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Databricks

Category: AI Infrastructure

A unified Data and AI platform built on the Lakehouse architecture that integrates data engineering, data science, machine learning, and analytics into a single collaborative environment. Databricks was founded in 2013. The company is led by Ali Ghodsi. Based in San Francisco, United States. Team size: 12,000+. Total funding raised: $9.0B+. Latest round: Series J ($5.0B, Feb 2026). Key investors include ["Andreessen Horowitz","Thrive Capital","MGX","NVIDIA","Microsoft","Amazon","CapitalG","Morgan Stanley","T. Rowe Price","NEA"].

Founded
2013
Headquarters
San Francisco, United States
Team size
12,000+
Total funding
$9.0B+

Value proposition

Eliminates data silos by combining the performance and governance of a data warehouse with the flexibility and low cost of a data lake, enabling 'Data Intelligence' through integrated generative AI.

Products and solutions

["Databricks SQL (Serverless data warehousing)","Delta Lake (Open-source storage layer for reliability)","Unity Catalog (Unified governance for data and AI)","Mosaic AI (End-to-end platform for building and deploying LLMs)","MLflow (Machine learning lifecycle management)","Photon (High-performance vectorized query engine)","Delta Live Tables (ETL framework)"]

Unique value

Pioneered the 'Lakehouse' architecture, which effectively merged the previously separate worlds of data lakes and data warehouses into a single platform.

Target customer

Fortune 500 enterprises, data engineering teams, data scientists, ML engineers, and business intelligence analysts.

Industries served

["Financial Services","Healthcare & Life Sciences","Retail & Consumer Goods","Manufacturing & Energy","Media & Entertainment","Public Sector"]

Technology advantage

Built by the original creators of Apache Spark; features a proprietary high-performance engine (Photon) and deep integration with generative AI via the Mosaic AI acquisition, allowing companies to train custom models on private data.

How they differentiate

Pioneered the 'Lakehouse' architecture which unifies data lakes and data warehouses. Unlike competitors that often require separate systems for BI and AI, Databricks provides a single platform for data engineering, machine learning, and analytics, heavily integrated with open-source standards like Apache Spark and Delta Lake.

Main competitors

["Snowflake","Google BigQuery","Amazon Redshift","Microsoft Azure Synapse/Fabric"]

Key partnerships

["Microsoft (Azure Databricks is a first-party service on Azure)","Amazon Web Services (AWS)","Google Cloud Platform (GCP)","NVIDIA (Optimizing AI training and inference on Databricks)","Accenture & Deloitte (Global strategic implementation partners)"]

Notable customers

["Shell","Comcast","Condé Nast","Regeneron","H&M","AT&T","Walgreens"]

Major milestones

["Pioneered the 'Lakehouse' category in 2020","Acquired MosaicML for $1.3B in 2023 to bolster Generative AI capabilities","Reached $1B+ revenue run rate in 2022","Completed a massive $5B equity and debt round in Feb 2026 at a $134B valuation"]

Growth metrics

Achieved a $5.4 billion annual revenue run rate as of February 2026; valuation reached $134 billion.

Market positioning

Dominant leader in the Data Intelligence and AI Infrastructure market for large-scale enterprises.

Geographic focus

Global (Headquartered in North America with significant operations in EMEA, Asia-Pacific, and Latin America).

Patents and IP

Holds numerous patents in distributed systems, optimized data storage formats (Delta), and automated query optimization for cloud-native environments.

About Ali Ghodsi

Ali Ghodsi is the Co-founder and CEO of Databricks and a central figure in the modern data ecosystem. He is one of the original creators of Apache Spark, Delta Lake, and MLflow. Before becoming CEO in 2016, he served as the company's VP of Engineering and Product Management. He is an Adjunct Professor at UC Berkeley and was a key researcher at the AMPLab, where he co-authored the seminal papers on Mesos and Spark. Under his leadership, Databricks pioneered the 'Lakehouse' architecture, merging data warehouses and data lakes.

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