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 226 Databricks releases · Updated
- Databricks Go SDK Releases sdksecuritydatabricksengineer ·
Databricks SDK for Go v0.172.0 Enhances IAMv2 and Job Cluster Management
Databricks SDK for Go v0.172.0 is now available, significantly expanding programmatic control over IAMv2 identity and access management across both account and workspace levels. This release introduces a comprehensive suite of new methods for managing users, groups, and service principals, alongside updates for dashboards and job clusters. Developers can now programmatically configure job clusters more flexibly, though a breaking change makes the `NewCluster` field optional for `jobs.JobCluster`. These updates primarily affect Go developers building automation or integrations with Databricks.
breaking patch - Databricks Java SDK Releases sdksecuritydatabricksgaengineer ·
Databricks Java SDK v0.146.0 Adds IAM V2 API, Updates Job Cluster Field
The Databricks SDK for Java has been updated to version 0.146.0, introducing extensive IAM V2 APIs for both account and workspace clients. This release allows developers to programmatically manage users, groups, service principals, and workspace assignments directly through the SDK. Additionally, the `newCluster` field in `com.databricks.sdk.service.jobs.JobCluster` is no longer required, which is noted as a breaking change. These updates enhance automation capabilities for Databricks platform management.
breaking patch - Databricks Python SDK Releases sdksecuritydatabricksengineer ·
Databricks SDK for Python v0.128.0 Adds Account and Workspace IAM V2 API Methods
Databricks SDK for Python v0.128.0 introduces a comprehensive set of new API methods for Account and Workspace IAM v2 services. These additions enable programmatic management of users, groups, service principals, and workspace assignments, enhancing automation capabilities for identity and access control. The release also includes `create_time` and `update_time` fields for `GenieSpace` and a breaking change where the `new_cluster` field for `JobCluster` is no longer required.
breaking patch - Databricks Blog bloggovernancedatabricksgaarchitect ·
Amtrak Builds Unified Data Backbone with Databricks for Rail Network Transformation
Amtrak is building a digital intelligence platform called Rail Intelligence on Databricks to unify its highly siloed operational data. This strategic move supports the organization's largest physical transformation in 50 years, enabling predictive maintenance, enhanced safety, and smarter capital decisions for its vast rail network. Leveraging Delta Lake, Unity Catalog, and MLflow, the platform integrates diverse data sources from new train fleets and infrastructure. It delivers insights to mechanical teams, operations, and executives, with future plans for agentic workflows and natural language querying.
announcement - Databricks Blog bloginfradatabricksarchitect ·
Databricks Re-architects Serverless Network Config Delivery for 97.5% Latency Cut
Databricks has re-architected its serverless network configuration delivery, moving from synchronous upstream calls to an event-driven pre-computation pipeline. This change significantly improved performance and reliability for its serverless compute platform, which launches tens of millions of VMs daily. The new architecture cut RPC p99 latency by 97.5% (from 5,000ms to 125ms) and boosted service availability to 99.99%. It also reduced upstream call volume by 86%, benefiting customers using SQL warehouses, notebooks, and ML serving endpoints.
feature - Databricks Blog blogaidatabricksengineer ·
How a Major Freight Railroad Scaled Pipeline Creation with Databricks Genie Code
A major Canadian freight railroad leveraged Databricks Genie Code, Unity Catalog, and custom Agent Skills to automate the modernization of its legacy data pipelines. This approach reduced new table ingestion from days to minutes and achieved over 90% automation for pipeline delivery. The solution allows developers to generate production-ready ingestion code from a short YAML prompt, embedding enterprise standards and metadata grounding. This drastically improves throughput for large-scale data estate modernization.
announcement - Terraform Databricks Provider Releases terraforminfradatabricksengineer ·
Databricks Terraform Provider v1.126.0 Released
The Databricks Terraform Provider v1.126.0 release delivers bug fixes to enhance configuration and resource management. It addresses an issue where `config.DefaultHostMetadataResolverFactory` was not respected during provider configuration. Additionally, the update resolves a perpetual plan diff and null `id` problem for `databricks_share` resources, improving stability. This version provides improved reliability for engineers automating Databricks deployments with Terraform.
