Databricks just closed a $5B round at a $190B valuation — up from the $188B term sheet from July, after crossing $7B in revenue run-rate and growing 80%+ YoY.
Electric is joining Databricks bringing WASM Postgres to AI agent sandboxes as part of the move.
For context: Electric has been building data primitives specifically for agent workloads — this includes PGlite (Postgres compiled to WASM, runs right in the browser/sandbox) and a real-time sync engine that keeps data in sync across distributed agents and a centralized Lakebase.
The idea is to extend Databricks' Postgres capabilities from the lakehouse all the way out to the edge. That means agents can spin up lightweight, open-source databases locally, and everything syncs back to Lakebase Postgres for centralization — all sitting on cheap, durable object storage.
Databricks officially closes Panther acquisition — folding it into Lakewatch to build an “agentic SIEM”
Databricks just announced Public Preview for two new Unity Catalog features:
Just came across developers.databricks.com — looks like Databricks pulled together a dedicated dev hub for building and deploying data apps/AI agents. A few things that stood out:
There's also a big library of ready-made templates (RAG chat app, Genie analytics, inventory intelligence, SaaS tracker, etc.) you can scaffold with databricks apps init, plus a prompt you can drop into Cursor/Claude Code/Codex to have a coding agent walk you through building an app end-to-end.
Worth a look if you're building internal tools or agents on top of Databricks.
Felt Like this is timely given the World Cup Fever. May the best team win the final.
Coach's Corner turns 51 million rows of match tracking data into a sub-second 2D/3D bench app for coaches, spanning the entire Databricks platform from Lakeflow ingestion to Genie scouting and agentic opponent dossiers.
Exciting to see Databricks raising funding at a $188 billion valuation.
Inkling, the open-weights model from Thinking Machines Lab, is now available on the Databricks platform through Unity AI Gateway, making it easy to build and deploy AI agents and coding applications on enterprise data.
Databricks just announced Public Preview of Feature Views, a new abstraction meant to fix the classic "build a feature in a notebook, then re-engineer it for production" problem in ML.
Key points:
Databricks quietly renamed Genie Spaces to Genie Agents. Not just a label swap — they now take autonomous action using MCP connections, scheduled tasks, and writes to external systems, and can reason over unstructured docs, not just tables. Basically your curated "Genie Space" leveled up from Q&A chatbot to actual coworker.
New Omnigent feature: policies aren’t just per-action allow/deny anymore — they can track state across a whole session.
Read a confidential doc? Writes get blocked for the rest of the session.
Risk score climbs too high? Human approval kicks in.
Spend crosses a threshold? Auto-downgrade to a cheaper model instead of a hard stop.
Basically stateful guardrails instead of static rules.
Genie Code now supports migrating from Tableau and PowerBI.
With the new /𝒊𝒎𝒑𝒐𝒓𝒕𝑩𝑰 command, you can:
→ Upload a .twb, .twbx, .tds, .tdsx, or .pbit file directly
→ Auto-generate an AI/BI dashboard that mirrors your existing visualizations
→ Convert measures & dimensions into reusable metric views
→ Promote them to Unity Catalog for governance, lineage, and access control
The migration path that used to take weeks now starts with a single command.
As coding agent adoption explodes across enterprises, standardizing configurations and controlling costs has become a real challenge.
ucode is a lightweight launcher that connects Claude Code, Gemini CLI, GitHub Copilot, and others directly to Databricks Unity AI Gateway - turning chaos into a single, governed entry point.
Databricks launched AI Spend Controls in Unity AI Gateway, adding proactive budget alerts that let organizations set AI cost limits at the per-user, per-use-case, per-workspace, and per-account levels. The goal is to prevent runaway AI costs - like agents stuck in retry loops or accidental overnight experiments - before they show up on the bill. All spend is logged to Unity Catalog system tables for detailed analysis by user, model, provider, and team.
Databricks is rolling out GPT-5.5 for enterprise agent workflows. The model set a new record on Databricks' own benchmark (OfficeQA Pro), which tests AI on messy real-world document tasks like scanned PDFs and long files — cutting errors by 46% vs. GPT-5.4 and becoming the first model to break 50% accuracy on the benchmark.
Your CRM data is a mess. Everyone knows it. Most AI tools pretend it isn't.
Databricks took a different approach with PipelineIQ - instead of building yet another forecasting model that assumes clean data (spoiler: it never is), they built a prescriptive action engine that works with the chaos.
The result? Every deal in the pipeline gets one of three verdicts:
🚶 Walk - disengage, this isn't worth your time
🔄 Pivot - viable deal, wrong approach
🚀 Accelerate - conditions are right, lean in now
No vague "insights." No dashboards that require a PhD to interpret. Just: here's what to do today.
Built on Databricks' own stack (Foundation Model APIs, Delta Lake, Unity Catalog) and used internally by their own sales org - this is a rare "we built it for ourselves first" story.
Read the full blog here: https://www.databricks.com/blog/pipelineiq-forward-looking-sales-intelligence-drives-action
With Catalog Commits, catalogs become the system of coordination for Delta tables, brokering table discovery, access, and state across engines.
Databricks to invest US$300 million in ANZ over the next three years.
The plan is to open a new ANZ headquarters, grow Lakebase and Genie adoption, and upskill 100,000 people in data and AI.
>Stripe data is now available on the Databricks Marketplace - unlocking significant new value for customers. Check this out
