What is a data warehouse? A full guide

A data warehouse is used for analytics, reporting, and business intelligence. It helps organizations analyze historical and aggregated data across multiple systems.

A database is optimized for transactional workloads, while a data warehouse is optimized for analytical queries. Databases support day-to-day operations. Data warehouses support analysis and decision-making.

OLTP systems handle real-time transactions such as inserts and updates. OLAP systems, including data warehouses, are designed for complex analytical queries over large volumes of historical data.

You should use a data warehouse when analytics and reporting begin to impact operational performance or when you need consistent metrics across multiple data sources.

MongoDB can support basic analytics when workloads are small, but it is optimized for transactional access patterns, such as frequent writes, low-latency reads, and evolving schemas. As analytics grow more complex and data volumes increase, scan-heavy and historical queries are better handled by a data warehouse, allowing analytics to scale independently while MongoDB remains the operational system of record.

Yes. Data warehouses are inherently expensive to build and operate because they require storing large volumes of historical data, running compute-intensive analytical queries, and managing inspection, transformation, and governance pipelines. When used for analytics and BI, the tooling, infrastructure, and ongoing operational effort represent a meaningful and persistent cost.