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 181 Databricks releases · Updated
- Databricks Blog blogaigovernancedatabricksengineer ·
Omnigent Adds Contextual Policies for Enhanced AI Agent Governance
Omnigent, an open-source meta-harness for AI agents, now features contextual policies that leverage session state to enhance governance. These policies allow for more nuanced control over agent actions, improving security and cost management by considering the agent's history within a session. This capability is available for various coding and custom agents wrapped by Omnigent, providing richer policy options than traditional static controls.
feature - 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 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 blogaisecuritydatabricksengineer ·
Barracuda Managed XDR adds AI-powered natural language log search
Barracuda Managed XDR has integrated Databricks Genie to enable security analysts to query logs using natural language instead of SQL, accelerating threat investigations. This feature is built with row-level security in Unity Catalog to ensure tenant isolation across thousands of customers. The enhancement significantly reduces the time needed for routine investigations and frees up SOC analyst hours.
feature - 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 - Databricks Blog blogaidatabricksengineer ·
Acxiom Modernizes Marketing Stack with Databricks for Agentic AI
Acxiom has migrated its data foundation from on-premises Hadoop to Databricks, enabling significant performance gains and accelerating the development of agentic AI workflows for marketing. This modernization shifts the company's competitive position from a data supplier to an intelligence layer, allowing for automated marketing processes from audience planning to campaign activation. The move is critical for organizations seeking to leverage AI without being constrained by legacy infrastructure, with Acxiom seeing run times improve by 80-90%.
feature announcement - Databricks Go SDK Releases sdkdatadatabricksengineer ·
Databricks SDK for Go v0.156.0 Adds New Fields
The Databricks SDK for Go version 0.156.0 introduces several new fields across various services, including disaster recovery, jobs, and machine learning experiments. These additions provide more granular information for disaster recovery URLs, run details, and experiment tracking. Developers using the Go SDK for Databricks will benefit from these enhanced capabilities for managing their workloads.
patch - Databricks Java SDK Releases sdkdatadatabricksengineer ·
Databricks SDK Java v0.127.0 adds fields to disaster recovery, jobs, and ML services
Databricks SDK for Java version 0.127.0 introduces new fields across several service APIs, including disaster recovery, jobs, and machine learning capabilities. These additions provide more detailed information for tracking and managing deployments and experiments. This update is relevant for Java developers using the Databricks SDK to interact with these services.
patch - Databricks Java SDK Releases sdkdatabricksengineer ·
Databricks SDK Java v0.126.0: API changes and breaking update
Databricks SDK Java version 0.126.0 introduces new API fields and a breaking change requiring the `role` field for `DatabaseDatabaseSpec`. This update is relevant for Java developers using the Databricks SDK, particularly those interacting with bundle deployments or PostgreSQL database specifications.
breaking patch - Databricks Go SDK Releases sdkdatadatabricksengineer ·
Databricks SDK Go v0.155.0: API updates and breaking change
Databricks SDK Go version 0.155.0 introduces an update to the bundle deployments resource with a new `UpdateTime` field. A significant breaking change requires the `Role` field to be mandatory for Postgres database specifications. These updates primarily affect Go developers using the Databricks SDK for managing Databricks resources.
breaking patch - Terraform Databricks Provider Releases terraforminfradatabricksgadeprecationengineer ·
Databricks Terraform Provider v1.121.0: Breaking Changes and New AI Features
Databricks Terraform provider version 1.121.0 introduces breaking changes to the `databricks_mws_ncc_private_endpoint_rule` resource, tightening read-only attributes and improving provisioning state visibility. It also adds new resources for AI Search and enhancements to cluster configurations. These changes primarily affect users managing Databricks infrastructure with Terraform, particularly those using the `mws_ncc_private_endpoint_rule` resource or looking to integrate with Databricks AI Search capabilities.
breaking patch - Databricks Java SDK Releases sdkdatadatabricksengineer ·
Databricks SDK for Java v0.125.0 adds new fields and enum values
Databricks has released version 0.125.0 of its Java SDK, introducing new fields for job and repository settings. This update enhances flexibility for managing jobs and Git repositories within Databricks. The changes affect developers working with the Java SDK for Databricks automation. The update is available now.
patch - Databricks Go SDK Releases sdkinfradatabricksengineer ·
Databricks SDK for Go v0.154.0 Adds New Fields and Enum Values
Databricks SDK for Go version 0.154.0 introduces several new fields to enhance job and repository management, including ParentPath for job creation and GitCredentialId for repository updates. Additionally, new enum values for workload types in the serving API provide more granular control over model deployment resources. These updates are available now for developers using the Go SDK to interact with Databricks.
patch - Databricks Java SDK Releases sdkdatadatabricksengineer ·
Databricks SDK for Java v0.124.0: API updates and breaking changes
Databricks SDK for Java version 0.124.0 introduces several API additions and modifications, including new fields for ML service configurations and updated workspace client methods. However, it also removes several fields across different services, such as `firstDistinctN`, `lastDistinctN`, `includeBrowse`, `browseOnly`, `externalSecretId`, and `groupName`, which may require consumers to update their existing code. This release focuses on enhancing existing functionalities while also incorporating breaking changes that necessitate careful review by developers using the Java SDK.
breaking patch - Databricks Go SDK Releases sdkinfradatabricksengineer ·
Databricks SDK for Go v0.153.0 Adds Features, Includes Breaking Changes
Databricks SDK for Go version 0.153.0 introduces numerous new fields across various services, enhancing capabilities for catalog management, data classification, job scheduling, and ML operations. This release also includes several breaking changes, primarily involving the removal of fields related to secret browsing and user identification, which may require updates for consumers relying on these specific fields. The updates affect developers interacting with the Databricks API via Go.
breaking patch - Databricks Python SDK Releases sdkinfradatabricksengineer ·
Databricks SDK for Python v0.120.0 adds features, removes user name field
Databricks SDK for Python v0.120.0 introduces numerous new fields across various services, enhancing functionality for workspace Genie, catalog permissions, ML aggregations, and PostgreSQL endpoints. Notably, it adds a `download_message_attachment_visualization()` method for Genie and includes telemetry configuration for serving endpoints. A breaking change removes the `name` field from the `User` object in the IAMv2 service, requiring consumers to adapt.
breaking patch - Terraform Databricks Provider Releases terraforminfradatabricksdeprecationengineer ·
Databricks Terraform Provider v1.120.0: New Postgres API resource, SDKv2 deprecation
Databricks Terraform Provider v1.120.0 introduces a new resource for the PostgreSQL Data API and deprecates older SDKv2 fallback implementations for several resources and data sources. This change affects users managing Databricks resources with Terraform, particularly those relying on the older SDKv2 fallbacks, who should transition to the default Plugin Framework implementations to avoid issues in future releases. The release also includes bug fixes for permissions drift and application authorization.
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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.