A data warehouse is a repository that consolidates structured data from operational systems such as sales, billing, and web analytics into a consistent schema designed for analysis rather than day-to-day transactions.
Why it matters
Running heavy reports against a live production database can slow your application. A warehouse separates the two, keeps history, and gives analysts one trusted source. Data usually arrives through ETL or ELT jobs and feeds dashboards.
Example
A common layout is a star schema: one fact table of events surrounded by dimension tables.
fact_orders(order_id, customer_id, date_id, amount)
dim_customer(customer_id, country, segment)
dim_date(date_id, month, quarter, year)Queries then join and aggregate across them quickly, since warehouses typically use columnar storage.
Common mistakes
- Using the warehouse as a live application database.
- Loading data without documenting definitions, so teams compute the same metric differently.
- Confusing it with a data lake, which holds raw, unmodeled data.
Related
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