What is the difference between a data lake and a data warehouse in this strategy?

• A. Data lake stores raw, diverse data; data warehouse stores structured, curated data for BI and ML.
• B. Data lake stores structured data only; data warehouse stores raw data.
• C. They are the same.
• D. Data lake is for backups only.

Answer: (A)

Data lakes and data warehouses serve different roles in analytics. A data lake stores raw, diverse data in its native formats, letting you ingest and explore everything from logs to images with minimal upfront transformation. It often relies on schema-on-read, meaning the structure is applied when you read the data, not when you store it. A data warehouse, on the other hand, stores structured, curated data with defined schemas, quality rules, and governance, optimized for fast, reliable BI reports and ML model training. This is typically schema-on-write, where data is transformed and cleaned before loading so analytics workloads can run efficiently.

That combination—raw data in the lake for flexibility and governance-ready, structured data in the warehouse for reliable analytics—captures why the two are distinct yet complementary. The other options misstate the roles (lakes aren’t only for backups or structured data only; warehouses aren’t for raw data), so they don’t reflect how these layers support scalable analytics in a modern strategy.