DBS-C01 Workload-Specific Database Design Practice Question
A company runs an analytics platform that queries billions of rows of sales data. Queries are complex and involve aggregations across multiple dimensions. The data is updated in bulk daily. Which service should be used as the primary data store?
⚠ Common exam trap
Watch out — candidates often confuse Amazon Athena as a primary data store because it can query data in S3, but it is a serverless query engine, not a data store, and lacks the performance optimizations for complex aggregations on billions of rows that a dedicated data warehouse like Redshift provides.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Amazon Redshift
Amazon Redshift is the correct choice because it is a fully managed, petabyte-scale data warehouse optimized for complex analytical queries involving aggregations across multiple dimensions. Its columnar storage, massively parallel processing (MPP) architecture, and ability to handle bulk daily updates via COPY commands or INSERT operations make it ideal for querying billions of rows of sales data.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Amazon Redshift
Why this is correct
Amazon Redshift’s columnar storage and massively parallel processing (MPP) architecture directly satisfy the need for complex aggregations across billions of rows, as it scans only relevant columns and distributes query execution across multiple nodes. The daily bulk update constraint is met by Redshift’s efficient COPY command and vacuum/analyse operations, which optimise large-scale data loads without degrading analytical query performance.
- ✗
Amazon DynamoDB
Why it's wrong here
DynamoDB is not designed for complex aggregations.
- ✗
Amazon Athena
Why it's wrong here
Athena is a query service, not a primary data store.
- ✗
Amazon RDS for MySQL
Why it's wrong here
RDS is not optimized for analytical queries on billions of rows.
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