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 blogdataaigovernancearchitecthealthcare ·
Building an AI-Forward Healthcare Organization on Data and Governance
This article outlines the key blockers and enablers for healthcare organizations aiming to become AI-forward. It argues that building a solid foundation of unified data, robust governance, and a clear operating model is crucial for successfully building, trusting, and scaling AI initiatives. The piece highlights that modern platforms can now enable governed use cases to go live in weeks, not months, allowing healthcare providers to overcome common challenges and adopt AI more effectively.
announcement - Databricks Blog blogdataanalyticsdatabricksengineermedia ·
NBCUniversal Migrates to Databricks Lakehouse for Scalable Analytics
NBCUniversal successfully migrated its data infrastructure to the Databricks Lakehouse Platform, achieving a 30% cost reduction by switching to dedicated job compute. This move enables independent scaling of data pipelines, faster job completion, and meets Service Level Agreements. The migration impacts data analysts, engineers, and scientists by providing a unified platform for advanced ML model development and real-time analytics, with a phased, partner-led implementation by EXL minimizing disruption.
feature patch announcement - Databricks Python SDK Releases sdkdatadatabricksengineer ·
Databricks SDK for Python v0.123.0: API changes and breaking updates
Databricks SDK for Python version 0.123.0 introduces several new workspace-level services and API fields, including AI Gateway and expanded telemetry configuration. Notably, breaking changes affect the bundle deployments service with modified method signatures and removed fields, impacting how developers manage deployments. This release primarily affects users of the Databricks SDK for Python, particularly those working with bundle deployments or integrating AI Gateway.
breaking patch - Databricks Blog blogdatadatabricksengineer ·
Databricks Genie Code Beta: Agentic Converter for SQL Dialect Migration
Databricks has launched the Agentic Converter in Genie Code as a beta feature, utilizing AI agents to automate the conversion of proprietary SQL dialects (T-SQL, Snowflake, Redshift, Oracle, BigQuery, Teradata) to open ANSI SQL. This aims to simplify and accelerate legacy data warehouse migrations to the Databricks Lakehouse by handling code analysis, iterative conversion, and syntax validation. The tool introduces migration projects for tracking progress and lineage, alongside custom skill creation for tailored conversions, making data warehouse migration a configurable and monitorable process.
feature announcement - Databricks Blog blogdatadatabricksengineer ·
Databricks Agents for Real-Time Production Line Decisions
Databricks introduces AI agents for production lines, enabling immediate, data-driven decisions to optimize operations. These agents ingest real-time streaming data from OT, MES, ERP, and LIMS into the Databricks Data Intelligence Platform. This integration allows for faster root cause analysis and recommendations, improving Overall Equipment Effectiveness (OEE) and reducing costly downtime for CPG companies and other manufacturers. The system provides in-shift answers rather than next-day reports, with recommendations drafted as work orders for human approval.
feature announcement - Databricks Blog blogdatadatabricksengineer ·
AI in Supply Chain: Demand Forecasting to AI Agents
This article details how AI is transforming supply chain management, focusing on enhanced demand forecasting, inventory optimization, and the implementation of AI agents for automated decision-making. It highlights the benefits, such as reduced costs and improved accuracy, and outlines the data foundation and organizational considerations for adoption. The guide is targeted at supply chain leaders, planners, and data teams.
announcement - Databricks Go SDK Releases sdkdatadatabricksengineer ·
Databricks SDK for Go v0.165.0 Adds New API Methods
Databricks SDK for Go version 0.165.0 introduces several new API methods and fields to enhance functionality for workspace telemetry, data pipelines, and SQL alerts. These updates provide developers with more granular control and expanded capabilities within the Databricks platform. The changes are available immediately for users of the Go SDK.
patch - 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 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 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 blogdataaidatabricksengineer ·
Databricks Coach's Corner: Soccer App Leverages Full Platform
Databricks has launched Coach's Corner, a soccer coaching application that transforms 51 million rows of match data into a real-time 2D/3D tactical analysis tool. This end-to-end solution showcases the Databricks platform's capabilities from data ingestion via Lakeflow to AI scouting with Genie and Vector Search, all governed by Unity Catalog. The app is designed for coaches to make split-second decisions by providing low-latency data access for replays and optimized query paths for complex analytics, demonstrating a unified approach to data and AI on the platform.
feature announcement - Databricks Blog blogdataaidatabricksengineer ·
Dotmatics Luma and Databricks Partner for AI-Ready Science
Dotmatics Luma, a scientific intelligence platform, is integrating with Databricks to create a unified data stack for R&D. This partnership aims to harmonize fragmented scientific data, enabling AI applications by providing a continuous, structured, and FAIR-compliant data foundation. The integration targets scientists and data engineers in R&D environments, facilitating faster insights and trustworthy AI outputs.
announcement - Databricks Blog blogdatadatabricksengineer ·
Guide to Python App Hosting for Data and AI Workloads
This guide explains Python app hosting, detailing various environments from shared servers to PaaS and serverless functions. It emphasizes that for data-intensive and AI applications, the hosting decision is intrinsically linked to data architecture, impacting data accessibility, latency, and governance. The choice depends on workload requirements and the desired level of infrastructure management, distinguishing Python hosting from regular web hosting.
announcement - Databricks Blog blogdataaidatabricksengineer ·
Databricks Lakebase Accelerators for Cross-Industry and Functional Solutions
Databricks has released a suite of foundational and function-specific accelerators for Lakebase, a serverless Postgres database designed for the agentic era. These solutions leverage Lakebase's capabilities to bridge operational and analytical workloads, enabling faster data modernization, MLOps, and AI agent transformations. Developed with consulting and SI partners, these accelerators are now available to help organizations realize immediate business value across various industries and functions.
feature announcement - Databricks Go SDK Releases sdkdatadatabricksengineer ·
Databricks SDK for Go v0.158.0 adds cleanrooms and pipelines fields
Databricks SDK for Go version 0.158.0 introduces several new fields across its cleanrooms and pipelines services. These additions enhance configuration options for shared outputs in cleanrooms and custom report options for marketing data pipelines. The updates affect users working with these specific Databricks features via the Go SDK.
patch - Databricks Java SDK Releases sdkdatadatabricksengineer ·
Databricks SDK for Java v0.129.0 Adds Clean Rooms & Ads Fields
The Databricks SDK for Java has been updated to version 0.129.0, introducing several new fields across its clean rooms and advertising integration modules. These enhancements allow for more granular control and configuration within Databricks Clean Rooms and provide expanded options for Google Ads, Meta Marketing, and TikTok Ads integrations. This update is relevant for Java developers working with Databricks for data collaboration and marketing analytics.
patch - Databricks Blog blogdatadatabricksengineerdatabricks-unity-catalog ·
Unity Catalog Managed Tables: Interoperability and Governance for Lakehouse
External engines like Spark, Flink, and DuckDB can now create, read, and write to Unity Catalog managed Delta tables with centralized governance. This integration leverages Predictive Optimization for improved query performance and reduced storage costs, while maintaining full interoperability. This feature is now in Public Preview, enabling enterprises to use their preferred engines on a single copy of data with enforced access policies.
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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.