A data warehouse (DWH) is a centralized and integrated data storage system, organized for business analysis and queries. Unlike an operational system, which stores transactional data, a data warehouse is optimized for reading, aggregation and complex queries.
How it works
Data comes from various sources (transactional systems, APIs, spreadsheets) and is extracted, transformed and loaded (ETL) into the data warehouse. The DWH stores data in dimensional tables, optimized for aggregation, analytical queries and reporting. Data is normalized and fits into a data architecture that organizes information by domain.
Practical example
An online store has: order systems, payments, product catalog, website traffic and support data. The data warehouse consolidates this data into a unified view, enabling analysis of: sales by product, by period, by channel, shipping, returns, customer satisfaction, using consistent metrics.
What it's for
A data warehouse is essential for: business reporting, data analysis, sales forecasting, market intelligence, campaign optimization, support and predictive modeling. It allows different departments to query the same reliable data.
Related terms
- data-lake: repository of raw data
- ETL: extract, transform, load
- BI: business intelligence, analysis process
How to test today
Run a select query on a public data warehouse, such as the example BigQuery, or use Google Analytics itself to export data via API. See how a simple query can provide an aggregated view of data.
Frequently asked questions
- What is a data warehouse? It is a storage and integration system optimized for business analysis and queries.
- Difference between data lake and data warehouse? A data lake stores raw data in various formats; a data warehouse contains structured and normalized data for querying.
- How to choose a data warehouse? Consider data volume, variety, query speed, budget, maintenance team and integration with other tools.