Franco Patano retweeted
Can a single database architecture satisfy both vibe-coded and large scale enterprise apps?
The same approach makes small database ultra cheap to run and branch can also make a large one faster and easier to operate. We call this architecture Lakebase.
Pro tip: if you have Genie One desktop app installed in your Mac, Option + Spacebar will pull up spotlight light bar to start your next thread.
People often ask how Databricks does things internally. I decided to start a new thing, “Tales from inside Databricks”. Check it out and let me know what you think!
youtu.be/N_2DbeIwtiU?si=wqVn…
If you use automatic notes transcription like Gemini notes, I dare you to turn on Closed Captioning and in real time see why your AI gets confused.
What are your funniest mistranslations from AI?
We used to spend years learning and practicing how to write well, built spell checkers and punctuation checkers, then when finally built the ultimate writing checker, we reside to adding little mistakes so people dont think its AI slop.
What other oddities have you discovered?
Franco Patano retweeted
What does it take to run data behind products used by one billion people every week?
For @OpenAI, Databricks serves as a governed, multicloud foundation across Azure and AWS, powering analytics, marketing, finance, trust and safety, and critical security operations—from threat detection to incident response across high-volume infrastructure.
Beyond using Databricks as its underlying data platform, OpenAI is also a strategic partner. Through the partnership, business users already using ChatGPT Work can use Genie MCP to talk to enterprise data.
Explore how OpenAI runs data at scale on Databricks: databricks.com/customers/ope…
Franco Patano retweeted
An extremely important functionality for agents is to simply extract fields out of PDFs. This turns out to be harder than people think because LLMs are primarily trained on predicting the next tokens. This leads them to "autocorrect" things that they shouldn't autocorrect. We launched an AI Extract capability that just excels at doing just this task with very high accuracy (95% vs 87% for others) and extremely low cost. Check out this blog on how we did it. The function can of course be called directly from SQL and be used throughout the platform.
databricks.com/blog/databric…
Franco Patano retweeted
I got this question so many times today. "How can you grow 80% at $7B?"
The true answer is that we're finally seeing a breakthrough with AI agents starting to work in the enterprise.
The AIs have been super smart for a while, but have lacked basic context that's in people's heads, or in some SaaS system-or-record. A lot of organizations are deploying FDEs to capture this context, or Ontology, and feed it to the AI. This is labor intensive and expensive. We just automated that with Genie Ontology.
Once you have that enterprise context graph, an AI agent like Genie becomes magical. I find myself no longer waiting for answers from my CRO, CFO, CMO, CHRO etc, I just keep queuing up questions on the phone while sitting in meetings. It'd frankly addictive.
Our customers are starting to do the same, over 70% of all queries on the platform are now generated by Genie agents. This fuels more questions to the platform, which drives consumption, which drives revenue. That's the simple answer.
Franco Patano retweeted
Replying to @pwendell @databricks
Full post here:
databricks.com/blog/managing…
Franco Patano retweeted
Kimi K3 at 239 tokens/s!
Databricks is now #1 for Kimi K3 inference speed and latency on Artificial Analysis.
A huge 2.8T parameters model, it’s the largest oss model we’ve ever served. We make sure the GPUs go brrr at Databricks.
Franco Patano retweeted
Replying to @databricks
@databricks has the fastest and lowest latency on Kimi K3 (max)! Great job by Databricks AI team!
artificialanalysis.ai/models…
Franco Patano retweeted
You may have heard that GLM-5.2 at 328 token/s is cool,
How about 392?
Databricks is now #1 in inference speed for GLM-5.2 on Artificial Analysis. It's a great model, and we did a lot of optimizations.
Franco Patano retweeted
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:
databricks.com/blog/next-gen…
Franco Patano retweeted
It's not often the co-founders and I author blog posts together, but we thought this one is important so we took time to write this together with the Neon team. In this blog we outline why we think a new type of database will slowly take over and replace all existing databases, we call it the Lakebase. We cover the WHY and the WHAT here:
𝐀 𝐍𝐞𝐰 𝐄𝐫𝐚 𝐨𝐟 𝐃𝐚𝐭𝐚𝐛𝐚𝐬𝐞𝐬: 𝐋𝐚𝐤𝐞𝐛𝐚𝐬𝐞
databricks.com/blog/what-is-…
Franco Patano retweeted
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%
databricks.com/company/newsr…
Franco Patano retweeted
“Levenshtein distance vs. AI-Powered Similarity in Databricks SQL” by Parviz Deyhim pdeyhim.medium.com/levenshte…
Franco Patano retweeted
“Levenshtein Distance and WHERE wildcard in Databricks SQL” by Parviz Deyhim pdeyhim.medium.com/levenshte…
Franco Patano retweeted
Catch @Databricks co-founder & CEO @alighodsi in conversation with @danprimack at the #AxiosAISummit SF.
Tune in tomorrow at 4:00pm PT: trib.al/JfCmIPi


















