MLS-C01 Data Engineering Practice Question
An e-commerce company uses Amazon DynamoDB as the primary data store for user sessions. They want to run analytics on historical session data using Amazon Athena. What is the recommended approach to export DynamoDB data to S3 in a format optimized for Athena?
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
✓
Use AWS Glue ETL to read from DynamoDB and write to S3 as Parquet
AWS Glue ETL can read from DynamoDB and write to S3 in Parquet format, which is optimized for Athena due to its columnar storage and compression. Option A (AWS Data Pipeline) can copy data to S3 as CSV, but CSV is less efficient for Athena and Data Pipeline is a legacy service. Option B (Amazon Kinesis Data Firehose) is designed for streaming data, not for exporting existing DynamoDB tables. Option C (DynamoDB Streams with Lambda) writes to S3 as JSON, which is less performant than Parquet for Athena queries and adds operational complexity.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use AWS Data Pipeline to copy data to S3 as CSV
Why it's wrong here
Data Pipeline is older; Glue is more modern.
- ✗
Use Amazon Kinesis Data Firehose to stream data from DynamoDB to S3
Why it's wrong here
Firehose is for streaming, not bulk export from DynamoDB.
- ✗
Use DynamoDB Streams with AWS Lambda to write to S3 as JSON
Why it's wrong here
JSON is not optimal for Athena; streaming adds complexity.
- ✓
Use AWS Glue ETL to read from DynamoDB and write to S3 as Parquet
Why this is correct
Glue can efficiently export data and convert to columnar format.
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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