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 blogaidatabricksengineer ·
Databricks Genie One launches native mobile apps for iOS and Android
Databricks has released native mobile applications for Genie One, making its AI data assistant accessible on iOS and Android devices. This allows business users to get grounded, instant insights and perform actions on the go, extending enterprise governance to mobile workflows. The app is now available in public preview and mirrors the web experience, including chat, dashboards, and Databricks Apps.
feature announcement - Databricks Blog blogsecuritydatabricksengineer ·
Databricks Omnigent Uses Contextual Policies to Block Slow-Burn Attacks
Databricks Omnigent has introduced stateful contextual policies designed to combat indirect prompt injection and slow-burn attacks. These policies track session risk across multiple actions, blocking outbound data exfiltration attempts that individual action checks would miss. This enhancement aims to protect against sophisticated attacks where malicious actions are disguised as routine operations, affecting users who employ AI agents for data processing and communication.
feature - Databricks Blog blogaidatabricksengineerfinanceretail ·
Databricks Genie: AI Coworker for Retail Finance Margin Protection
Databricks has launched Genie, an AI-powered coworker designed to help retail finance teams navigate omni-channel complexity and protect profit margins. Genie uses an evolving ontology to provide trustworthy, sourced answers to complex questions about margin, cash flow, and revenue, moving beyond traditional reporting. Available now, it aims to empower finance professionals to make proactive, profitable decisions by understanding real-time business context.
feature 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.
feature - Databricks Java SDK Releases sdkdatadatabricksengineer ·
Databricks SDK Java v0.128.0: CDF Configs and Breaking IAM Changes
Databricks SDK Java version 0.128.0 introduces new methods for managing Change Data Feed (CDF) configurations via the workspace client. It also adds a `parent` field to connection objects and a `MINUTES` enum for job triggers. Crucially, this release includes breaking changes to the `internalId` field type for IAM entities (Groups, Service Principals, Users), requiring Java SDK users to update their code to adapt to the String type.
breaking patch - Databricks Go SDK Releases sdkinfradatabricksengineer ·
Databricks SDK Go v0.157.0: New CDF methods, Parent field, and breaking ID changes
The Databricks SDK for Go version 0.157.0 introduces new methods for managing Change Data Feed (CDF) configurations for Postgres and adds a Parent field to catalog connection requests. It also includes breaking changes, modifying the `InternalId` field for IAM groups, service principals, and users to be a string type. These updates are relevant for developers using the Go SDK to interact with Databricks services, particularly those managing Postgres CDF or IAM entities.
breaking patch - Databricks Blog blogaigovernancedatabricksengineer ·
Omnigent Adds Contextual Policies for Enhanced AI Agent Governance
Omnigent, an open-source meta-harness for AI agents, now features contextual policies that leverage session state to enhance governance. These policies allow for more nuanced control over agent actions, improving security and cost management by considering the agent's history within a session. This capability is available for various coding and custom agents wrapped by Omnigent, providing richer policy options than traditional static controls.
feature - Databricks Blog blogdatabricksgaengineerhealthcare ·
Imperial College London Accelerates Dementia Research with Databricks Platform
Imperial College London modernized its dementia research platform by integrating IoT, clinical, and research data using Databricks. This new architecture separates workloads, enhances data access via Unity Catalog, and empowers non-technical users to explore patient insights. The platform significantly reduced data integration timelines from six months to one month, accelerating model development and improving dementia care.
announcement feature - Databricks Blog blogaidatabricksengineer ·
Databricks Genie Hackathon Highlights Agentic AI Capabilities
Databricks hosted its fifth hackathon showcasing Databricks Genie, a family of AI tools for data interaction. The event focused on three tracks: Genie Agents for conversational analytics, Genie Code for autonomous AI assistance in data workflows, and composing Genie into broader agentic systems. These projects demonstrate how governed, conversational analytics can become a foundational element for various teams, from business users to engineers.
announcement feature - Databricks Blog blogaisecuritydatabricksengineer ·
Barracuda Managed XDR adds AI-powered natural language log search
Barracuda Managed XDR has integrated Databricks Genie to enable security analysts to query logs using natural language instead of SQL, accelerating threat investigations. This feature is built with row-level security in Unity Catalog to ensure tenant isolation across thousands of customers. The enhancement significantly reduces the time needed for routine investigations and frees up SOC analyst hours.
feature - 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 Go SDK Releases sdkdatadatabricksengineer ·
Databricks SDK for Go v0.156.0 Adds New Fields
The Databricks SDK for Go version 0.156.0 introduces several new fields across various services, including disaster recovery, jobs, and machine learning experiments. These additions provide more granular information for disaster recovery URLs, run details, and experiment tracking. Developers using the Go SDK for Databricks will benefit from these enhanced capabilities for managing their workloads.
patch - Databricks Java SDK Releases sdkdatadatabricksengineer ·
Databricks SDK Java v0.127.0 adds fields to disaster recovery, jobs, and ML services
Databricks SDK for Java version 0.127.0 introduces new fields across several service APIs, including disaster recovery, jobs, and machine learning capabilities. These additions provide more detailed information for tracking and managing deployments and experiments. This update is relevant for Java developers using the Databricks SDK to interact with these services.
patch - 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
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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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.