DEA-C01 Data Ingestion and Transformation Practice Question
A data pipeline uses AWS Glue to read from an Amazon S3 bucket containing millions of small CSV files (each < 1 MB). The ETL job is slow. Which optimization would most improve performance?
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 S3 file grouping to combine small files
Using Amazon S3 file grouping or converting to columnar format like Parquet reduces the number of files and improves read performance. Increasing workers helps, but file consolidation is more impactful. Using G.1X worker type may help, but grouping files is key. Using Spark SQL directly does not address the small files problem.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Write the ETL script using PySpark instead of Scala
Why it's wrong here
Language choice is not the bottleneck; small files are the issue.
- ✗
Increase the number of Glue workers
Why it's wrong here
More workers help but do not solve the small files problem; overhead from many files remains.
- ✗
Use the G.1X worker type for more memory
Why it's wrong here
More memory helps but does not address the root cause of many small files.
- ✓
Use S3 file grouping to combine small files
Why this is correct
Grouping small files reduces the number of partitions and improves Spark performance.
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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