Which AWS pairing correctly represents a data lake and a data warehouse example?

Which AWS pairing correctly represents a data lake and a data warehouse example?

• A. AWS Lake Formation; Redshift
• B. Google Cloud Storage; BigQuery
• C. Azure Data Lake Storage; Synapse
• D. AWS S3 + Lake Formation/Glue; Redshift

Answer: (D)

Think of a data lake as a vast storage of raw data in an object store, while a data warehouse is a separate system optimized for answering analytics questions on structured data. In AWS, the raw data lake lives on an object store like S3, and Lake Formation or Glue helps organize, catalog, and govern that data so it’s usable for analytics. The data warehouse in this setup is Redshift, which is designed for fast, complex queries on clean, structured data. So the best pairing shows the lake built on S3 with Lake Formation or Glue handling cataloging and governance, plus Redshift as the warehouse for analytics. The other options either point to non-AWS services or describe the data lake only in terms of governance without naming the storage layer.

Think of a data lake as a vast storage of raw data in an object store, while a data warehouse is a separate system optimized for answering analytics questions on structured data. In AWS, the raw data lake lives on an object store like S3, and Lake Formation or Glue helps organize, catalog, and govern that data so it’s usable for analytics. The data warehouse in this setup is Redshift, which is designed for fast, complex queries on clean, structured data.

So the best pairing shows the lake built on S3 with Lake Formation or Glue handling cataloging and governance, plus Redshift as the warehouse for analytics. The other options either point to non-AWS services or describe the data lake only in terms of governance without naming the storage layer.