DEA-C01 Data Ingestion and Transformation Practice Question
A data engineer needs to transform data in an S3 data lake using AWS Glue ETL. The data is in CSV format and needs to be converted to Parquet with partitioning by date. The engineer wants to minimize the number of files written to S3 to improve query performance. Which TWO configuration options should the engineer use? (Select TWO.)
⚠ Common exam trap
Candidates often confuse increasing parallelism (Option A) with improving performance, but in this context, more parallelism leads to more small files, which degrades query performance; the correct approach is to reduce file count via coalesce and enable partition pruning.
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 the coalesce method to reduce the number of output partitions.
Using `coalesce` reduces the number of output partitions, which directly minimizes the number of files written to S3. Fewer, larger Parquet files improve query performance by reducing the overhead of file listing and metadata operations in engines like Amazon Athena or Redshift Spectrum.
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 workers in the Glue job to increase parallelism.
Why it's wrong here
More workers usually produce more partitions and more files.
- ✓
Use the coalesce method to reduce the number of output partitions.
Why this is correct
Coalesce reduces the number of partitions before writing, resulting in fewer files.
- ✗
Disable compression in the Parquet output.
Why it's wrong here
Disabling compression increases file size, which may degrade query performance.
- ✓
Enable partition pruning in the Glue job by setting the 'partitionKeys' parameter.
Why this is correct
Partition pruning ensures that only relevant partitions are read, reducing I/O and improving performance.
- ✗
Set the 'groupFiles' option to 'inPartition' in the DynamicFrame writer.
Why it's wrong here
GroupFiles can combine small files but does not directly minimize the number of files; coalesce is more direct.
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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