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 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 blogaidatabricksengineer ·
Cushman & Wakefield unified AI with Databricks
Cushman & Wakefield built an enterprise AI core over four years by embedding technologists into business units and prioritizing trust over pilot programs. They implemented a product operating model and a capital investment framework co-created with business leaders to align all 53,000 employees around common outcomes. Utilizing Databricks, including Genie for natural-language data governance, they reduced idea-to-outcome timelines from months to days, demonstrating a successful strategy for scalable AI deployment.
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 ·
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 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 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 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 blogdataanalyticsdatabricksarchitect ·
Evaluate Enterprise Analytics Platforms Beyond Dashboards
This article argues that enterprise analytics platform evaluations often focus too narrowly on dashboards and features, overlooking the critical architectural decision of whether analytics, AI, and agents can run on a unified data foundation. It proposes a structured approach using seven weighted criteria and a proof of concept to pressure-test vendor claims beyond demos. The evaluation is crucial for architects and engineers as it shapes the data team's capabilities for the next decade.
announcement - 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 blogdataazuredatabricksengineer ·
Guide: Migrating Azure Synapse workloads to Databricks Lakehouse
This guide provides a practical playbook for migrating workloads from Azure Synapse Analytics (Dedicated SQL, Serverless SQL, and Spark pools) to a unified Databricks Lakehouse. It details how to consolidate multiple services, enable AI and ML capabilities, and improve operational efficiency, leading to simpler architecture, better performance, and lower costs. The document outlines a phased migration strategy, emphasizing discovery, assessment, and design with field-tested engineering tips for successful execution.
announcement - 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
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.