Reynold Xin

I find the Lakebase design for serverless Postgres very elegant, so I spent some time explaining how it works in this blog. The blog starts by explaining how databases really persist data (with a write-ahead-log and data files that are updated async), and how Lakebase separates storage and compute by externalizing those two components. It ends with how the Lakebase architecture naturally leads to LTAP, enabling OLTP and analytical workloads against a single governed copy of data. My goal was to make it readable by anyone curious about how these systems work, not just database and storage experts. That turned out to be a lot more challenging than I first thought. Database storage is one of the most complex areas in computer science (the ARIES paper cited in blog was the hardest paper I personally ever had to read). The first draft had too little detail and I couldn't land the ideas. The second had too much and I'd lost anyone who isn't already a storage expert. This is the third draft, and I'd love feedback on whether the depth feels right. databricks.com/blog/lakebase-…

Gartner’s Magic Quadrant for Analytics and Business Intelligence (BI) is out, and Databricks was named a Visionary in our first appearance, the highest debut for any vendor in this MQ’s 20+ year history. BI has already changed. Anyone can drill from a signal down to the truth behind it and agentic loops make sure every answer has the full story. That’s where we're ahead with Genie and AI/BI. I use it every day to see the key signals, understand what changed and why, and decide what to do about it. Huge congratulations to the teams, and thank you to our customers.

We benchmarked coding agents on our own internal tasks at Databricks and learned a lot! There are many surprising opportunities to lower cost and increase quality, and many models including open source ones are truly competitive now. 🧵

Happy 30th birthday Postgres! 🎉 What an accomplishment, almost nothing in tech has stayed relevant for 30 years. From humble beginnings as a Berkeley research project to becoming the default for everything from side projects to massive production systems. 30 years later, the database and the community have more momentum than ever. It's why we built Neon on Postgres, serverless, but 100% the Postgres you love. 🐘

My co-founder @rxin personally wrote this really good paper that explains the main idea behind postgres Lakebase as well as LTAP. It almost serves as a primer on how transactional databases are built and how Lakebase and LTAP work. Maybe more importantly, what are the tradeoffs, and what are you giving up by adopting this new approach. Highly recommended reading: databricks.com/blog/lakebase-…

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.

We're proud to be named a Leader in the 2026 Gartner® Magic Quadrant™: AI Platforms for Data Science and Machine Learning! Databricks is positioned highest in Ability to Execute and furthest in Completeness of Vision for the second year in a row. If you're evaluating AI platforms, this is the report to start with. Get the report: databricks.com/resources/anal…

I liked how @databricks laid out the 5 fiefdoms of the "Data Realm" 1. OLTP Databases 2. Data Engineering 3. Data Science 4. Data Warehouse 5. Real Time Analytics Technically there was a 6 "catch all" bucket called niche databases

@tasso 31000! (And 70000 more online).

I won’t lie, presenting in front of 25,000 amazing data & AI professionals is quite the experience. Amazing audience and reception for CustomerLake!

current vibe #DataAISummit

After months of intense building, I am super excited to launch CustomerLake today! CustomerLake is Databricks' answer to how Customer Data & Marketing will evolve in the agentic world. AdWeek sat down with @alighodsi and msyelf to dig into the details. Check it out below!

Really excited to open source a new project: Omnigent, a meta-harness for AI agents. It lets you build multi-agent coding and custom agents, sitting above Claude Code, Codex, Pi, and agent SDKs to let you compose them. It also adds live collaboration and rich control policies.

Big news: Neon is expanding to offer a more complete set of backend primitives for running apps and agents: ✅ Database ✅ Authentication 🔜 Storage 🔜 Compute 🔜 AI Gateway

The future of databases is being built directly on top of object stores. We call this the Lakebase architecture. For a long time, the industry treated data lakes strictly as analytical or offline storage. But the Lakebase architecture is changing that by enabling true operational databases directly on top of the lake. I believe this is the future of data infrastructure. It is how every database, whether it's an OLTP system or a vector database, should be built moving forward. Of course, delivering the stringent performance requirements for operational databases on top of object stores require some creative engineering. Really excited to see more real-world examples of this architecture emerging. The team at Zilliz just shared a piece on why they rebuilt their vector database using this exact approach, and it perfectly captures where the industry is heading. Check it out here: zilliz.com/blog/why-we-bu…

1️⃣ dev.to/rick_houlihan_… 2️⃣ blogs.oracle.com/database/oracl…

Oracle has spent the last two weeks writing articles comparing Oracle (and PDB) to Lakebase, and it highlights a massive philosophical divide in how we view databases in the agentic era. They are trying to retrofit heavy, traditional architectures for AI. We believe Lakebase are the future because agents need something entirely different: ⚡️ Super simple APIs: so agents don't have to read a giant manual and hallucinate a query. ⚡️ Sub-second provisioning & auto-scaling: so you aren't paying legacy-level prices for idle time. ⚡️ Branching: Git-style branching to create isolated, safe environments for agents on the fly. ⚡️ Automatic backup & restore: so you don't sweat it when an autonomous agent inevitably drops a table. The numbers speak for themselves. Lakebase is our fastest growing product. In the last few months alone, we've seen database start rate 30X, and now we are starting tens of millions of databases EVERY DAY. Some of these databases have 500 level deep branches and lifetime of just seconds due to how fast agents move. Go try it yourself in a few seconds on neon.com! The team has been cooking hard to push this gap even further. Come to Data and AI Summit next month to hear about some major new breakthrough capabilities. 🚀 (Links next so you can read their take)

Life update: I joined Databricks this week! I thought I’d do another startup after Hyperbolic, but I was surprised by how startup-y Databricks AI is. @alighodsi, @pwendell, @matei_zaharia are in full founder mode. They’re the best founders I’ve met. I like working with people who aren’t “normal” and they definitely aren’t. For example, they invited me to an all-hands before I joined. I’m also impressed by how many former founders are here. @akhilgupta and @hanlintang are incredible leaders. A big bonus: I finally have unlimited Claude Code & Codex tokens! AI adoption on the Databricks AI team is insanely high. Every engineer I’ve met uses AI heavily and shares their own ways to drive agents. Many talented people here. I’m super pumped for this new journey!

Replit now deploys directly to Databricks. Your apps run inside your Databricks environment while inheriting its security, governance, and data access. Beta is live. Enterprises are already building with it and seeing massive acceleration in BI and internal tools.

@JeffDean says it best, the problem in this new agentic era is "tools designed for human speed interaction". That's why we think agents love 𝗟𝗮𝗸𝗲𝗯𝗮𝘀𝗲 𝗣𝗼𝘀𝘁𝗴𝗿𝗲𝘀, it can branch, snapshot, scale up and down in a second, orders of magnitude faster than other databases. youtube.com/watch?v=joTYgv… Read about this architectural shift from Database to Lakebase: databricks.com/blog/what-is-a…