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 Blog blogaidatabricksengineer ·
Databricks Benchmarks Coding Agents on its Codebase
Databricks developed an internal benchmark to evaluate coding agents' performance and cost-efficiency on real-world tasks within its multi-million line codebase. The analysis reveals that a mix of models and harnesses is needed for optimal performance, and token price is a poor indicator of overall task cost. This benchmark aims to guide engineers in selecting the most efficient tools for various coding complexities, improving overall engineering productivity.
announcement feature - Databricks Blog blogaidatabricksengineerhealthcare ·
Health Catalyst Ambulatory Intelligence Deploys on Customer Databricks
Health Catalyst has launched Ambulatory Intelligence, a solution designed to address operational barriers limiting growth in healthcare ambulatory care. This new offering combines AI with healthcare expertise to provide insights into patient access, referrals, and financial performance, helping health systems identify and act on constraints. By deploying directly within a customer's Databricks environment and leveraging Unity Catalog and Lakebase, it ensures data governance and low-latency performance, addressing concerns about data control and privacy.
feature announcement - Databricks Blog blogmldatabricksengineer ·
Databricks Introduces Feature Views for Managed ML Feature Pipelines
Databricks has released Feature Views, a new managed framework designed to simplify the creation, serving, and governance of ML features across the entire lifecycle. This feature aims to eliminate training-serving skew and reduce operational overhead by allowing developers to define a feature once and use it for both experimentation and production inference, including real-time applications. The public preview is now available, with streaming capabilities requiring an Enterprise-tier workspace.
feature announcement - Databricks Blog blogaidatabricksengineer ·
Acxiom Modernizes Marketing Stack with Databricks for Agentic AI
Acxiom has migrated its data foundation from on-premises Hadoop to Databricks, enabling significant performance gains and accelerating the development of agentic AI workflows for marketing. This modernization shifts the company's competitive position from a data supplier to an intelligence layer, allowing for automated marketing processes from audience planning to campaign activation. The move is critical for organizations seeking to leverage AI without being constrained by legacy infrastructure, with Acxiom seeing run times improve by 80-90%.
feature announcement - Databricks Java SDK Releases sdkdatabricksengineer ·
Databricks SDK Java v0.126.0: API changes and breaking update
Databricks SDK Java version 0.126.0 introduces new API fields and a breaking change requiring the `role` field for `DatabaseDatabaseSpec`. This update is relevant for Java developers using the Databricks SDK, particularly those interacting with bundle deployments or PostgreSQL database specifications.
breaking patch - Databricks Go SDK Releases sdkdatadatabricksengineer ·
Databricks SDK Go v0.155.0: API updates and breaking change
Databricks SDK Go version 0.155.0 introduces an update to the bundle deployments resource with a new `UpdateTime` field. A significant breaking change requires the `Role` field to be mandatory for Postgres database specifications. These updates primarily affect Go developers using the Databricks SDK for managing Databricks resources.
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 Java SDK Releases sdkdatadatabricksengineer ·
Databricks SDK for Java v0.124.0: API updates and breaking changes
Databricks SDK for Java version 0.124.0 introduces several API additions and modifications, including new fields for ML service configurations and updated workspace client methods. However, it also removes several fields across different services, such as `firstDistinctN`, `lastDistinctN`, `includeBrowse`, `browseOnly`, `externalSecretId`, and `groupName`, which may require consumers to update their existing code. This release focuses on enhancing existing functionalities while also incorporating breaking changes that necessitate careful review by developers using the Java SDK.
breaking 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 - 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
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.