What is the difference between a data warehouse and a data lake?
Short answer: A warehouse stores cleaned, structured data for fast analytics (schema on write); a lake stores raw data in any format cheaply (schema on read).
What to cover
- Warehouse: structured, modelled tables, SQL analytics, BI dashboards (Snowflake, BigQuery, Redshift).
- Lake: raw files of any type on object storage (S3, ADLS), schema applied when read.
- Users: analysts on the warehouse; data scientists and engineers on the lake.
- Lakehouse (Delta Lake, Iceberg) adds tables and ACID transactions on top of a lake.
Example answer
A data warehouse holds cleaned, structured data in a defined schema, optimised for fast SQL and BI — like Snowflake. A data lake stores raw data in its native format on cheap object storage such as S3, and the schema is applied when you read it. In practice we land raw data in the lake, then transform the trusted parts into the warehouse. Lakehouse formats like Iceberg blur the line by adding tables and transactions on the lake.
Avoid
- Saying one replaces the other — most companies use both.
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