patch - Databricks Blog blogaidatabricksanalyst ·
AI Automation Reshapes Data Analyst Roles, Prioritizing Judgment Over Queries
Artificial intelligence is automating routine data analysis tasks like SQL queries and dashboard creation, freeing analysts from technical mechanics. This pivotal shift allows data professionals to focus on higher-value activities such as problem framing, contextual judgment, and strategic decision-making. The article argues that this transformation redefines the analyst's job description, emphasizing human skills for interpreting insights and driving business outcomes over technical proficiency, and suggests organizations must adapt hiring and success metrics accordingly.
announcement - Databricks Go SDK Releases sdkaidatabricksengineer ·
Databricks Go SDK v0.171.0 Adds AI & Data API Fields, Includes Breaking Changes
Version v0.171.0 of the Databricks Go SDK introduces several new API fields across services such as apps, catalog, jobs, pipelines, and model serving, enhancing programmatic control over Databricks resources. Key additions include fields for AI Runtime Tasks, GPU workload types, and improved job triggering mechanisms. This release also makes some IAM fields required and contains breaking changes by removing specific `IncludeBrowse` and `BrowseOnly` fields from catalog services. Developers using the Go SDK for automation and integration will need to review these updates, particularly to address the breaking changes and leverage new capabilities for AI/ML workloads and data pipelines.
breaking patch - Databricks Java SDK Releases sdkdatadatabricksgaengineer ·
Databricks Java SDK v0.145.0 Enhances API with New Fields
Databricks has released version 0.145.0 of its Java SDK, introducing several new API fields across various services. This update adds support for specifying MLflow artifact locations in AI runtime tasks and introduces trigger details for job management. Developers can now also configure API source connector options for pipelines and define GCP endpoint settings with additional VPC SC and Google API endpoints.
patch - Databricks Python SDK Releases sdkmldatabricksgaengineer ·
Databricks Python SDK v0.127.0 Adds Job, Pipeline, and Networking API Fields
Databricks Python SDK v0.127.0 introduces new API fields across its services, enhancing configuration capabilities for AI Runtime Tasks and job orchestration. These additions provide more granular control over MLflow artifact locations and job triggers, as well as improved data pipeline source configurations with API connector options. The update also includes fields for Google Cloud VPC Service Controls and API endpoints, improving cloud network integration. This release primarily benefits developers and architects leveraging the SDK for advanced Databricks job management and data platform development.
patch - Databricks Blog blogcompliancedatabricksengineer ·
Databricks details advanced AUTO CDC for bitemporal and partial updates
This Databricks post details how AUTO CDC addresses complex real-world change data capture challenges for data engineers. It explains Bitemporal AUTO CDC for dual-axis history tracking, vital for financial compliance and reproducible ML. Additionally, it covers AUTO CDC Partial Updates, which simplify handling incomplete CDC sources. The article also highlights the expansion of certain AUTO CDC features into open-source Apache Spark 4.2.
announcement - Databricks Blog bloginfradatabricksgaengineer ·
Databricks Open-Sources Metals v2 for Scala & Java Monorepo Support
Databricks has open-sourced Metals v2, a re-engineered Java and Scala language server designed to provide fast code intelligence for multi-million line monorepos. This enables lightweight editors like Cursor, VS Code, and Neovim to offer robust JVM development experiences. The server optimizes for rapid startup and reliable codebase orientation, crucial for current AI-assisted development workflows. Metals v2 is available today in these editors and allowed Databricks to consolidate its internal JVM development away from IntelliJ.
feature - Databricks Blog blogaidatabricksengineer ·
Databricks Integrates Electric's WASM Postgres for AI Agent Sandboxes
Databricks is integrating Electric's WASM Postgres and real-time synchronization technology to enhance AI agent sandboxes. This move extends Databricks' Postgres capabilities to the edge, providing lightweight, localized Postgres instances (PGlite) directly within agents for ultra-low latency context. The integration also includes Electric's real-time sync engine, which keeps distributed agent state synchronized with a central Lakebase. This offering addresses the unique data management needs of agentic applications, where traditional databases fall short due to dynamic data requirements and distributed execution.
