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 blogaidatabricksmedia ·
Databricks Accelerates Agentic Media Buying with New Reference Implementation
Databricks has released a reference implementation and self-deploy accelerator for agentic media buying, enabling autonomous buyer and seller agents to transact business on its platform. This solution addresses the critical need for governed data, transactional state, identity, hosted models, and end-to-end observability, which are often bottlenecks in agent-based projects. The accelerator aims to provide a working blueprint for teams to deploy in their own workspaces, streamlining the media buying process which traditionally involves significant manual coordination. Buyer and seller organizations can adapt these agents to their environments and transact with partners over open standards.
feature 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 blogaidatabrickspreviewengineer ·
Databricks Agentic Code Converter transitions proprietary SQL to ANSI SQL in Beta
Databricks Genie Code now includes an agentic code converter, available in Beta, designed to automate the migration of proprietary SQL dialects to open ANSI SQL. This tool uses parallel agents for iterative conversion and validation, aiming to simplify legacy data warehouse migrations for Databricks users. The converter supports T-SQL, Snowflake, Redshift, Oracle, BigQuery, and Teradata, with features like migration project management, code complexity analysis, and lineage tracking to guide the process.
feature announcement - Databricks Blog blogaidatabricksengineerfinance ·
Databricks Genie AI Coworker for Telecom Finance
Databricks introduces Genie, an AI coworker designed for telecom finance teams to combat revenue leakage by providing accurate, context-aware answers to complex financial questions. By leveraging an ontology that learns the business, Genie helps identify and prevent unbilled services, fraud, partner settlement errors, and customer churn in real-time. This allows finance professionals to move beyond manual reconciliation and act proactively to retain revenue, with early adopters like Lumen Technologies seeing significant time savings. Genie is now available for finance teams to improve revenue assurance and operational efficiency.
feature announcement - Databricks Blog blogaidatabricksengineerhealthcare ·
Databricks Genie: AI Coworker for Healthcare Finance
Databricks has introduced Genie, an AI-powered coworker designed to address the challenges faced by healthcare finance teams operating on fragmented and outdated data. Genie leverages an ontology to provide contextually correct financial insights, enabling faster and more accurate decision-making. This feature aims to help finance professionals understand cost vs. reimbursement, identify revenue leakage from denials, and track trapped cash in receivables, ultimately protecting the organization's margin.
feature announcement - Databricks Blog blogaidatabricksengineerfinance ·
Databricks Genie: AI Coworker for Manufacturing Finance
Databricks has introduced Genie, an AI coworker designed to help manufacturing finance teams manage capital and protect margins. Genie addresses complexities introduced by AI agents by providing context and control through an evolving business ontology. It answers key questions about trapped cash in inventory, aging receivables, and underperforming assets, offering traceable and actionable insights for finance professionals.
feature announcement - Databricks Blog blogaidatabricksengineerfinanceenergy ·
Databricks Genie: AI Coworker for Energy Finance
Databricks has launched Genie, a new AI coworker designed to help energy finance professionals navigate market volatility and protect margins. Genie utilizes an ontology to provide context-aware, traceable answers to critical financial questions, distinguishing it from standard BI tools. It aims to help finance teams manage revenue risk and capital allocation for AI-driven power demand, offering a governed and continuously learning system for critical decision-making.
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 bloggovernancedatabricksengineerfinancegovernment ·
Databricks enables real-time fraud prevention for government benefits
Databricks is enhancing fraud prevention for government benefit programs by leveraging its data and AI platform. This approach aims to shift from a reactive 'pay and chase' model to real-time detection, significantly reducing billions lost annually to fraud. The platform facilitates cross-agency data sharing and sophisticated AI-driven risk scoring for entities like federal departments that already utilize Databricks.
feature announcement - Databricks Blog blogaidatabricksengineerfinancemedia ·
Databricks Genie: AI Coworker for Media Finance
Databricks has introduced Genie, a data-smart AI coworker designed to help media finance teams manage audience value and protect margins. Genie leverages an ontology to provide accurate, context-aware answers to complex financial questions, improving decision-making in areas like subscription pricing and advertising yield. This tool aims to bridge the gap between raw data and actionable insights for finance leaders, marketing, and operations teams within media companies.
feature announcement - Databricks Blog blogaidatabricksgaengineerretail ·
Databricks AI Search adds high-QPS scaling
Databricks AI Search now supports scaling endpoints to thousands of queries per second (QPS) with a single configuration parameter, eliminating the need for manual replica management or load balancing. This feature is crucial for real-time applications like search bars, recommendation systems, and entity resolution that experience high traffic volumes. It's generally available today for all users, simplifying the transition from prototype to production.
feature announcement - Databricks Blog blogaidatabricksengineer ·
Databricks uses Unity AI Gateway for internal AI coding agent spend control
Databricks has implemented internal controls for its AI coding agent spend by routing all traffic through Unity AI Gateway, enforcing unified budgets and policies. This system separates daily limits for runaway spend protection from higher monthly limits for extraordinary use, balancing innovation with cost control. It allows engineers to self-service budget increases for normal usage, reducing bottlenecks and enabling faster adoption of AI tools.
announcement feature - 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 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 blogaigovernancedatabricksengineer ·
Databricks Unity AI Gateway adds spend controls and budget alerts
Databricks' Unity AI Gateway now offers AI spend controls, allowing users to set budgets and hard caps at various granularities including user, workspace, and organization levels. This feature addresses the challenge of managing unpredictable AI workload costs by providing proactive budget alerts and automated spending limits. The controls are integrated with Unity Catalog and Databricks budgets, offering unified governance for AI usage, cost visibility, and operational accountability across all models and providers, and are available starting today.
feature - 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 blogaisecuritydatabricksengineer ·
Databricks Omnigent adds intent-based authorization for AI agents
Databricks Omnigent has introduced intent-based authorization to enhance the security of AI agents. This feature ensures agents only perform actions aligned with their declared purpose, closing a gap in traditional identity-based authorization systems that attackers can exploit through prompt injection. The system requires human approval for sensitive actions and denies unauthorized ones, with intents defined at design time or approved by a human at session start, impacting developers and architects managing AI agents.
feature security
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.