Ready to sharpen your data and AI skills this fall? The Databricks Advanced Learning Festival runs through October 14, with self-paced learning pathways across Data Engineering, Data Analytics, Machine Learning, Generative AI Engineering, Apache Spark™ and Data Warehousing. Complete at least one listed pathway during the event window to receive 50% off any Databricks Certification and 20% off a yearly Databricks Academy Labs subscription! Explore the pathways and enroll through Customer Academy:

More AI tools usually means more questions: who’s using what, what’s it costing, and is it actually working well? In this webinar, Matei Zaharia and Kasey Uhlenhuth show how Unity Gateway helps teams see and control AI usage across models, agents, harnesses, and workflows. They also cover how Omnigent lets builders combine and share the coding agents and SDKs they already use, while leaders keep governance, cost, and quality in check. Watch now:

We found and eliminated an estimated $1.2M a year in wasted AI spend and lost productivity in one hour. Here's how 👇 Using Unity Gateway tracing and Genie One, we identified seven small MCP-server bugs that were quietly driving: • ~$499K/year in wasted tokens • ~12,000 engineering hours/year in agent wait time • 1,409 tool errors in a single 24-hour window Genie One turned the trace data into a ranked list of what to fix, and a coding agent helped close the loop in an hour.

Here's how to run Jev-style decision models directly on governed data with Databricks 👇 Serve open-weight models like SemIf-OpenJev on serverless GPUs with AI Runtime + Model Serving, then call them directly from SQL or Lakeflow jobs using ai_query to return structured classifications and probability scores. ↳ Pick your model ↳ Select Serverless GPU ↳ Click Run All One importable notebook. Swap in your own data and decision criteria from there.

What started in Bear Territory is back where it all began 🐻🧱 Proud to announce a new partnership with @databricks and the “Databricks Field at California Memorial Stadium.” Databricks is now the Official Enterprise Data + AI platform for Cal Athletics.

A new model comes out roughly every five days. The best model for a coding task today may not be the best or most cost-effective choice a few months from now. The new Unity Gateway CLI helps organizations keep up with that pace. Developers can keep working in Claude Code, Codex, OpenCode and other coding agents, while admins centrally manage models, tools, policies and budgets in Unity Gateway. When a better option emerges, teams update the configuration once and the CLI rolls it out across supported agents. Now with the Unity Gateway CLI, teams can: • Set default models, MCP servers, skills, Smart Routing and spending policies centrally • Launch approved coding agents with one command and automatically apply organization settings • Track spend and capture traces across supported coding agents

All seven Databricks co-founders began our journey together at UC Berkeley. We started the company in a room in Soda Hall, moved to a small office on Addison Street, and spent our first few years in Berkeley before eventually heading to SF. Two of our co-founders are still on the faculty. Today we announced Databricks Field at California Memorial Stadium, our first collegiate athletics sponsorship. Berkeley shaped everything about how this company thinks, and this is our way of investing in the next generation of students and builders who'll do their best work on that campus. Go Bears!

Today, we’re proud to announce Databricks as the new sponsor of Cal Bears Field! 🐻 All seven of our co-founders are Cal alumni. The earliest lines of what would become Databricks were written at Berkeley’s Soda Hall, fueled by late nights and a shared belief that data and AI could accelerate human progress. This sponsorship is our way of investing in the next generation of researchers and builders who’ll have their own breakthroughs there. Our mission has always been to democratize data and AI. It started at Cal. We’re glad to help it continue there. Go Bears. 💙

At the beginning of this year, big portions of @databricks started running on Genie. When finance started doing all their work with Genie, we noticed that they were combining Genie with a Live Cloud Spreadsheet called Row Zero. This combination is really powerful. We met the Row Zero team and were blown away. Today, we're excited to announce that we've agreed to acquire Row Zero. Stay tuned for an awesome experience of Genie + Row Zero:

Big news: @GetRowZero is joining Databricks! 📊 We’re bringing a native, fully governed spreadsheet experience to Databricks Genie, combining the flexibility of spreadsheets with enterprise-grade security and lineage powered by Unity Catalog. Finance, operations, sales, and marketing teams will be able to work, model scenarios, and collaborate using standard spreadsheet logic without leaving their data and AI foundation. Welcome to the team, Row Zero! 🚀

Give coding agents room to experiment without putting production data at risk. Lakebase Branching creates isolated, short-lived database environments that capture both schema and data, so agents can test changes without affecting production. Watch how to set up an agentic branching workflow with the Databricks CLI and AGENTS.md, compare changes against the parent branch, and move approved updates through CI/CD.

