This is why you use database branching.
Never give your agents access to prod. Ever.
I love @neondatabase - have an article in the works about how i use it with agents, should be out this week
Databricks has been named one of TIME's 10 Most Influential Software Companies of 2026.
The recognition reflects where intelligent analytics and enterprise AI are heading: any employee can query their data in natural language, no programming required.
This innovation takes a certain kind of conviction to build. As our CEO Ali Ghodsi puts it: "To win the market, you have to have a non-consensus opinion, and you have to be right."
Our next big bet: databases built for the AI era. https://t.co/VAl9mMljzz
𝐆𝐞𝐧𝐢𝐞 is now the most important way to do data analysis in Databricks. What's unique about it is its ability to extract semantics from your entire Lakehouse, enabling it to answer complex data questions that cripple agents without a deep data understanding. We've now added a Mobile version, added Unstructured data processing, as well as enabled it to operate on all your dashboards and notebooks. Check it out:
https://t.co/bqPvg2lYS7
Databricks is excited to partner with @OpenAI on GPT-5.5, their latest frontier model. GPT-5.5 will be available in Unity AI Gateway on launch. You can use it with coding tools such as Codex, or to power your enterprise agents.
GPT-5.5 is state-of-the-art on many benchmarks including OfficeQA Pro, our benchmark for evaluating grounded reasoning on enterprise tasks.
We are partnering with OpenAI to co-launch on Databricks. Hear more from our co-founder @pwendell and OpenAI CRO @dhdresser on GPT-5.5 in Databricks. https://t.co/daeHkBB1pT
In my continuing efforts to add "DIY" in front of existing industries to see what happens (DIY Drones, DIY Robocars, now DIY SDLs -- self-driving labs), here's my latest in democratized lab automation
Although most color-mixing SDLs use robotics and colored liquids, this really minimal version just uses an ESP32 with a RGB LED and a color sensor. You choose various different algorithms to try to learn color theory and find a target color. No messy liquids and it fits in your pocket!
You can put it together for $40. Link in comments
This is a HUGE deal from Databricks.
Security data (think logs etc) are massive, and one of the main data sources that *already* exist in the Lakehouse. However even though Lakehouses have powerful processing abilities, until now you had to extract that data and load them in proprietary “data islands” with limited functionality.
This means your data was difficult to access, security insights hard/slow to get, and costs went through the roof.
Open Lakehouse + Lakewatch is the way to go. Excited to offer this new approach to the market!
Congrats to the wrestling Bells who finished 3rd in the section! Below are our CIF qualifiers who will be heading to state!
108 Jason Liau 3rd
126 Julian Holguin 1st
140 Liam Conway 4th
146 Nathan Jones 4th
152 Tommy Holguin 1st
192 Rocco Biasotti 2nd
217 James Hayden 2nd
65% growth @ $5B+ ARR is outstanding and wouldn’t be possible without the AI disruption. AI and LLMs are cool, but they only work with good data, governance and evals. Databricks does all of that and helps AI deliver on its promise.
I now constantly get questions about the SAAS meltdown, role of AI, system of records etc. I don't have an answer to all these.
But I do know that we saw an acceleration in our business in Q2, Q3, and now finished the year with accelerating Q4.
The question is, why?
Short answer: AI. But the underlying reason is subtle. We are growing fast because we are finally removing the biggest bottleneck in data: the technical barrier to entry.
For years, if you didn’t know SQL, Python, you were locked out of the value chain. That has changed fundamentally with the 𝐆𝐞𝐧𝐢𝐞 𝐟𝐚𝐦𝐢𝐥𝐲, and it is the "secret sauce" behind our recent momentum:
• 𝐆𝐞𝐧𝐢𝐞: Analysts can query data without any SQL. I use this every day myself.
• 𝐃𝐚𝐭𝐚 𝐒𝐜𝐢𝐞𝐧𝐜𝐞 𝐆𝐞𝐧𝐢𝐞: Builds end-to-end AI models for you, similar to Cursor for ML on your data.
• 𝐃𝐚𝐭𝐚 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫 𝐆𝐞𝐧𝐢𝐞: Write Spark pipelines, does plumbing, troubleshooting.
We've been talking about DATA + AI democratization, but generative AI finally enabled it in a way that wasn't possible before. That's why we're seeing a market response.
