MLS-C01 Data Engineering Practice Question
A data engineer needs to load data from a MySQL database to Amazon S3 daily. The database is 500 GB and the load window is 2 hours. The data must be extracted without impacting the source database performance. Which AWS service should be used to perform the extraction?
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
✓
AWS Database Migration Service (AWS DMS) with a full-load task to S3.
(AWS Database Migration Service). AWS DMS is specifically designed for migrating databases to AWS with minimal impact on the source. It can perform a full-load task to extract data from MySQL and write it to S3 efficiently within the 2-hour window. Option A (AWS Glue ETL) uses JDBC and can cause higher overhead on the source, potentially impacting performance. Option C (Amazon Athena with MySQL federated query) is a query service, not an extraction tool, and may not handle 500 GB efficiently. Option D (Amazon EMR with Spark) is for big data processing and incurs overhead for setup and coordination, making it less suitable for direct daily extraction without impact.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
AWS Glue ETL job using a JDBC connection to read the full table.
Why it's wrong here
A full table read via JDBC in AWS Glue would lock the MySQL source during extraction, violating the requirement to avoid impacting database performance. This approach is tempting because Glue ETL is designed for batch data transformation and loading, and would be correct if the source could tolerate read locks or if incremental extraction were acceptable.
- ✓
AWS Database Migration Service (AWS DMS) with a full-load task to S3.
Why this is correct
DMS is designed for minimal impact migration and can load data directly to S3.
- ✗
Amazon Athena with the MySQL federated query connector.
Why it's wrong here
Athena is for querying data in S3, not for extracting from MySQL.
- ✗
Amazon EMR with a Spark job reading from MySQL via JDBC.
Why it's wrong here
EMR is for processing, not a managed data extraction service.
Quick reference
AWS S3 Storage Class Comparison
| Storage Class | Min Duration | Retrieval | Use Case |
|---|---|---|---|
| S3 Standard | None | Immediate | Frequently accessed data |
| S3 Standard-IA | 30 days | Immediate | Infrequent access, rapid retrieval |
| S3 One Zone-IA | 30 days | Immediate | Non-critical infrequent data |
| S3 Intelligent-Tiering | None | Immediate–hours | Unknown or changing access patterns |
| S3 Glacier Instant | 90 days | Milliseconds | Archive with instant retrieval |
| S3 Glacier Flexible | 90 days | Minutes–hours | Archive, flexible retrieval |
| S3 Glacier Deep Archive | 180 days | Hours | Long-term compliance archive |
Go deeper
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