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
- Databricks Blog blogaidatabricksgaengineer ·
AI Literacy Framework for Education and Workforce
Databricks introduces a comprehensive AI literacy framework designed to equip individuals with functional, critical, and ethical skills for responsible AI use. The framework, organized around understanding, evaluating, and using AI, aims to guide higher education institutions and organizations in curriculum development and workforce training. This initiative addresses the growing demand for AI proficiency as AI tools become integral to daily work, projecting significant shifts in workplace skills.
announcement - Databricks Blog blogaidatabricksengineer ·
Databricks Genie One: AI Use Cases for Business Users
Databricks introduces Genie One, an AI-powered agent designed to automate recurring tasks for business users across various systems. The tool aims to transform how users work by integrating with existing applications like calendars, CRMs, and document repositories to produce concrete outputs and actions. Key use cases include automated business reviews, meeting preparation and follow-up, knowledge work automation, and operational monitoring, enabling teams to save time and improve efficiency.
feature announcement - Databricks Blog bloggovernancecompliancedatabricksengineer ·
EU Digital Product Passport: Databricks Solution for Traceability Deadline
Manufacturers selling into the EU must comply with the Digital Product Passport regulation by February 2027, a deadline that mandates product traceability and sustainability data. This regulation poses significant data challenges, particularly concerning data availability, quality, and integration across complex supply chains. Databricks offers a Solution Accelerator on its platform to address these issues, providing a unified data foundation for compliance, resilience, and sustainability reporting.
announcement - Databricks Java SDK Releases sdkdatadatabricksengineer ·
Databricks SDK Java v0.135.0 Adds Catalog Enums, Includes Breaking Changes
Databricks SDK Java v0.135.0 introduces new enum values for catalog ownership, enhancing schema and table management. However, this release also contains breaking changes, including a modified `createDeployment()` method signature and the removal of the `deploymentId` field from the `CreateDeploymentRequest`. These changes require users of the Java SDK to update their code to accommodate the new API structure.
breaking 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 bloggovernancedatabricksengineerhealthcare ·
FDA Builds Widely Adopted Generative AI Platform on Databricks
The US Food and Drug Administration (FDA) successfully launched a generative AI platform, ELSA, built on Databricks Halo, achieving 85% staff adoption within two months. This initiative consolidated data silos across eight agency centers, enabling faster regulatory research and custom AI agent development for 16,000 staff. The platform utilizes Databricks Unity Catalog for governance and is now expediting critical tasks like drug application reviews, reducing them from days to minutes.
feature announcement - 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 Blog blogaidatabricksengineer ·
Databricks Genie Code Agent Outperforms General Agents in Accuracy and Cost
Databricks evaluated its data agent, Genie Code, against three general coding agents on over 400 real-world tasks. Genie Code proved to be the most accurate and cost-efficient, delivering correct answers at less than half the cost of other agents. This performance stems from its deep semantic understanding of enterprise context and specialized capabilities, allowing it to navigate complex data workspaces more effectively than general-purpose agents. The evaluation included a wide spectrum of tasks such as data discovery, code creation, debugging, and data lookups, highlighting Genie Code's advantages for full-spectrum data work.
announcement feature - Databricks Java SDK Releases sdkdatabricksengineer ·
Databricks SDK for Java v0.134.0: New fields and one breaking change
Databricks SDK for Java v0.134.0 introduces several new fields across compute, jobs, ML, and Postgres integration, enhancing cluster configuration and schema management. However, it also includes a breaking change with the removal of the 'lifetime' field in the TimeWindow model for ML services. These updates primarily affect Java developers working with the Databricks platform, particularly those integrating with compute, ML workflows, or data synchronization features.
breaking patch - 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 Blog blogaidatabricksengineer ·
Databricks simplifies AI agent orchestration with Lakebase Postgres
CliftonLarsonAllen (CLA) collaborated with Databricks to build a fully Databricks-native solution for agentic auditing and document processing. The solution leverages Lakebase Postgres as an orchestration backbone, eliminating the need for external infrastructure like message brokers or schedulers. This approach addresses challenges such as unpredictable task latency, rate-limiting, workload prioritization, cost attribution, and real-time visibility for long-running agentic tasks.
