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 blogaigovernancedatabricksengineer ·
Databricks Blog Post: Navigating AI Compliance
This Databricks blog post explains the necessity of AI compliance, covering frameworks like the EU AI Act and NIST AI RMF, and outlines how organizations can manage risks and implement controls across the AI lifecycle. It emphasizes the importance of continuous monitoring, bias testing, and data governance to ensure AI systems operate within legal and ethical boundaries, especially as regulations like the EU AI Act impose significant penalties for non-compliance. The guide is intended for compliance teams, data science leads, and business owners involved in deploying AI systems at scale.
announcement - Databricks Blog blogaigovernancedatabricksengineer ·
Responsible AI Guide: Governance, Principles, and Practical Application
Databricks has released a comprehensive guide on Responsible AI, covering its definition, core principles, and practical implementation across the AI lifecycle. This guide emphasizes the shift of Responsible AI from a compliance footnote to a core governance discipline, driven by increasing regulatory pressure and generative AI adoption. It is essential for data scientists, AI governance teams, and business leaders to manage risks, build trust, and ensure ethical AI development. The guide details technical best practices, governance structures, and regulatory considerations, including the EU AI Act.
announcement - Databricks Blog blogaigovernancedatabricksengineer ·
AI Transparency: Governance, Explainability, and Data Practices in AI Systems
This article outlines the importance of AI transparency in building trustworthy and compliant AI systems, emphasizing governance, explainability, and robust data practices. It highlights how transparency, distinct from explainability and interpretability, is crucial for decision-making, trust, and meeting regulatory demands like the EU AI Act. The piece details necessary documentation artifacts, architectural considerations for explainability, and the practical integration of these concepts for effective AI deployment.
announcement - Databricks Blog blogaidatabricksengineerretail ·
AI's Three Levers for Transformation in Retail and Consumer Goods
This article argues that AI, deployed as a system, is the first technology capable of overcoming the traditional barriers of trust, time, and cost that prevent businesses from acting on data insights. It highlights how AI can transform unstructured data into actionable signals, expand analytical possibilities, and automate actions, thereby accelerating decision-making and reducing reliance on manual processes. The piece targets leaders in retail, consumer packaged goods, and travel industries facing challenges in leveraging their data effectively.
announcement - Databricks Java SDK Releases sdkinfradatabricksengineer ·
Databricks SDK Java v0.131.0 adds fields, includes breaking API changes
Databricks SDK Java version 0.131.0 introduces new fields across several services, including ML, Jobs, Networking, and Workspace. This release also contains breaking changes affecting IAM v2 client methods and the TimeWindow object in ML, requiring consumers to update their code. These updates are now generally available.
breaking patch - 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 Python SDK Releases sdkinfradatabricksengineer ·
Databricks SDK for Python v0.121.0: API updates and breaking changes
Databricks SDK for Python version 0.121.0 introduces new fields and methods across various services, including workspace grants, disaster recovery, and machine learning aggregations. Notably, several breaking changes are implemented, such as making the 'role' field required for PostgreSQL database specs and removing fields related to secret browsing and distinct count aggregations. The release also includes improved documentation for authentication types, benefiting developers using the SDK.
breaking patch - 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 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 ·
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
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.