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 642 Databricks releases · Updated
- Databricks Python SDK Releases sdkmldatabricksgaengineer ·
Databricks SDK for Python v0.143.0 Adds ML Feature Fields, Fixes Logging
The Databricks SDK for Python v0.143.0 has been released, introducing enhancements for Machine Learning features and addressing a critical logging configuration bug. This update adds `job_id` and `pipeline_id` fields to `MaterializedFeature`, providing more detailed metadata for ML operations. It also resolves an issue where importing `databricks.sdk.runtime` or `WorkspaceClient`/`dbutils` would silently override `logging.basicConfig()`, ensuring consistent logging behavior for developers. These changes primarily affect engineers working with Databricks ML features or those with custom logging setups in their Python applications.
patch - Databricks Release Notes infradatabricksgaengineer ·
Horizontal scaling for Databricks Apps is now generally available
Databricks has announced the general availability of horizontal scaling for Databricks Apps. This capability allows users to run a single Databricks application across multiple instances, all accessible via one URL. The feature is designed to significantly improve app availability and concurrency, supporting zero-downtime deployments and offering best-effort session affinity.
feature announcement - Databricks Release Notes aidatabricksgaengineer ·
Genie Code Builds Intelligent Document Processing Pipelines
Databricks Genie Code now supports building end-to-end intelligent document processing pipelines, automating tasks like parsing, classification, extraction, and validation. It's designed to process unstructured documents such as PDFs, contracts, and invoices. The system evaluates extraction quality against labeled data, iteratively improving its accuracy. This enhancement streamlines workflows for developers handling large volumes of varied document types.
feature - Databricks Blog bloganalyticsdatabricksengineer ·
Top 5 Databricks System Table Queries for Cost Management
Databricks highlights five SQL queries utilizing system tables like `system.billing.usage` to help users understand and manage their platform costs. These queries provide insights into daily spend by product, SQL warehouse cost trends, and tag-based cost attribution. They also enable daily cost anomaly detection and future spend forecasting using SQL AI Functions, empowering platform administrators to monitor and optimize their Databricks expenditure effectively.
announcement - Databricks Release Notes aidatabricksdeprecationengineer ·
DeepSeek V4 Pro (0813) Model Scheduled for Retirement
Databricks has announced that the DeepSeek V4 Pro (0813) model is scheduled for retirement on October 30, 2026. This change impacts users currently leveraging this specific model for their AI workloads. DeepSeek V4.1 Flash is recommended as the replacement, with migration guidance available for affected customers. Users should plan their migration efforts well in advance of the retirement date.
deprecation - Databricks Blog blogaidatabricksarchitectenergy ·
S&P Global Energy details conversational AI for structured data with Databricks Genie Agents
S&P Global Energy successfully implemented Databricks Genie Agents and the Model Context Protocol (MCP) to enable natural language access to its complex structured data estate. This architecture allows domain experts to curate conversational AI endpoints directly, significantly shortening time-to-market for data products from months to days. By exposing Genie Agents as managed MCP servers and using a FastMCP proxy, S&P Global achieved governed, cross-domain queries while preserving Unity Catalog governance. This approach addresses the challenges of traditional text-to-SQL pipelines and custom APIs by letting SMEs productize their knowledge.
announcement - Databricks Python SDK Releases sdkaidatabricksgaengineer ·
Databricks Python SDK v0.142.0 Adds AI Gateway Skill Management Methods
Databricks Python SDK v0.142.0 introduces new methods for managing AI Gateway skills directly through the SDK. These additions allow developers to programmatically create, delete, finalize, retrieve, list, and update generative AI model skills. This enhances automation and MLOps workflows for AI applications on the Databricks platform.
patch - Databricks Release Notes aidatabricksgaengineerdatabricks-unity-catalog ·
Databricks Genie Agents enhance programmatic control and tool integration
Databricks has made Agent mode APIs for Genie Agents generally available, allowing programmatic integration into applications like chatbots and internal tools. Additionally, agent authors can now attach scalar and table-valued Unity Catalog functions as tools for Genie Agents. These enhancements provide developers with greater control and expand the functional capabilities of their AI agents.
feature - Databricks Release Notes governancedatabricksgaengineerdatabricks-unity-catalog ·
Databricks Launches General Availability for Workspace Asset Tags
Databricks has announced the general availability of tagging for various workspace assets, including dashboards, notebooks, Databricks apps, and Genie Agents. This enhancement enables users to better organize and categorize their assets within the platform. The new capability supports both governed and ungoverned key-value tags. Users can apply these tags to Unity Catalog securable objects for improved asset management and governance.
feature - Databricks Release Notes databricksarchitect ·
Databricks Genie Usage by Service Principals Now Billed at Standard Rates
Databricks has announced that Genie usage attributed to service principals will now incur standard pay-as-you-go pricing. This change clarifies cost implications for automated workflows and integrations that leverage service principals. Importantly, these principals are explicitly excluded from the free monthly usage allowance typically provided to individual users. Organizations should review their cost controls and budgets for Genie services to account for this update.