Take 𝐋𝐚𝐤𝐞𝐛𝐚𝐬𝐞 𝐏𝐨𝐬𝐭𝐠𝐫𝐞𝐬. We launched this serverless engine for agents and apps recently. At 8 months into its journey, its revenue is already 2x what our Data Warehouse product was at the same stage.
All this taken together, we ended up with the following stats for Q4:
🚀 $5.4B Revenue Run-Rate, growing >65% YoY
🚀 $1.4B AI Revenue Run-Rate
🚀 FCF Positive for the year
🚀 NRR >>140%
https://t.co/yq3riYyr8r
Reynold covers how we went from $0 to $1B ARR in a completely new business we launched just 4 years ago: Data Warehousing. Today, all our focus is on Lakebase Postgres! Read this 👇
Favorite part of @alighodsi's keynote: we're announcing a new data warehouse that's 29% faster but 35% more expensive -- NO, WAIT, that was the other conference last week! We're just making DBSQL 25% faster.
📣 After months of collaboration across the open source community, we’re thrilled to announce the release of 𝗠𝗟𝗳𝗹𝗼𝘄 𝟯.𝟬! 🎉
𝘞𝘩𝘺 𝘵𝘩𝘪𝘴 𝘮𝘢𝘵𝘵𝘦𝘳𝘴:
GenAI has changed the game, introducing complex execution flows, subjective output quality, and new challenges for observability and evaluation. MLflow 3.0 meets these challenges head-on, expanding the platform trusted by millions for ML operations into a unified solution for all AI workloads.
Key highlights of MLflow 3.0:
🌟 𝗗𝗲𝗲𝗽 𝗢𝗯𝘀𝗲𝗿𝘃𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝘄𝗶𝘁𝗵 𝗧𝗿𝗮𝗰𝗶𝗻𝗴
Instantly capture complex GenAI execution flows—including every LLM call, vector retrieval, document ranking, and prompt orchestration. MLflow Tracing provides a complete, hierarchical timeline for debugging and monitoring, all with a single line of code.
🌟 𝗦𝘆𝘀𝘁𝗲𝗺𝗮𝘁𝗶𝗰 𝗤𝘂𝗮𝗹𝗶𝘁𝘆 𝗘𝘃𝗮𝗹𝘂𝗮𝘁𝗶𝗼𝗻
Evaluate GenAI quality at scale using the new evaluation framework. Assess outputs with built-in and custom LLM judges, human feedback, and automated scoring—directly on traces—so you can compare and improve application versions with confidence.
🌟 𝗨𝗻𝗶𝗳𝗶𝗲𝗱 𝗔𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗟𝗶𝗳𝗲𝗰𝘆𝗰𝗹𝗲 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁
Treat GenAI applications as first-class, versioned artifacts. Package, register, and deploy your complete app—including models, prompts, retrieval logic, and dependencies—atomically, ensuring what you test is exactly what you deploy.
🌟 𝗘𝗻𝗵𝗮𝗻𝗰𝗲𝗱 𝗠𝗼𝗱𝗲𝗹 𝗥𝗲𝗴𝗶𝘀𝘁𝗿𝘆 & 𝗗𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁
The new LoggedModel abstraction tracks the full lifecycle of both GenAI and traditional ML models, linking metrics, parameters, and traces across training, evaluation, and deployment. Deployment Jobs orchestrate evaluation, approval, and deployment with automated quality gates, all governed by Unity Catalog.
🌟 𝗜𝗺𝗽𝗿𝗼𝘃𝗲𝗺𝗲𝗻𝘁𝘀 𝗳𝗼𝗿 𝗧𝗿𝗮𝗱𝗶𝘁𝗶𝗼𝗻𝗮𝗹 𝗠𝗟 & 𝗗𝗲𝗲𝗽 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴
Enhanced experiment tracking, unified evaluation, and better checkpoint management benefit all model types—from scikit-learn classifiers to multi-modal foundation models.
Ready to get started? Upgrade with 𝚙𝚒𝚙 𝚒𝚗𝚜𝚝𝚊𝚕𝚕 -𝚄 𝚖𝚕𝚏𝚕𝚘𝚠 and experience the next era of unified, observable, and reliable AI development.
🔗 Learn more: https://t.co/RCkJkwUCdz
👀 View the release notes: https://t.co/tjnfTuWsyr
P.S. Our BRAND NEW https://t.co/4HVNb2Obbn website is now LIVE! Check it out today. 👏
#opensource #mlflow #ml #oss #linuxfoundation