Databricks

Yuchen Jin

@Yuchenj_UW

Super cool work from Databricks AI research team. Data agents are harder than coding agents. Coding agents have verifiable tests. Data agents have to find “truth” across millions of tables, docs, dashboards. Databricks Genie got to 91.6% accuracy, while the leading coding agent only got 32% on enterprise data analysis tasks. Specialized knowledge search + Parallel Thinking + Multi-LLM is the key. Databricks has an amazing research team, and I've been enjoying working with them!

Tomasz Tunguz

@ttunguz

Databricks started later. It built a more complex architecture. It focused on unstructured data; images, documents, logs, audio. Though vast within the enterprise, this data had historically produced little insight. Too hard to process. Too messy to query. Too expensive to store in formats that mattered. Snowflake took the opposite bet. Structured data. Clean tables. SQL queries that ran fast & returned answers executives could read. The market agreed. Snowflake went public at a $70 billion valuation. Databricks raised private rounds at half that. Then AI arrived. Suddenly the data that was too messy to query became the data that models needed to train. Unstructured data wasn’t a liability. It was the asset. Databricks has overtaken Snowflake in revenue. Two years ago, Snowflake led by $220 million per quarter. Today, Databricks leads by $120 million. Databricks’ growth rate is accelerating at scale, from 50% to 55% to 65% year over year. Growth rates don’t accelerate at $5 billion in revenue. The crossover happened because AI is an architectural transition, not a feature addition. Most enterprise data never made it into Snowflake. It sat in object storage, unstructured, waiting. Databricks built tools to use it there. No migration required.

Jamin Ball

@jaminball

Awesome job by the @databricks team My summary: They trained a model called KARL that beats Claude 4.6 and GPT 5.2 on enterprise knowledge tasks (searching docs, cross-referencing info, answering questions over internal data), at ~33% lower cost and ~47% lower latency. The key insight: instead of throwing expensive frontier models at enterprise search, you can use reinforcement learning on synthetic data to train a smaller model that's faster, cheaper, AND better at the specific task. RL went beyond making the model more accurate. I t learned to search more efficiently (fewer wasted queries, better knowing when to stop searching and commit to an answer). They're opening this RL pipeline to Databricks customers so they can build their own custom RL-optimized agents for high-volume workloads. I think we'll continue to see data platforms become agent platforms. Databricks' KARL paper is really an agent platform play. The pitch: you already store your enterprise data in the Lakehouse, now Databricks will train a custom RL agent that searches and reasons over it, tuned specifically for your highest-volume workloads (workloads = apps = agents). The business move is closing the loop: data storage → retrieval → custom agent training → serving, all on Databricks. They're turning "your data lives here" into "your agents live here too." Kudos @alighodsi @matei_zaharia @rxin

Amjad Masad

@amasad

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. https://t.co/O33uJHohgo

Shay Boloor

@StockSavvyShay

$NOK is expanding its autonomous network push with $AMZN AWS and Databricks. Nokia is packaging agentic AI, digital twins and closed-loop operations into a platform it says has already helped operators reach 90%+ automation and cut network slice rollout times by up to 85%. https://t.co/JOIliQKCmf

vas

@vasuman

Imagine joining Databricks in 2017 at their Series D thinking oh boy the IPO is just around the corner and then 9 years later you raise a Series M

Ali Ghodsi

Co-founder & CEO

CEO of Databricks; UC Berkeley adjunct professor and Apache Spark contributor.

Matei Zaharia

Co-founder & CTO

Creator of Apache Spark; CTO of Databricks and Stanford professor.

Ion Stoica

Co-founder & Executive Chairman

UC Berkeley professor; co-founder of Anyscale and Conviva.

Reynold Xin

Co-founder & Chief Architect

Lead developer of Apache Spark; chief architect at Databricks.

Patrick Wendell

Co-founder & VP Engineering

Apache Spark committer; VP of engineering at Databricks.

Andy Konwinski

Co-founder

Co-founder of Databricks and later co-founder of Perplexity.

Arsalan Tavakoli-Shiraji

Co-founder & SVP Field Engineering

Co-founder; leads field engineering at Databricks.

Dave (David) Conte

Chief Financial Officer

Longtime CFO overseeing Databricks’ global finance, capital raising and investor relations.

Hatim Shafique

Chief Operating Officer

Runs company‑wide operations and execution as chief operating officer.

Fermín Serna

Chief Security Officer

Leads enterprise security strategy and programs as chief security officer.

Previously at Google · New York, New York, United States

Joined Jun 2026

Vivek Srikantan

Director

Previously at Yubi · Bengaluru, Karnataka, India

Joined Jun 2026

John Lyle

Engineering Director

Previously at Unobravo · London, England, United Kingdom

Joined May 2026

Samuel Dovey

Client Director

Previously at Neptune Software · London, England, United Kingdom

Joined May 2026