MLS-C01 Data Engineering Practice Question
A company runs a data pipeline using AWS Glue ETL jobs that process about 10 TB of data daily from Amazon S3. The jobs are triggered by a schedule and write results to a separate S3 bucket. Recently, the jobs have been taking longer to complete, and the data engineering team has observed that the number of files in the source bucket has increased significantly, from thousands to millions of small files (each about 100 KB). The Glue jobs are configured to use the 'Group Files' option, but performance is still poor. The team needs to improve the job performance without changing the source data generation process. Which course of action should the team take?
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
✓
Create a separate Glue job that runs before the main job to consolidate small files into larger ones in the source bucket
The main performance bottleneck is the large number of small files, which causes high overhead in reading metadata and opening files. Option D addresses this by creating a separate Glue job that consolidates small files into larger files (e.g., 100 MB) before the main ETL job runs, reducing the file count and improving read performance. Option A is incorrect because increasing DPUs may provide more parallelism but does not solve the underlying small-file problem; the overhead of opening millions of files remains. Option B is incorrect because switching to Amazon EMR with Spark would still encounter the same small-file issue unless additional measures (like coalesce or file compaction) are taken, which Option D already provides. Option C is incorrect because AWS Lambda has limitations on execution duration and memory, making it impractical to pre-process millions of small files efficiently.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Increase the number of DPUs allocated to the existing Glue job
Why it's wrong here
More DPUs provide parallelism but each file still requires overhead.
- ✗
Switch the ETL processing to Amazon EMR with Spark
Why it's wrong here
Spark also suffers from small file overhead if not addressed.
- ✗
Use AWS Lambda to pre-process the files and combine them
Why it's wrong here
Lambda has a 15-minute timeout and is not ideal for large-scale consolidation.
- ✓
Create a separate Glue job that runs before the main job to consolidate small files into larger ones in the source bucket
Why this is correct
Consolidation reduces the number of files, improving 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 |
Go deeper
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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