announcement - Databricks Blog blogaidatabricksgadata-scientist ·
Running open-Jev in SQL on Databricks
Databricks announced the launch of "System One" decision models, such as Jev, which became available over the weekend. These models can now be run directly in SQL queries on the Databricks platform, streamlining the integration of advanced decisioning logic into data workflows. This capability is significant for engineers and data scientists looking to enhance their analytical and operational systems.
feature - Databricks Release Notes analyticsdatabricksgaengineer ·
Databricks OpenSharing for Metric Views is Now Generally Available
Databricks has made the ability to share metric views using OpenSharing generally available. This feature allows users to add curated metric views to a share and query them directly with the `MEASURE(...)` function on serverless compute. It streamlines the distribution and analysis of critical business metrics, fostering enhanced collaboration and data-driven decision-making. This capability is accessible to all users leveraging serverless compute.
feature - Databricks Release Notes governancedatabrickspreviewengineerdatabricks-unity-catalog ·
Time Travel Queries Enforce ABAC Policies in Databricks (Beta)
Databricks Runtime 19 introduces the ability to use time travel on qualifying Unity Catalog managed tables while enforcing Attribute-Based Access Control (ABAC) policies. This enhancement allows granular control over historical data access, including row filter and column mask policies. The feature is currently available in Beta, affecting users who manage data access and compliance within Unity Catalog. Developers can leverage this for more secure and governed historical data analysis.
feature - Databricks Blog blogaidatabricksgaengineer ·
Databricks Unity Gateway CLI streamlines coding agent deployment and governance
Databricks has introduced the Unity Gateway CLI to enable centralized deployment and management of coding agents at scale. This new tool allows admins to configure approved models, tools, and spending policies from a single interface, ensuring governance and cost-efficiency. Developers can access any approved agent with a single command, automatically applying organizational settings. It addresses the challenge of rapidly evolving AI models by simplifying updates and providing features like Smart Routing and tracing for spend optimization.
feature - Databricks Blog blogaidatabricksarchitect ·
Automating Security Reviews with AI Agents on Databricks
This article details the development of an agent-based security review layer on Databricks, designed to automate routine security tasks and improve intake quality. The solution leverages Databricks' unified platform components, including Unity Catalog, hosted foundation models, Lakeflow Jobs, and Databricks Apps, to create a governed stack for review workflows. By automating predictable cases and reserving human judgment for complex decisions, organizations can significantly reduce manual effort, accelerate review cycles, and maintain consistent security standards. This approach benefits security and development teams by streamlining the review process and ensuring efficient resource allocation.
announcement - Databricks Release Notes aidatabricksgaengineer ·
Databricks Unified Tracing Now Shows Coding Agent Activity
Databricks Unity Gateway now integrates coding agent activity into its unified tracing system, accessible via the Unity Gateway CLI. This enhancement allows users to visualize the tools and skills employed by AI agents alongside their model requests. It provides deeper insights into agent behavior, facilitating better understanding and debugging. Users can explore this data within the Govern > Traces section or by using Genie.
feature - Databricks Release Notes aidatabricksengineer ·
Databricks Unity Gateway unifies tracing for coding agent activity
Databricks Unity Gateway now integrates coding agent activity into its unified tracing capabilities. This enhancement allows users to view the specific tools and skills employed by agents alongside model requests, offering greater transparency into agent operations. The functionality is accessible through the Unity Gateway CLI and can be explored in the Govern > Traces section or via Genie.
feature - Databricks Release Notes aidatabricksgaengineer ·
Connect Coding Agents to Unity Gateway Using the ug CLI
The Unity Gateway CLI (ug) now enables seamless connection of coding agents like Claude Code, Codex, and Pi to Unity Gateway with a single command. This enhancement simplifies agent integration by automating authentication and configuration, managing approved MCP servers and skills, and providing real-time spend visibility. The feature primarily benefits developers and engineers, offering a streamlined workflow for integrating advanced AI coding capabilities within their Databricks environment.
feature - Databricks Release Notes aidatabricksgaengineer ·
Anthropic Claude Opus 5.5 now available on Databricks Unity Gateway
Databricks has made Anthropic Claude Opus 5.5 available on its Unity Gateway platform. This allows customers to access the powerful large language model directly via Foundation Model APIs. The integration provides Databricks users with advanced generative AI capabilities for their data and applications. Customers are reminded of their responsibility to comply with applicable terms.
feature - Databricks Release Notes aidatabricksgaengineer ·
Databricks Enables Central Configuration for Coding Agents
Databricks now allows organizations to centrally configure coding agents, streamlining the management of models, MCP servers, skills, default harnesses, and Smart Routing. This new capability enables unified control over agent settings, which apply at launch or can be rolled out via mobile device management (MDM). It simplifies the deployment and consistent operation of coding agents across an organization, benefiting technical teams responsible for AI/ML infrastructure.
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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.
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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.