Databricks

Restoring a 100 TB database with Lakebase Postgres takes seconds. How? A branch-based restore is a metadata operation. It creates a branch at a point in time, referencing immutable history that already lives in decoupled storage. No copying data, no replaying WAL. Recovery is near-instant regardless of database size, and simple enough for agents to use in branching and undo workflows. https://lnkd.in/gae_xYpD

With Unity Catalog managed tables, you can keep your data in cloud storage you own, at a location you control. ✓ Set managed storage locations at the metastore, catalog, or schema level ✓ Change where new tables land as requirements evolve ✓ Choose the destination when converting external tables to managed Read our guide to storing Unity Catalog managed tables: https://lnkd.in/gkbZ_vxB

Hamlet is using AI to turn thousands of local government meetings into searchable civic intelligence. Behind that product is an equally modern development workflow. Every pull request gets its own Lakebase Postgres branch of production data, so engineers can test fixes against the exact data that caused a bug while AI coding agents run pipelines and validate changes in isolation before a person approves the merge. The team previously spent several hours per engineer each week keeping test data current. Now, more of that time can go into improving the product. Swipe through to see how Hamlet made it work: https://lnkd.in/gVeECCUF

Databricks and NVIDIA are expanding our collaboration so enterprises can customize open models on their own data and take them all the way to production. With AI Runtime, developers can use NVIDIA NeMo, NGC containers and new Nemotron cookbooks to train and fine-tune models on serverless NVIDIA GPUs alongside governed enterprise data. Nemotron 3.5 lightning and Super VL are also ready to load, adapt and serve on Databricks from day one. Customize with governed enterprise data. Serve in production. All on Databricks. https://lnkd.in/gnDdK473

AI Runtime is now generally available, bringing serverless, on-demand GPUs directly into Databricks to power the next generation of AI development. As AI teams scale model training and agent-driven experimentation, they need GPU infrastructure that can keep pace. AI Runtime delivers seconds-level launch latency, reliable multinode training, and Unity Catalog governance on every workload. Hundreds of customers, including Robinhood, PicPay, Flo Health Inc. and FactSet already use it to ship deep learning models to production. Accelerate AI innovation, from experimentation to production → https://lnkd.in/ggWMXU3h

Introducing MemCurrent, a persistent memory architecture designed to help production agents stop starting from zero. MemCurrent combines episodic memory with derived memory across three access patterns: • Plugins to log and load context automatically • Tools the agent calls on demand • An offline “dreaming” pipeline that consolidates memory in the background Agents carry forward user preferences, prior corrections, metric definitions and successful workflows instead of rediscovering them every session. On our internal benchmark built from real enterprise data, MemCurrent triples the agent's pass rate while using 31% fewer tokens and running 26% faster. It's built into Agent Bricks' Memory offering and already powers memory in Genie. Read the research: https://lnkd.in/gg4wrSrK

Two major milestones for CustomerLake: Campaign Agents is advancing to Beta, and Profile Agents is entering Private Preview. The Agentic CDP embedded in Databricks lets marketers build Customer360 profiles, activate audiences, and personalize 1:1 engagement at scale, all on the trusted Data + AI foundation your business already runs on. Marketing agents work right where trusted customer data, AI models, and governance live, so they have the context and guardrails to deliver relevant experiences safely and effectively. Early results include 90% faster audience turnaround, a 10 point lift in conversion rates, and a 40% reduction in CDP total cost of ownership. Learn more: https://lnkd.in/gSggNxAZ

More than 1 million Genie Agents were created in 2026. Which ones should you build first? Across dozens of rollouts, the agents that get adopted fastest share a clear pattern: tightly scoped domains, high-frequency repetitive questions, and governed data with strong metadata. One team built a risk and portfolio Q&A agent that saw usage climb before it even reached production. Another had a working agent live in weeks because the demand and data were already there. We put together a simple rubric to help you score and rank candidate workflows before you build 👇 https://lnkd.in/gNB2b-dZ

Open table formats are important. But they don’t answer the harder question: who actually controls the data, and can you move it without copying everything somewhere else? That’s the standard we use for openness in Unity Catalog. Your managed tables can live in customer-owned storage. The best table features work on those tables. And with REGISTER and UNREGISTER in Private Preview, tables can move between catalogs while the underlying data stays in place. For us, an open catalog should give you storage ownership, full capabilities on the tables you own, and a clean way out. https://lnkd.in/gwTHcWnM

The energy has been electric at #DataAIWorldTour 2026, and we’re not even halfway through! Next up: 🌬️ Thu, Oct 29 — Chicago 🇩🇪 Wed, Nov 4 — Munich 🍑 Tue, Nov 10 — Atlanta 🇳🇱 Tue, Nov 10 — Amsterdam 🇬🇧 Wed, Nov 11 — London Across each stop, learn how organizations are grounding AI in enterprise data and business context, governing data and AI usage, and putting apps, agents and analytics into production. Find a stop near you: https://lnkd.in/gxCdJEgu