feature announcement - Databricks Blog blogdataaidatabricksengineer ·
Databricks: Bridging the Gap Between Data and Marketing Campaigns
This article explains how the 'composable canvas' architecture, powered by Databricks, closes the gap between first-party data and actual marketing campaign execution. By unifying data foundations and enabling AI agents, it eliminates integration bottlenecks and speeds up campaign activation. This is crucial for marketing teams struggling with siloed tools and delayed data activation, allowing them to leverage rich customer signals for personalized campaigns.
announcement feature - Databricks Blog blogdataaidatabricksengineer ·
Databricks Lakehouse for R&D Data and AI Agents
Cellcentric's Data Hub, built on Databricks Unity Catalog and Lakehouse Federation, integrates scattered R&D data into a unified, AI-ready product. By prioritizing context coverage as a quality metric, the platform accelerates R&D investigations and provides agents with the same governed context as human users. This approach ensures secure and traceable data access for both employees and AI clients.
announcement - Databricks Blog blogdatagovernancedatabricksengineer ·
Dow Builds Carbon Footprint Ledger on Databricks for Sustainability
Dow has implemented a Carbon Footprint Ledger (CFL) on the Databricks Data Intelligence Platform to calculate cradle-to-gate Product Carbon Footprints (PCFs) for its entire product portfolio. This initiative significantly reduces processing time from weeks to a fraction of that by leveraging Apache Spark, Delta Lake, Unity Catalog, and MLflow, enabling faster optimization and verifiable certification of low-carbon products. The system is designed for third-party assurance against ISO 14067 and the GHG Protocol Product Standard, benefiting both Dow's sustainability goals and its customers' Scope 3 emission reduction efforts.
announcement feature - Databricks Blog blogdatadatabrickspreviewdatabricks-unity-catalog ·
Databricks Previews Unity Catalog Discover and Domains for Data Marketplace
Databricks has launched a Public Preview of its internal data and AI marketplace, "Discover," and a business context layer called "Domains," both powered by Unity Catalog. These features aim to help users and AI agents find trusted, relevant data and AI assets more easily by organizing them around business structures. This is particularly beneficial as data estates grow and AI agents require contextual understanding for reliable results.
feature announcement - 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 - Databricks Java SDK Releases sdkmldatabricks ·
Databricks SDK for Java v0.132.0: API changes
Databricks SDK for Java version 0.132.0 introduces several API modifications, including a new field for pipeline connector options and changes to window duration requirements for ML components. These updates affect developers using the Java SDK for Databricks integrations, particularly those working with ML models or data pipelines.
breaking patch - Databricks Blog blogdatadatabricksengineerarchitect ·
Branching Lakebase databases like code for CI/CD
Glaspoort implemented a CI/CD pattern for their Databricks Lakebase, treating database changes with the same rigor as application code. This involves branching every environment directly from production and using ephemeral per-PR databases, with migrations as the single source of truth. This approach aims to avoid the common "reset-from-parent trap" that causes environments to drift from production and necessitates costly rebuilds. The pattern allows for faster, more reliable database updates in production environments.
feature announcement - Databricks Blog blogaidatabricksengineer ·
Databricks combines vector search and AI Classify for efficient large-scale document classification
Databricks has introduced a new method for classifying documents against taxonomies with over 100,000 labels, addressing limitations of existing approaches like regex and direct LLM calls which struggle with cost, maintenance, and context windows. The solution pairs vector search with the Databricks AI Classify function, retrieving a shortlist of candidate labels before AI Classify makes the final selection. This hybrid approach has demonstrated higher accuracy at a significantly lower cost compared to using frontier models alone across multiple benchmarks, benefiting thousands of Databricks customers dealing with use cases like biomedical entity linking and vendor normalization.
feature announcement
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.