Data Warehouse
A database optimized for analytics — big aggregate queries over historical data — separate from the transactional systems.
A data warehouse is a database built for analytics: it stores large volumes of historical, structured data and is optimized for big aggregate queries (sums, group-bys over billions of rows) rather than the small fast transactions of an operational database. Examples: Snowflake, BigQuery, Redshift.
Worked example: nightly ETL jobs load cleaned data from many source systems into the warehouse, where analysts run “revenue by region by month” queries that would cripple the production OLTP database. Gotcha: a warehouse is for reads and analysis, not live app traffic — data is usually minutes to hours stale, and it is columnar and denormalized for scan speed, so treating it like a transactional store (frequent row updates) works against its design.