Databricks

Databricks is a data and AI platform company that has become essential enterprise infrastructure for the AI era. Founded by the creators of Apache Spark at UC Berkeley, Databricks provides the "lakehouse" architecture — unifying data warehousing and data lakes — along with the MLOps tooling that enterprises use to build, train, and deploy AI systems at scale. Valued at over $60 billion, Databricks is the most valuable private data infrastructure company in the world.

The Lakehouse Architecture

Databricks' core innovation is the lakehouse: a unified data platform that combines the flexibility of data lakes with the performance and governance of data warehouses. Built on open formats (Delta Lake, Apache Parquet), the lakehouse approach enables organizations to store all their data — structured, semi-structured, and unstructured — in a single system optimized for both analytics and AI training. This is infrastructure-level composability: modular data components that can be composed into AI pipelines.

AI and Machine Learning Platform

Databricks' Mosaic AI platform provides the full ML lifecycle: data preparation, model training (including custom LLM fine-tuning), experiment tracking, model serving, and monitoring. The company's acquisition of MosaicML brought foundation model training expertise, and its DBRX open-source model demonstrated that efficient training infrastructure can produce competitive models at lower cost.

Enterprise AI Infrastructure

As agentic AI moves into enterprise deployments, Databricks is positioning itself as the data substrate that agents operate on. Agents need access to structured enterprise data — customer records, financial data, operational metrics — and Databricks' governance, security, and query optimization features make it a natural backend for enterprise agent systems.