DEA-C01 Data Ingestion and Transformation Practice Question
A data engineer needs to transform JSON data from Amazon S3 into Parquet format using AWS Glue. The source files are in a bucket with thousands of small files. What is the best practice to optimize the Glue job performance?
⚠ Common exam trap
A common mix-up: candidates assume more DPUs always improve performance, but for small files the bottleneck is metadata overhead, not compute capacity, so increasing DPUs without addressing file grouping leads to wasted resources and no speedup.
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 'Group small files' in the Glue job or use a DynamicFrame with coalesce.
Enabling 'Group small files' in AWS Glue automatically coalesces thousands of small input files into larger partitions, reducing the number of tasks and minimizing overhead from task scheduling and S3 list operations. This is the recommended best practice for handling small files in Glue ETL jobs, as it optimizes read performance without requiring manual coalesce or repartitioning.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Convert the JSON files to CSV before processing with Glue.
Why it's wrong here
Converting JSON to CSV adds a full extra transformation pass and still leaves the small-file count unchanged, so Glue's per-object overhead persists. CSV suits fixed-schema tabular exports consumed by non-Spark tools; it does not address the file-count bottleneck here.
- ✓
Enable 'Group small files' in the Glue job or use a DynamicFrame with coalesce.
Why this is correct
Coalescing merges thousands of small S3 objects into fewer, larger partitions before processing, cutting per-file overhead and task scheduling latency. This satisfies the small-files constraint by reducing the number of Spark tasks and improving Parquet write efficiency.
- ✗
Use an AWS Lambda function to pre-process the files.
Why it's wrong here
Lambda pre-processing adds an extra hop and cannot compact thousands of small files into fewer large ones the way Glue's grouping or S3 compaction does; the job still reads the same object count. Lambda suits lightweight per-object event handling, not bulk compaction ahead of a distributed Spark transform.
- ✗
Increase the number of DPUs to the maximum.
Why it's wrong here
Adding DPUs scales worker parallelism, but with thousands of tiny objects each task still opens one file, so overhead per object dominates and cost rises without throughput gain. Maximum DPU allocation is right for genuinely large single-file or CPU-heavy shuffles, not small-file fan-out.
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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