Databricks is a cloud-based data and AI platform built around the “lakehouse” model. It combines data lakes and data warehouses into one system so teams can work on analytics, BI, and machine learning from the same data.
It’s best known as the commercial creator of Apache Spark and Delta Lake.
Modern data stacks are fragmented. One tool for storage. Another for analytics. Another for ML. Databricks removes that split.
You get one platform for ingesting data, transforming it, querying it, training models, and deploying them. That reduces data duplication, lowers infra overhead, and speeds up experimentation. Data engineers, analysts, and ML teams can finally work on the same foundation.
It’s especially valuable for companies doing large-scale analytics or production machine learning.
Databricks runs on top of major cloud providers (AWS, Azure, GCP). Data is stored in open formats (like Delta Lake) and processed using scalable Spark clusters.
