SAP-C02 Practice Question: Accelerate Workload Migration and Modernization
A company is planning to migrate a large-scale Hadoop cluster to Amazon EMR. The cluster currently processes batch jobs using a mix of MapReduce and Spark. The company wants to minimize changes to the existing code and operational processes. Which migration approach should the architect recommend?
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
✓
Rehost the cluster on Amazon EMR using the same MapReduce and Spark configurations
Rehosting the Hadoop cluster on Amazon EMR with the same configuration allows the existing MapReduce and Spark code to run with minimal changes. Refactoring to use only Spark would require code changes. Replatforming to Amazon Redshift would change the architecture. Retiring and using Athena would require significant changes to data storage and queries.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Refactor all jobs to use only Apache Spark on Amazon EMR
Why it's wrong here
Refactoring MapReduce jobs to Spark requires code changes.
- ✗
Retire the cluster and use Amazon Athena for ad-hoc queries
Why it's wrong here
Athena is not suitable for batch processing workloads.
- ✗
Replatform the data processing to use Amazon Redshift Spectrum
Why it's wrong here
This would require significant architectural changes.
- ✓
Rehost the cluster on Amazon EMR using the same MapReduce and Spark configurations
Why this is correct
This preserves existing code and processes, minimizing changes.
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