DEA-C01 Data Ingestion and Transformation Practice Question
A data engineer notices that an AWS Glue job writing to Amazon S3 in Parquet format creates many small files (less than 1 MB each). This leads to poor query performance in Amazon Athena. What is the BEST way to reduce the number of output files?
⚠ Common exam trap
Candidates often confuse 'coalesce(1)' or 'repartition()' as file-size solutions, but these operations control the number of Spark partitions, not the final file size, and can actually worsen the problem or cause job failures.
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
✓
Enable 'groupFiles' in the Glue job's S3 target configuration.
Enabling 'groupFiles' in the AWS Glue job's S3 target configuration instructs Glue to coalesce small files into larger ones (default target size ~128 MB) during the write phase. This directly reduces the number of small Parquet files written to S3, improving Athena query performance by minimizing S3 LIST and GET overhead.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Enable 'groupFiles' in the Glue job's S3 target configuration.
Why this is correct
Glue's groupFiles option merges small files during write.
- ✗
Use 'coalesce(1)' at the end of the ETL script.
Why it's wrong here
May cause OOM and single executor bottleneck.
- ✗
Use 'repartition(100)' to increase parallelism.
Why it's wrong here
Increases number of files, worsening the issue.
- ✗
Configure an S3 lifecycle policy to delete small files.
Why it's wrong here
Lifecycle policies do not merge files.
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 |
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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