Databricks provides a unified Data Intelligence Platform that combines data lake and warehouse capabilities into a single, scalable environment for data storage, governance, ETL, analytics, and AI model development. The platform offers serverless PostgreSQL lakebases, integrated open‑source tools like Spark, Delta Lake, and MLflow, and multi‑cloud support to reduce operational complexity and total cost of ownership for enterprise data and AI teams.
Funding
$1B raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
+20Founders
Product
Problem
Enterprises often manage separate systems for data warehousing, engineering, analytics, and AI, leading to data silos, high operational costs, and complex pipelines that hinder rapid insight generation.
Solution
Databricks offers a unified Data Intelligence Platform built on an open lakehouse architecture that combines the capabilities of data lakes and warehouses. The platform provides a single, scalable environment for data storage, governance, batch and streaming ETL, analytics, and AI model development. Serverless PostgreSQL lakebases enable transactional workloads directly on the lakehouse, while integrated tools such as Lakeflow, Delta Lake, and MLflow support reliable pipeline orchestration and model lifecycle management. Open standards and native cloud support keep data under customer control and simplify sharing via Delta Sharing. The platform’s performance optimizations deliver low total cost of ownership and enable production‑ready AI agents and applications to be built and deployed at scale.
Target Audience
Primary customers are data, analytics, and AI teams within large enterprises and Fortune 500 companies that need a single platform to consolidate data engineering, warehousing, and AI workloads.
Features
- Serverless PostgreSQL lakebases (Lakebase) that provide a transactional layer for applications and AI agents
- Unified environment for data engineering, warehousing, analytics, and AI using open source projects Apache Spark™, Delta Lake, MLflow, and Unity Catalog
- Lakeflow designer for building reliable batch and streaming ETL pipelines with end‑to‑end lineage tracking
- Built‑in data governance and cataloging that offers fine‑grained access control and data provenance across all workloads
- Automatic performance and storage optimization delivering world‑record low TCO for both SQL analytics and large language model training
- Multi‑cloud support with seamless integration into AWS, Azure, and Google Cloud, plus open APIs for data sharing via Delta Sharing