DEA-C01
Data Lake vs Data Warehouse for the DEA-C01 (2026)
Sep 25, 2026
The Data Engineer exam expects you to know the difference between a data lake and a data warehouse, and which AWS services power each.
The core difference
A data lake (built on S3) stores raw data in any format — structured, semi-structured, unstructured — cheaply and at massive scale, schema applied on read (Athena, Glue). A data warehouse (Redshift) stores cleaned, structured data optimized for fast, repeated analytical queries, schema on write. Raw everything, cheap, flexible → lake; curated, structured, fast BI → warehouse.
Test yourself
A company wants to store massive amounts of raw, mixed-format data cheaply now and decide how to analyze it later. Which approach?
- A Redshift data warehouse
- An S3-based data lake
- An RDS relational database
- A DynamoDB table
👉 Click to reveal the answer & explanation
Correct answer: B. An S3-based data lake stores raw data of any format cheaply with schema-on-read — ideal when you’ll decide analysis later. A warehouse (A) needs structured, modeled data upfront; RDS (C) and DynamoDB (D) are operational databases, not cheap raw-data lakes.
Related topics
Amazon S3 · Amazon Redshift · Lake Formation
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