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
- endoflife.date eoldatabricks-runtime ·
Databricks Runtime 13.3 reaches end of life in 30 days
Databricks Runtime 13.3 reaches end of life on 2026-08-22 (30 days from now).
deprecation - 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 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 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 - Databricks Blog blogaidatabricksengineerfinance ·
Databricks Genie One powers AI-driven finance operations
Databricks has introduced Genie One, an AI coworker designed to help finance teams in tech and AI-native companies manage complex unit economics. It addresses the challenge of rapidly changing business metrics by grounding answers in a constantly updated ontology, providing real-time insights into gross margin, consumption revenue, and compute spend. This allows finance professionals to proactively protect growth economics by identifying risks and opportunities before they impact the business.
feature announcement - Databricks Blog blogaigovernancedatabricksengineer ·
Databricks Unity AI Gateway adds Model Provider Services
Databricks introduced Model Provider Services (MPS) in Unity AI Gateway, allowing organizations to securely access and govern external AI models like Meta's Muse Spark 1.1. This feature centralizes API key management, enforces access controls via Unity Catalog, and provides end-to-end observability for usage and spend. MPS aims to simplify the adoption of new AI models by consolidating governance and security across different providers.
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 blogaidatabricksengineer ·
Databricks Introduces Unified Context for Enterprise AI
Databricks unveiled Genie One and Genie Ontology to address scattered business context, a key limitation for AI assistants in decision-making. Genie One acts as an AI coworker, leveraging unified context to provide business-term answers grounded in trusted data and enable actions within existing tools. Genie Ontology serves as the central context layer, mapping business operations to help AI understand and follow key concepts across systems, making AI-driven decisions more reliable and efficient for businesses.
feature announcement - 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 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 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.
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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.
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.