A data warehouse is a database built for analysis rather than for running the business. Bill Inmon, who popularized the idea around 1990, described it as a subject-oriented, integrated, time-variant and non-volatile collection of data that supports decisions: organized around subjects such as customers and sales rather than applications, reconciled across sources, kept with its history, and appended to rather than edited. Ralph Kimball built warehouses differently, from dimensional data marts that answer one business process each (Dimensional and Data Vault), and the two schools still shape how warehouses are modeled (Slowly Changing Dimensions).
Three properties define a warehouse in practice. It enforces schema-on-write: data is typed, cleaned and conformed before analysts see it. It stores tables in columns for fast scans (Column Stores Under the Hood), and it speaks SQL. The cloud warehouses that replaced on-premises appliances in the 2010s (Snowflake, BigQuery 1 , Redshift 24 , Synapse and Fabric) added one more: storage and compute are separate, so you can store years of history cheaply and pay for processing only while queries run (Cloud Data Warehouses).
The weaknesses show at the edges. Raw logs, images and machine-learning training sets fit poorly, and the data sits in the vendor's format, reachable only through the vendor's engine. For BookNest's curated sales figures a warehouse is still the right home; Analytical SQL and Data Warehouses builds one in PostgreSQL 1,289 and DuckDB 61,228 .