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 Java SDK Releases sdkinfradatabricks ·
Databricks SDK Java v0.133.0 Adds Fields to Workspace, Job, and Serving APIs
Databricks SDK Java version 0.133.0 introduces new fields across several service APIs, including `effectiveEntitlements` for workspace assignments, `serverlessComputeId` for job clusters, and `tableNames` along with `telemetryProfileId` for serving telemetry configuration. These updates enhance the SDK's ability to manage and configure Databricks resources. This is a patch release, affecting developers using the Java SDK for Databricks integration.
patch - Databricks Go SDK Releases sdkgovernancedatabricksengineer ·
Databricks SDK for Go v0.162.0 Adds IAM and Jobs Fields
The Databricks SDK for Go version 0.162.0 introduces new fields to enhance IAM and job cluster configurations. It adds `EffectiveEntitlements` for `WorkspaceAssignmentDetail` and `ServerlessComputeId` for `JobCluster`, providing more granular control and visibility into workspace assignments and serverless compute options. These updates are relevant for Go developers integrating with Databricks APIs, offering expanded capabilities for managing entitlements and configuring serverless job clusters.
patch - 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 blogdataaidatabricksengineer ·
Databricks Lakehouse for R&D Data and AI Agents
Cellcentric's Data Hub, built on Databricks Unity Catalog and Lakehouse Federation, integrates scattered R&D data into a unified, AI-ready product. By prioritizing context coverage as a quality metric, the platform accelerates R&D investigations and provides agents with the same governed context as human users. This approach ensures secure and traceable data access for both employees and AI clients.
announcement - 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 Go SDK Releases sdkinfradatabricksengineer ·
Databricks SDK Go v0.161.0: API changes and breaking updates
Databricks SDK Go version 0.161.0 introduces several new fields across various services, including jobs, machine learning, networking, and serving, enhancing functionality for users. However, it also includes breaking changes to IAMv2 methods and modifications to window duration fields in ML services, requiring developers to update their code. These updates affect users interacting with Databricks account and workspace IAM features, as well as those utilizing ML time window functionalities.
breaking patch - Databricks Python SDK Releases sdkinfradatabricksengineer ·
Databricks SDK for Python v0.122.0 Introduces New APIs and Breaking Changes
This release of the Databricks SDK for Python (v0.122.0) adds new methods for managing clean rooms and PostgreSQL configurations, along with numerous field additions across various services. Notably, it includes several breaking changes, particularly affecting IAM v2 and ML TimeWindow configurations, which may require adjustments in existing integrations. These updates are primarily relevant to developers and architects working with the Databricks platform and its associated APIs.
breaking patch - Databricks Java SDK Releases sdkmldatabricks ·
Databricks SDK for Java v0.132.0: API changes
Databricks SDK for Java version 0.132.0 introduces several API modifications, including a new field for pipeline connector options and changes to window duration requirements for ML components. These updates affect developers using the Java SDK for Databricks integrations, particularly those working with ML models or data pipelines.
breaking patch - 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 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 - Terraform Databricks Provider Releases terraforminfradatabricksengineer ·
Databricks Terraform Provider v1.122.0 adds Postgres CDF, fixes view column comments
Version 1.122.0 of the Databricks Terraform provider introduces new resources for managing PostgreSQL Change Data Feed (CDF) configurations and their statuses. This release also resolves a bug that prevented updates to column comments on views, a common issue for engineers managing SQL objects via Terraform. The fix ensures that column comment changes on views are applied correctly, preventing perpetual diffs for affected users.
patch - 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
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.