Genie One MCP is a new way to bring governed business context into the AI assistants and coding agents your teams already use. It connects MCP-compatible clients like ChatGPT, Claude and Microsoft Copilot to Genie One, so they can work with approved definitions, trusted data relationships and permission-aware access. Our latest walkthrough covers how to set it up, how identity and access controls work, and best practices for managing and monitoring it in production:

Exciting day for Databricks customers! We’re launching three new frontier models on Databricks today. ↳ @OpenAI's GPT-6 Sol and GPT-6 Luna, with strong performance at lower cost and a higher Pareto frontier on the OfficeQA Pro benchmark ↳ @AnthropicAI's Claude Opus 5.5, with substantial improvements in cost efficiency and strong performance on enterprise document parsing tasks The industry’s best models are shipping faster than ever. Putting them into production and scaling your AI footprint means governing every model call, controlling costs, monitoring performance, and using the right model for the right task. Unity Gateway gives you one governance layer across every model, so you can govern and audit requests, set budgets, track costs, and choose the best model for each job.

Databricks Genie One MCP is now generally available! Teams working in ChatGPT, Claude, Cursor, or custom agents can now use one interface to access structured and unstructured data, insights, and answers from Genie One, grounded in governed business context from Genie Ontology. Key highlights: • 𝐂𝐨𝐧𝐬𝐢𝐬𝐭𝐞𝐧𝐭 𝐛𝐮𝐬𝐢𝐧𝐞𝐬𝐬 𝐜𝐨𝐧𝐭𝐞𝐱𝐭 across agents, with shared definitions and domain semantics • 𝐑𝐢𝐜𝐡, 𝐝𝐚𝐭𝐚-𝐬𝐦𝐚𝐫𝐭 𝐞𝐱𝐩𝐞𝐫𝐢𝐞𝐧𝐜𝐞𝐬 including questions, results, visualizations, and Genie Ontology citations • 𝐂𝐞𝐧𝐭𝐫𝐚𝐥𝐢𝐳𝐞𝐝 𝐠𝐨𝐯𝐞𝐫𝐧𝐚𝐧𝐜𝐞 through Unity Gateway, with fine-grained policies and audit logging Genie One MCP brings trusted business context into the tools teams already use.

Databricks Chief Information Officer Naveen Zutshi has been named to TIME's 2026 Executives of the Year: Tech & Data list! @TIME highlights Naveen's mission to make every Databricks employee a builder with Genie, while strengthening the data governance behind how Genie accesses and acts on company data. Congratulations to Naveen and all 50 leaders shaping the future of technology, AI, cybersecurity, and digital innovation. Explore the full list:

Databricks CEO and co-founder @alighodsi joined @bhalligan on Long Strange Trip to share his journey to CEO, how to hire and scale, and where the next unlock in enterprise AI is. "You need to capture the enterprise context - every meeting that's being recorded, every email - then feed it to your AI." That's the missing piece that will allow organizations to move from using AI as a chatbot to having thousands of AI agents that work with each other and autonomously to move work forward. Watch the full episode:

Starting an agentic app shouldn’t mean starting from scratch. Databricks DevHub gives developers: ↳ Ready-to-use templates ↳ Docs and step-by-step guidance ↳ Agent prompts you can copy into Cursor, Claude Code, Codex, or another coding agent Build with Databricks Apps, Lakebase, Agent Bricks, and more. Build agentic applications in minutes, not months:

[DEMO] Define features once, then use the same definitions through training and production. See how Databricks Feature Store helps teams build batch and streaming features with Feature Views, create training sets with automatic point-in-time joins, and productionize those features with built-in governance and observability. Plus, see how Genie Code and MLflow support iteration along the way.