Two storage philosophies
A data warehouse stores cleaned, structured tables optimized for fast SQL analytics. A data lake stores raw files of any shape at low cost.
Data warehouse
- Holds schema on write data, meaning structure is enforced when you load it.
- Tuned for business intelligence, dashboards, and complex joins.
- More expensive per byte but very fast for analytical queries.
Data lake
- Holds schema on read data, so structure is applied only when you query.
- Accepts logs, images, JSON, and CSV side by side in object storage.
- Cheap and flexible, but raw data can become a hard to use data swamp without governance.
How they fit together
Many pipelines land raw data in a lake first, then refine and load curated tables into a warehouse for analysts. The lake is the durable source of truth, while the warehouse is the polished serving layer.
Key idea
A lake stores cheap raw data of any shape, while a warehouse stores structured data tuned for fast analytics.