Databricks Intelligence Platform

A unified lakehouse platform that supports data engineering, analytics, SQL, machine learning, and AI on a single copy of data stored in open formats, with integrated governance via Unity Catalog.

Data engineering/ETL, BI and SQL analytics, machine learning, and generative AI all run against the same governed data, avoiding separate siloed systems for each.

An architecture that combines the low-cost, open storage of a data lake with the ACID transactions, governance, and performance of a data warehouse, eliminating the need for two separate tiers.

A setup where raw data lands in a data lake, then a subset is copied/ETL'd into a separate data warehouse for BI. This duplicates data, adds latency, and creates governance gaps.

Traditional lakes lack ACID transactions, suffer schema drift/no schema enforcement, and have poor query performance and reliability. The lakehouse adds transactions, schema enforcement, and performance via Delta Lake.

Warehouses use closed proprietary formats, are expensive to scale, and poorly support unstructured data and machine learning. The lakehouse uses open formats, separates storage/compute, and serves ML directly.