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 180 Databricks releases · Updated
- Terraform Databricks Provider Releases terraforminfradatabricksengineerdatabricks-unity-catalog ·
Databricks Terraform Provider v1.123.0: Improved Plan Validation, Delta Sharing, MLflow, and Bug Fixes
Databricks Terraform provider version 1.123.0 shifts workspace ID validation from plan to apply, resolving false-positive plan failures for certain configurations. It also introduces a data source for Delta Sharing recipients and enhances MLflow experiment tracing with Unity Catalog integration. Several bug fixes address issues with view column comments and access control rule set drift detection, impacting users managing Databricks resources via Terraform.
patch - Databricks Go SDK Releases sdkinfradatabricksengineer ·
Databricks SDK for Go v0.166.0 Enhances Workspace Services
Databricks has released version 0.166.0 of its Go SDK, introducing new workspace-level services and enhancing existing ones. Key additions include an AiGateway service and an UpdateOperation method for BundleDeployments, along with expanded fields for deployment and operation details. These changes provide developers with more granular control and visibility over workspace resources, particularly for deployment management.
patch - Databricks Go SDK Releases sdkinfradatabricksengineer ·
Databricks SDK Go v0.164.0: API changes and breaking updates
Databricks SDK Go v0.164.0 introduces new enum values for catalog ownership and includes two breaking changes. Developers using the SDK must update their code to accommodate the reordered arguments in the `CreateDeployment` method and the removal of the `DeploymentId` field in the request. These changes primarily affect engineers and architects working with Databricks bundles and workspace deployments.
breaking patch - Databricks Blog bloginfradatabricksengineer ·
Databricks Builds Self-Serve Infrastructure Vending Machine for Field Engineering
Databricks has developed the Field Engineering Vending Machine (FEVM), a Databricks App that provisions isolated, governed, and use-case-specific cloud resources on demand. This addresses infrastructure challenges faced by their growing field engineering organization of over 7,000 people, enabling faster development and agent-first framework adoption. The system uses Terraform for provisioning across AWS, Azure, and GCP, with transparency and audibility built-in.
feature announcement - Databricks Go SDK Releases sdkinfradatabricksengineer ·
Databricks SDK Go v0.163.0: New fields and breaking change
Databricks SDK Go v0.163.0 introduces several new fields across compute, jobs, ML, and PostgreSQL services, enhancing cluster configuration and ML tasks. Notably, the SDK removes the `Lifetime` field from `ml.TimeWindow`, which is a backwards-incompatible change requiring consumer action. These updates are primarily for Go developers working with the Databricks platform.
breaking patch - Databricks Java SDK Releases sdkinfradatabricks ·
Databricks SDK Java v0.133.0 Adds Fields to Workspace, Job, and Serving APIs
Databricks SDK Java version 0.133.0 introduces new fields across several service APIs, including `effectiveEntitlements` for workspace assignments, `serverlessComputeId` for job clusters, and `tableNames` along with `telemetryProfileId` for serving telemetry configuration. These updates enhance the SDK's ability to manage and configure Databricks resources. This is a patch release, affecting developers using the Java SDK for Databricks integration.
patch - Databricks Go SDK Releases sdkinfradatabricksengineer ·
Databricks SDK Go v0.161.0: API changes and breaking updates
Databricks SDK Go version 0.161.0 introduces several new fields across various services, including jobs, machine learning, networking, and serving, enhancing functionality for users. However, it also includes breaking changes to IAMv2 methods and modifications to window duration fields in ML services, requiring developers to update their code. These updates affect users interacting with Databricks account and workspace IAM features, as well as those utilizing ML time window functionalities.
breaking patch - Databricks Python SDK Releases sdkinfradatabricksengineer ·
Databricks SDK for Python v0.122.0 Introduces New APIs and Breaking Changes
This release of the Databricks SDK for Python (v0.122.0) adds new methods for managing clean rooms and PostgreSQL configurations, along with numerous field additions across various services. Notably, it includes several breaking changes, particularly affecting IAM v2 and ML TimeWindow configurations, which may require adjustments in existing integrations. These updates are primarily relevant to developers and architects working with the Databricks platform and its associated APIs.
breaking patch - Terraform Databricks Provider Releases terraforminfradatabricksengineer ·
Databricks Terraform Provider v1.122.0 adds Postgres CDF, fixes view column comments
Version 1.122.0 of the Databricks Terraform provider introduces new resources for managing PostgreSQL Change Data Feed (CDF) configurations and their statuses. This release also resolves a bug that prevented updates to column comments on views, a common issue for engineers managing SQL objects via Terraform. The fix ensures that column comment changes on views are applied correctly, preventing perpetual diffs for affected users.
patch - Databricks Java SDK Releases sdkinfradatabricksengineer ·
Databricks SDK Java v0.131.0 adds fields, includes breaking API changes
Databricks SDK Java version 0.131.0 introduces new fields across several services, including ML, Jobs, Networking, and Workspace. This release also contains breaking changes affecting IAM v2 client methods and the TimeWindow object in ML, requiring consumers to update their code. These updates are now generally available.
