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 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 Java SDK Releases sdkdatadatabricksengineer ·
Databricks SDK Java v0.128.0: CDF Configs and Breaking IAM Changes
Databricks SDK Java version 0.128.0 introduces new methods for managing Change Data Feed (CDF) configurations via the workspace client. It also adds a `parent` field to connection objects and a `MINUTES` enum for job triggers. Crucially, this release includes breaking changes to the `internalId` field type for IAM entities (Groups, Service Principals, Users), requiring Java SDK users to update their code to adapt to the String type.
breaking patch - Databricks Go SDK Releases sdkinfradatabricksengineer ·
Databricks SDK Go v0.157.0: New CDF methods, Parent field, and breaking ID changes
The Databricks SDK for Go version 0.157.0 introduces new methods for managing Change Data Feed (CDF) configurations for Postgres and adds a Parent field to catalog connection requests. It also includes breaking changes, modifying the `InternalId` field for IAM groups, service principals, and users to be a string type. These updates are relevant for developers using the Go SDK to interact with Databricks services, particularly those managing Postgres CDF or IAM entities.
breaking patch - 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 - 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 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 v0.124.0: 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
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.