Company Intelligence
Databricks
Databricks pioneered the Data Lakehouse architecture and enterprise AI platform, unifying data engineering, Apache Spark computing, and custom foundation model development via Mosaic AI.
- Country
- United States
- Website
- www.databricks.com
- Founded
- 2013
- Products
- 1
- Reports
- 4
- Last reviewed
- Aug 2026
Overview
What this company is, what it builds, and who it serves.
Databricks is a global data intelligence and machine learning software company founded in San Francisco in 2013. Established by the academic creators of Apache Spark at UC Berkeley—including Chief Executive Officer Ali Ghodsi and Chief Technology Officer Matei Zaharia—Databricks pioneered the Lakehouse architecture that unifies enterprise data and artificial intelligence.
Who it serves
Chief Data Officers, enterprise data engineers, MLOps practitioners, and Fortune 500 analytics teams building production AI systems.
What it builds
Databricks Data Intelligence Platform, Delta Lake, Mosaic AI (pretraining and fine-tuning), MLflow, Unity Catalog, and Databricks SQL.
Why It Matters
Databricks pioneered the Data Lakehouse architecture and enterprise AI platform, unifying data engineering, Apache Spark computing, and custom foundation model development via Mosaic AI.
Funding
Verified funding disclosures on file for this company.
- December 2025–February 2026: Completes a Series L funding round exceeding $5 billion in equity plus $2 billion in new debt capacity, at a $134 billion valuation.
- June 2026: Reported to be in talks for a new funding round that could value the company between $165 billion and $175 billion, ahead of a potential 2027 IPO.
Products
Products and platforms connected to this company.
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The Databricks Data Intelligence Platform
Explore The Databricks Data Intelligence Platform by Databricks. Verified AI product intelligence covering core capabilities, Enterprise Software use…
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Founders
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Ecosystem
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Company
Databricks
Industry
Technologies
Capabilities
- Unified Data Lakehouse combining data lakes and relational warehouses
- Mosaic AI full-stack foundation model pretraining and fine-tuning
- Open-source governance and lineage tracking via Unity Catalog
- End-to-end MLOps lifecycle management with MLflow
Features
- Mosaic AI Model Serving with low-latency GPU provisioned throughput
- Databricks Assistant for generative SQL and Python coding
- Delta Lake open format with ACID transactions and schema enforcement
- Vector Search and Compound AI system evaluation frameworks
Country
Official Resources
Official and developer links on file for this company.
Latest Developments
Verified developments and timeline notes on file.
- CEO Ali Ghodsi repeatedly states that 2026 is "a terrible year to go public," citing a crowded field of mega-IPOs, while confirming the company will eventually list. Latest
- December 2025–February 2026: Completes a Series L funding round exceeding $5 billion in equity plus $2 billion in new debt capacity, at a $134 billion valuation.
- February 2026: Reports annualized revenue exceeding $5.4 billion, up 65% year over year.
- June 16, 2026: Reports annualized revenue of $6.9 billion at its Data and AI Summit, up more than 80% year over year, with $1.7 billion of that from AI products.
- June 2026: Reported to be in talks for a new funding round that could value the company between $165 billion and $175 billion, ahead of a potential 2027 IPO.
Frequently Asked Questions
What is the Databricks Data Intelligence Platform?
The Databricks Data Intelligence Platform combines the storage scale of data lakes with the reliability and governance of data warehouses, allowing companies to build analytics, BI, and generative AI models on a unified foundation.
Who founded Databricks?
Databricks was founded in 2013 by the creators of Apache Spark at UC Berkeley: Ali Ghodsi (CEO), Matei Zaharia (CTO), Reynold Xin, Patrick Wendell, Andy Konwinski, Arsalan Tavakoli-Shiraji, and Ion Stoica.
What is Mosaic AI in Databricks?
Mosaic AI is Databricks' generative AI suite that enables enterprise teams to pretrain, fine-tune, evaluate, and serve proprietary foundation models directly on their internal corporate lakehouse data.
Sources & Verification
How this profile is verified and where the information comes from.
Primary sources
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