DBS-C01 Workload-Specific Database Design Practice Question
A company is migrating a large Oracle Data Warehouse (10 TB) to Amazon Redshift. The current system uses complex stored procedures, materialized views, and window functions. The company wants to minimize migration effort. Which approach is MOST suitable?
⚠ Common exam trap
Candidates often assume all cloud databases are equally suitable for data warehousing, overlooking that Redshift's columnar storage and MPP architecture are specifically designed for analytical workloads, while options like Aurora or DynamoDB are optimized for different use cases (OLTP or NoSQL).
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
✓
Migrate to Amazon Redshift and rewrite stored procedures as SQL scripts.
Amazon Redshift is purpose-built for large-scale data warehousing and analytics, making it the natural target for a 10 TB Oracle Data Warehouse. While stored procedures would need to be rewritten as SQL scripts (since Redshift uses a different procedural language), materialized views and window functions are natively supported, minimizing migration effort compared to other options.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Migrate to Amazon Aurora MySQL and use its parallel query feature.
Why it's wrong here
Aurora is OLTP, not optimized for large-scale analytics.
- ✓
Migrate to Amazon Redshift and rewrite stored procedures as SQL scripts.
Why this is correct
Redshift supports window functions, materialized views, and SQL scripting.
- ✗
Migrate to Amazon DynamoDB and use DAX for caching.
Why it's wrong here
DynamoDB is NoSQL, not suitable for complex SQL analytics.
- ✗
Migrate to Amazon RDS for PostgreSQL and use its foreign data wrappers.
Why it's wrong here
PostgreSQL may not handle 10 TB analytical workload as efficiently as Redshift.
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