Explore the primary data warehouse types — EDW, data marts, ODS, cloud, hybrid, and lakehouse — and learn which architecture fits your analytics and AI goals.
by Databricks Staff
A data warehouse is a centralized repository that collects, organizes, and stores structured data from across an organization so that analysts and data scientists can run complex queries, generate reports, and support business intelligence (BI) workloads. Unlike operational databases designed for transaction processing, a data warehouse is built for analytical workloads — joining data from multiple sources, preserving historical data across years, and delivering the governed foundation that strategic decision-making requires.
Understanding the different data warehouse types is essential before committing to any platform or migration. Each type reflects a distinct architectural tradeoff between scale, latency, cost, and subject scope. This guide covers every major type of data warehouse — from traditional Enterprise Data Warehouses to modern lakehouse architectures — and provides clear guidance on when each is the right choice.
The field recognizes three core data warehouse types that form the foundation of modern data architecture: the Enterprise Data Warehouse (EDW), the Data Mart, and the Operational Data Store (ODS). Beyond these, organizations also deploy cloud-based data warehouses, virtual data warehouses, hybrid data warehouses, and lakehouse platforms depending on workload requirements, data volume, and governance complexity.
