terraform Provider v1.114.0 Adds Disaster Recovery and Supervisor Agent Resources
Version 1.114.0 of the Databricks Terraform provider introduces new resources for disaster recovery failover groups, stable URLs, and supervisor agents. It also enhances support for adopting pre-existing PostgreSQL branch and endpoint resources. These updates benefit Databricks users managing their infrastructure via Terraform, particularly those focused on resilience and agent-based automation.
patch - Databricks Go SDK Releases sdkinfradatabricksengineer ·
Databricks SDK Go v0.131.0: New APIs and Breaking Changes
Databricks SDK Go version 0.131.0 introduces several new API methods and fields across workspace, account, and compute services, including disaster recovery and knowledge assistants. Notably, there are breaking changes to the supervisor agents connection field. These updates primarily affect developers and architects working with the Databricks API via Go, requiring adjustments for specific supervisor agent interactions.
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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.