breaking patch - Databricks Python SDK Releases sdkinfradatabricksengineer ·
Databricks SDK for Python v0.121.0: API updates and breaking changes
Databricks SDK for Python version 0.121.0 introduces new fields and methods across various services, including workspace grants, disaster recovery, and machine learning aggregations. Notably, several breaking changes are implemented, such as making the 'role' field required for PostgreSQL database specs and removing fields related to secret browsing and distinct count aggregations. The release also includes improved documentation for authentication types, benefiting developers using the SDK.
breaking patch - Databricks Go SDK Releases sdkinfradatabricksengineer ·
Databricks SDK Go v0.160.0 adds new fields and enums
Databricks SDK Go version 0.160.0 introduces several new fields and enum values across various services, including Clean Rooms, Bundle Deployments, and Compute. These additions enhance the SDK's ability to manage and interact with Databricks workspace resources, offering greater flexibility for developers working with these services. The update primarily affects engineers and architects utilizing the Go SDK for programmatic access to Databricks functionalities.
patch - Databricks Java SDK Releases sdkinfradatabricksengineer ·
Databricks SDK Java v0.130.0 Adds Features, Includes Breaking Change
Databricks SDK Java v0.130.0 introduces several new fields and enum values across various services, enhancing capabilities for bundle deployments, cleanrooms, compute, disaster recovery, ML, and pipelines. A significant breaking change is the removal of the `codeSourcePath` field from `AiRuntimeTask` in the jobs service, requiring users to update their code if they rely on this field. This release is available now and impacts developers using the Java SDK.
breaking patch - Databricks Blog bloginfradatabricksengineer ·
Databricks Introduces Real-Time Mode for Spark Structured Streaming
Databricks has launched Real-Time Mode (RTM) for Apache Spark Structured Streaming, enabling sub-second latency for operational workloads like fraud detection and IoT monitoring. This feature simplifies the tech stack by eliminating the need for separate real-time processing engines, leveraging existing Spark expertise and APIs with a simple trigger configuration change. RTM is available now, offering a unified platform for both analytical and operational data processing to reduce complexity and costs.
feature announcement - Databricks Go SDK Releases sdkinfradatabricksengineer ·
Databricks SDK Go v0.159.0: New fields, one breaking change
The Databricks SDK Go version 0.159.0 introduces new fields for disaster recovery and pipeline configurations, enhancing data resilience and pipeline management. However, it also removes the `CodeSourcePath` field from `AiRuntimeTask` in the jobs service, which is a breaking change requiring user code updates. This release impacts developers using the Go SDK for Databricks integration.
breaking patch - Databricks Go SDK Releases sdkinfradatabricksengineer ·
Databricks SDK Go v0.157.0: New CDF methods, Parent field, and breaking ID changes
The Databricks SDK for Go version 0.157.0 introduces new methods for managing Change Data Feed (CDF) configurations for Postgres and adds a Parent field to catalog connection requests. It also includes breaking changes, modifying the `InternalId` field for IAM groups, service principals, and users to be a string type. These updates are relevant for developers using the Go SDK to interact with Databricks services, particularly those managing Postgres CDF or IAM entities.
breaking patch - Terraform Databricks Provider Releases terraforminfradatabricksgadeprecationengineer ·
Databricks Terraform Provider v1.121.0: Breaking Changes and New AI Features
Databricks Terraform provider version 1.121.0 introduces breaking changes to the `databricks_mws_ncc_private_endpoint_rule` resource, tightening read-only attributes and improving provisioning state visibility. It also adds new resources for AI Search and enhancements to cluster configurations. These changes primarily affect users managing Databricks infrastructure with Terraform, particularly those using the `mws_ncc_private_endpoint_rule` resource or looking to integrate with Databricks AI Search capabilities.
breaking patch - Databricks Go SDK Releases sdkinfradatabricksengineer ·
Databricks SDK for Go v0.154.0 Adds New Fields and Enum Values
Databricks SDK for Go version 0.154.0 introduces several new fields to enhance job and repository management, including ParentPath for job creation and GitCredentialId for repository updates. Additionally, new enum values for workload types in the serving API provide more granular control over model deployment resources. These updates are available now for developers using the Go SDK to interact with Databricks.
patch - Databricks Go SDK Releases sdkinfradatabricksengineer ·
Databricks SDK for Go v0.153.0 Adds Features, Includes Breaking Changes
Databricks SDK for Go version 0.153.0 introduces numerous new fields across various services, enhancing capabilities for catalog management, data classification, job scheduling, and ML operations. This release also includes several breaking changes, primarily involving the removal of fields related to secret browsing and user identification, which may require updates for consumers relying on these specific fields. The updates affect developers interacting with the Databricks API via Go.
breaking patch - Databricks Python SDK Releases sdkinfradatabricksengineer ·
Databricks SDK for Python v0.120.0 adds features, removes user name field
Databricks SDK for Python v0.120.0 introduces numerous new fields across various services, enhancing functionality for workspace Genie, catalog permissions, ML aggregations, and PostgreSQL endpoints. Notably, it adds a `download_message_attachment_visualization()` method for Genie and includes telemetry configuration for serving endpoints. A breaking change removes the `name` field from the `User` object in the IAMv2 service, requiring consumers to adapt.
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.