Matei Zaharia
AI LeaderCo-Founder & CTO @ Databricks
San Francisco, CA
Co-founder and CTO of Databricks and professor at UC Berkeley. Created Apache Spark and MLflow, two of the most influential open-source projects in data engineering and MLOps. MLflow has millions of downloads.
Tools & expertise
Latest content
Auto-updated feedServerless Workspaces in Azure Databricks is now Generally Available
We’re excited to announce that serverless workspaces are now Generally Available on Azure, ...
AI Governance Is the Strategy: Why Successful AI Initiatives Begins with Control, Not Code
AI is becoming embedded in workflows, customer interactions, and enterprise decision-making...
The Evolution of Data Engineering: How Serverless Compute is Transforming Notebooks, Lakeflow Jobs, and Spark Declarative Pipelines
Data engineering has reached an inflection point. As organizations increasingly rely...
10 Data Warehouse Migration Myths Blocking AI-readiness (and Your Blueprint for Seamless Modernization)
Moving to a modern data warehouse is a critical part of any enterprise AI-readiness strategy...
How Ontologies Help Nuclear Scale to Meet Global Energy Demand
Nuclear reactors are among the most complex engineered systems we operate at scale. Safe...
Mitigating The Risk of Prompt Injection for AI Agents on Databricks
Overview:Since we released the Databricks AI Security Framework (DASF) in 2024, the...
Introducing Genie Code
We are excited to announce Genie Code, the newest addition to the Databricks Genie...
Your Databricks Guide to HIMSS26
We’re in full swing at HIMSS. Returning to Las Vegas once again, we’re incredibly...
Databricks acquires Quotient AI to power AI agent evaluations
Databricks is excited to announce the acquisition of Quotient AI, an innovator in...
Beyond Provisioning: The Developer’s Guide to Databricks Lakebase Autoscaling
You only have 2 choices when provisioning databases:Over-provision: set your compute...
Content & resources
Notable quotes
"MLflow was created to address the chaos of ML development. Every team was reinventing the wheel for experiment tracking, model packaging, and deployment. We needed an open standard."
— Databricks Blog