DEA-C01 Data Ingestion and Transformation Practice Question
A company uses AWS Glue to process data from multiple S3 buckets. The Glue job runs daily and reads data from a bucket that contains millions of small files (each < 1 MB). The job has been running for hours and is often close to the 8-hour timeout limit. Which optimization would MOST reduce the job's runtime?
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
✓
Pre-process the data to consolidate small files into larger files before the Glue job.
The most impactful optimization for reducing the runtime of a Glue job that reads millions of small files is to consolidate those files into larger files prior to processing. Each small file incurs overhead for listing, opening, and reading, and Spark's task scheduler must create a separate task for each file or partition. By grouping small files (e.g., via S3 batch operations or a compaction job), the number of files decreases dramatically, reducing scheduling overhead and I/O operations. While converting to Parquet (Option B) and increasing DPUs (Option C) can improve performance, their benefits are limited if the underlying file count remains high. A larger Spark shuffle partition size (Option D) only affects shuffle operations, not the initial file read overhead. Therefore, file consolidation is the most effective single change.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Pre-process the data to consolidate small files into larger files before the Glue job.
Why this is correct
Fewer, larger files reduce the overhead of opening and reading files.
- ✗
Convert the source data from CSV to Parquet format.
Why it's wrong here
Parquet is efficient but still many small files cause overhead.
- ✗
Increase the number of DPUs allocated to the Glue job.
Why it's wrong here
More DPUs can improve parallelism but the overhead of many small files remains.
- ✗
Use a larger Spark shuffle partition size.
Why it's wrong here
Shuffle partitions affect data shuffling, not input read 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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