feature - Databricks Blog blogdatabricksarchitectfinance ·
Databricks Powers Real-Time, AI-Driven Risk Management for Modern CROs
This article details how modern risk failures are increasingly architectural due to fragmented data and batch processes, impeding timely decision-making for Chief Risk Officers (CROs). It argues that the CRO role is becoming more strategic, requiring integrated, real-time risk intelligence rather than retrospective reporting. Databricks positions its unified, governed platform with real-time data access and AI-enabled analytics as the solution. This approach helps financial institutions reduce reconciliation friction, accelerate scenario analysis, and improve risk decisioning speed.
announcement - Databricks Java SDK Releases sdkaidatabricksengineer ·
Databricks Java SDK v0.144.0 Enhances API for AI Runtimes and IAM
Version 0.144.0 of the Databricks Java SDK introduces several API additions and modifications. These updates enhance integration with various Databricks services, including new support for forwarding user access tokens in apps, managing catalog secrets, and specifying GPU workload types for model serving. Developers using the Databricks Java SDK for building applications and integrations are affected. It also makes several IAM user and service principal fields required, improving data governance.
patch - Databricks Python SDK Releases sdkaidatabricksgaengineer ·
Databricks Python SDK v0.126.0 Enhances APIs with New Fields and Breaking Changes
Databricks has released v0.126.0 of its Python SDK, introducing several new API fields across services like applications, catalog, jobs, and model serving. Key additions include `forward_user_access_token` for apps, `include_value` for catalog secret requests, and `docker_image_url` for AI runtime tasks, alongside new GPU workload types for model serving. This update also makes `account_sp_status`, `display_name`, `account_user_status`, `full_name`, and `username` fields required for IAMv2 service principals and users. Notably, the release includes several breaking changes by removing `include_browse` and `browse_only` fields from various catalog service requests and objects.
breaking patch - Databricks Blog bloggovernancedatabricksgaarchitect ·
Databricks Unifies Genie Agent Governance Across Structured and Unstructured Data
Databricks details how its Genie Agents leverage existing Unity Catalog governance to securely operate across structured and unstructured data. This allows agents to reason over tables, metrics, and documents while respecting user permissions, simplifying architecture and eliminating the need for separate systems for data types. The solution prevents data loss and unauthorized access by establishing Unity Catalog as the security perimeter, rather than relying on the LLM. It utilizes features like Automatic Identity Management (AIM), object privileges, row filters, column masks, and the newly generally available Attribute-Based Access Control (ABAC) policies.
feature announcement - Databricks Blog blogsecuritydatabricksengineer ·
Databricks Omnigent Contextual Policies Prevent AI Agent Data Exfiltration
Databricks details how Omnigent Contextual Policies prevent data exfiltration by AI agents, tackling the "lethal trifecta" vulnerability. This risk arises when agents combine access to private data, exposure to untrusted content, and outbound communication capabilities. Traditional per-action authorization checks fail to detect this sequence, but Omnigent's policies track session state, blocking exfiltration only when the first two conditions are met. The approach allows agents to perform normal tasks without hindrance while safeguarding sensitive information.
announcement - Terraform Databricks Provider Releases terraforminfradatabricksengineer ·
Databricks Terraform Provider v1.125.0: Git credential and workspace ID improvements
Version 1.125.0 of the Databricks Terraform provider introduces enhancements for managing Git credentials and validating workspace IDs. The `git_credential_id` attribute is now optional for `databricks_repo` resources, allowing explicit credential selection. For workspace-level hosts, the provider now resolves and validates `workspace_id` from discovery metadata, avoiding unnecessary SCIM calls and improving plan-time validation. These changes primarily affect engineers and architects using the Databricks Terraform provider for infrastructure automation.
feature 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.
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.