Technical Program Manager at Databricks. Opinions are my own.

Redmond, WA
Joined April 2012
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.
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Chris Green retweeted
At the beginning of this year, big portions of @databricks started running on Genie. When finance started doing all their work with Genie, we noticed that they were combining Genie with a Live Cloud Spreadsheet called Row Zero. This combination is really powerful. We met the Row Zero team and were blown away. Today, we're excited to announce that we've agreed to acquire Row Zero. Stay tuned for an awesome experience of Genie + Row Zero: databricks.com/company/newsr…
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Chris Green retweeted
This is true. It wasn't actually possible before 2010 because datacenter networks would bottleneck. We used to design coupled storage/compute where you brought compute close to "big data". But research on "full bisection bandwidth" networks made it possible to essentially just talk from any machine to the storage system at full speed. The disaggregation started then! Databricks and Snowflake started soon after many others followed. Now "Put it on the object store" is the way to go.
"Put it on the object store" remains undefeated. - OLAP: Lakehouse - OLTP: Lakebase - Kafka: Warpstream - Vector Search: Turbopuffer - Git: Cursor Origin
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Chris Green retweeted
Today @databricks we're publishing a detailed analysis of techniques we used to drastically reduce our internal AI spend while aggressively growing adoption. Savings come from layering in several techniques, which combine to drive unit costs down as much as 90% in some scenarios. Tl;dr, the wins come from: 1. Shifting defaults to more efficient models, including OSS models such as GLM. Maximum intelligence models simply aren't needed for many coding tasks, and "good enough" models are quickly becoming very cheap. We shift traffic between models using Unity AI Gateway. Approximate savings: 50% or more. 2. Using smart routing to automate model selection. Routing can further squeeze efficiency by dynamically selecting the model or harness that can most efficiently execute a particular task. Our task-level routing leverages @omnigent_ai. Approximate savings: 30%. 3. Providing user visibility and adaptive budgeting. Every user can see how much they spend, and users receive hints on how to contain spend. Heavy spenders encounter progressive friction as they ratchet spend above certain levels. Approximate savings: 10%. 4. Managing context bloat by pruning tool call results and tuning harness settings. Extraneous context costs $$ and delivers no value. Tuning cache settings also help lower average token costs. Approximate savings: 10%.
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Replying to @databricks
@databricks has the fastest and lowest latency on Kimi K3 (max)! Great job by Databricks AI team! artificialanalysis.ai/models…
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The Databricks inference team is 🔥
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.
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Chris Green retweeted
Introducing Project Glasswing: an urgent initiative to help secure the world’s most critical software. It’s powered by our newest frontier model, Claude Mythos Preview, which can find software vulnerabilities better than all but the most skilled humans. anthropic.com/glasswing
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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-…
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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…
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Chris Green retweeted
Excited to raise $4B+ in our latest fundraise! We shared that we will primarily be using the capital to invest in: 1️⃣ Lakebase Postgres - serverless database for Agents 2️⃣ Agent Bricks - high quality agents that can reason on enterprise data 3️⃣ Databricks Apps - Data Intelligence Apps built on Lakebase and Agent Bricks As part of this we also disclosed: 🚀 Crossed $4.8B revenue run-rate, over 55% YoY growth 🚀 Crossed $1B of revenue run-rate for our Data Warehousing product 🚀 Crossed $1B of revenue run-rate for our AI products 🚀 Continued to be cash flow positive in the last 12 months Big thanks to Insight Partners, Fidelity Investments, and J.P. Morgan who led the round. databricks.com/company/newsr…
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OpenAI models will now be natively available on Databricks
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Big news: Databricks and @OpenAI are partnering to deliver powerful AI to the enterprise. OpenAI frontier models will now be available natively in Databricks. This means you can build, evaluate and scale production-grade AI apps and agents on your governed enterprise data, leveraging the latest OpenAI models like GPT-5. We’re excited to expand our relationship with OpenAI; Databricks was one of the first to host gpt-oss open models, they use Databricks products and now we’re offering OpenAI models natively on Databricks: databricks.com/blog/run-open…
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We’ve released a free edition of Databricks and opened up a lot of our training materials to help developers learn data engineering, data science and AI! Check it out to rapidly get started with the latest tools and with challenging use cases.
Databricks launches Free Edition! Explore and learn the latest data and AI technologies for free. Users can experiment with the full range of use cases – from building AI agents and applications to analyzing data with SQL – on the same platform used by millions of professionals. We’re proud to help today’s students and enthusiasts build real skills on real tools in today's competitive AI and ML job market.
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I am super excited to announce that we have agreed to acquire Neon, a developer-centric serverless Postgres company. The Neon team engineered a new database architecture that offers speed, elastic scaling, and branching and forking. The capabilities that make Neon great for developers are also great for AI agents. Together, we'll deliver an open, serverless database foundation for developers and AI agents. databricks.com/company/newsr…
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Chris Green retweeted
Your first look at what’s coming up for LlamaCon on April 29th! Mark will be sitting down with Microsoft Chairman and CEO @satyanadella to discuss the latest trends in AI for devs; and with @databricks Co-Founder and CEO, Ali Ghodsi on open source AI + advice for founders.
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Chris Green retweeted
Getting high-quality labeled data at scale is one of the biggest enterprise challenges with fine-tuning LLMs on specific tasks. TAO (Test-time Adaptive Optimization) is a new method that uses test-time compute and reinforcement learning to improve LLMs—no labeled data required. Requiring only example inputs, TAO often surpasses supervised fine-tuning with thousands of labeled data points and makes low-cost open source models outperform expensive proprietary ones. Learn more about this research from the Mosaic Research Team. dbricks.co/420Xte1
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Now GA on Google Cloud: Serverless Compute for notebooks, Workflows and DLT pipelines! Serverless compute provides practitioners with the speed, scalability, and simplicity needed to address challenges across today’s data engineering and analytics landscape: dbricks.co/3Q4Om6s
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Congratulations, @JoeBiden and @KamalaHarris. Our two countries are close friends, partners, and allies. We share a relationship that’s unique on the world stage. I’m really looking forward to working together and building on that with you both.
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Today, we joined #healthcare workers in #seattle to raise awareness of #racialjustice and to show support for the Black Lives Matter movement. #doctorsforjustice 👊🏻👊🏼👊🏽👊🏾👊🏿
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Are you tuning in? Places, Please is now underway! Watch here: youtube.com/watch?v=mBzxfj_W… Scroll down on the page to view the star-studded line-up for the next 24 hours. And if you're able, here's where to make a donation to @AFC_helps: j.mp/3ahL0GE #SheridanProud
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