A centralized storage system that holds raw data in its native format until needed, at any scale.
A data lake stores data as-is — structured tables, semi-structured JSON, unstructured text, images, logs — without requiring a predefined schema. This flexibility makes it easy to ingest data quickly and decide how to use it later.
Data lakes are often contrasted with data warehouses: a warehouse imposes structure upfront for known reporting needs; a lake preserves raw data for exploratory analysis, machine learning, and future use cases not yet defined.
Example: A federal open data program stores raw sensor readings, satellite imagery, and survey responses in a data lake. Data scientists can access the raw files directly; analysts can query structured subsets that have been processed and loaded into a warehouse.
The risk of a data lake is becoming a "data swamp" — raw data with no documentation, no quality controls, and no way to find what you need. Good metadata and governance practices prevent this.