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 App Scores Transactions in Milliseconds Using Model Serving and Lakebase
A new Databricks App combines Model Serving with route optimization and Lakebase Postgres to score credit card transactions for fraud in real-time. This integration significantly reduces latency, enabling transaction scoring within tens of milliseconds by optimizing network paths and providing fast online feature lookups. The application is suitable for engineers and architects building low-latency applications, featuring autoscaling Lakebase and efficient connection pooling for stable performance under load.
feature announcement - Databricks Blog blogaidatabricksengineer ·
Databricks launches Context Engineer certification and AI training
Databricks has introduced an industry-first "Context Engineer" certification beta exam to validate skills in building reliable AI agent systems, addressing the critical bottleneck of context management in agentic AI. This launch is complemented by an expanded learning catalog with targeted courses and an official AI certification prep guide that advises on using LLMs like ChatGPT for study. These initiatives aim to close the skills gap for professionals working with agentic AI and offer a new, AI-first approach to technical certification preparation.
feature announcement - Databricks Java SDK Releases sdkinfradatabricksengineer ·
Databricks SDK Java v0.130.0 Adds Features, Includes Breaking Change
Databricks SDK Java v0.130.0 introduces several new fields and enum values across various services, enhancing capabilities for bundle deployments, cleanrooms, compute, disaster recovery, ML, and pipelines. A significant breaking change is the removal of the `codeSourcePath` field from `AiRuntimeTask` in the jobs service, requiring users to update their code if they rely on this field. This release is available now and impacts developers using the Java SDK.
breaking patch - Databricks Blog blogaidatabricksengineer ·
Apache Spark 4.2 Enhances AI Analytics, Data Pipelines, and Usability
Apache Spark 4.2 introduces significant updates, including metric views for governed business definitions, Spark Connect for remote execution, and enhanced Python integration with Arrow. These changes aim to provide AI-native analytics, improve data freshness through features like Auto CDC and Real-Time Streaming, and simplify Spark's use across various applications and services. The release is now available in Databricks Runtime 19 Beta, benefiting engineers and architects working with large-scale data and AI workloads.
feature - Databricks Blog blogaidatabricksengineer ·
Databricks adds Inkling open-weights model for AI agents and coding
Databricks has integrated the Inkling open-weights model from Thinking Machines Lab via its Unity AI Gateway. This allows enterprises to build and deploy AI agents and coding applications using their own data, benefiting from customizable models, centralized governance, and cost-effective deployment. Inkling is available now on Databricks and can be accessed through the AI Playground or deployed via the Unity AI Gateway.
feature announcement - Databricks Blog bloginfradatabricksengineer ·
Databricks Introduces Real-Time Mode for Spark Structured Streaming
Databricks has launched Real-Time Mode (RTM) for Apache Spark Structured Streaming, enabling sub-second latency for operational workloads like fraud detection and IoT monitoring. This feature simplifies the tech stack by eliminating the need for separate real-time processing engines, leveraging existing Spark expertise and APIs with a simple trigger configuration change. RTM is available now, offering a unified platform for both analytical and operational data processing to reduce complexity and costs.
feature announcement - Databricks Blog blogaigovernancedatabricksengineer ·
Databricks: Data-Native AI Agents Offer Integrated Governance and Security
Databricks advocates for running AI agents within their Data Intelligence Platform instead of separate stacks to avoid issues like fragmented governance, high egress costs, and latency. Data-native agents embed governance directly into computation, enforced at query planning time, unlike post-hoc controls that fail when agents compute over data. This integrated approach on Databricks, using features like Unity Catalog and AI Gateway, enables faster, more secure deployment of enterprise AI applications by keeping data, governance, and policies together.
feature announcement - Databricks Blog blogaidatabricksengineereducation ·
Databricks applies GenAI to improve higher education student advising
Databricks has released a new solution that leverages Generative AI to address challenges in higher education student support services. The platform uses LLM-based transcription and analysis to improve advisor quality and identify student needs at scale, reducing manual review costs and providing faster insights. This solution is available on a single, governed platform for higher education institutions.
feature announcement - Databricks Go SDK Releases sdkinfradatabricksengineer ·
Databricks SDK Go v0.159.0: New fields, one breaking change
The Databricks SDK Go version 0.159.0 introduces new fields for disaster recovery and pipeline configurations, enhancing data resilience and pipeline management. However, it also removes the `CodeSourcePath` field from `AiRuntimeTask` in the jobs service, which is a breaking change requiring user code updates. This release impacts developers using the Go SDK for Databricks integration.
breaking patch - 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 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.
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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.