DEA-C01 Data Ingestion and Transformation Practice Question
A company runs a nightly AWS Glue ETL job that reads from a JDBC source (PostgreSQL) and writes to S3 in Parquet format. The job takes over 6 hours, but the SLA requires completion within 4 hours. The source table has 500 million rows and is updated frequently. Which approach will most reliably reduce job duration?
⚠ Common exam trap
Test-takers frequently assume increasing DPUs (Option D) or adding parallelism (Option B) will linearly speed up JDBC reads, but they fail to recognize that the bottleneck is the source database's I/O and network throughput, not Glue's compute capacity, and that predicate pushdown is the only option that reduces the data volume at the source.
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
✓
Partition the source table by year and use pushdown predicates in the Glue job.
Partitioning the source table by year and using pushdown predicates allows AWS Glue to read only the relevant partitions from PostgreSQL, drastically reducing the data scanned and transferred. This directly addresses the 500 million row volume by minimizing the JDBC read workload, which is the primary bottleneck. Option B, using multiple JDBC connections in parallel via hash partitioning, can improve performance but is less reliable: it significantly increases load on the source database, may hit connection limits, and its effectiveness depends on the JDBC driver's support for hashfield/hashexpression. Moreover, it still reads all rows, whereas pushdown predicates reduce the data volume at the source, making option C more reliable for meeting the 4-hour SLA.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Enable job bookmark and schedule the job to run more frequently.
Why it's wrong here
Bookmarks help with incremental processing but not with the initial full load.
- ✗
Use multiple JDBC connections in parallel by setting 'hashexpression' and 'hashfield'.
Why it's wrong here
Glue supports parallel JDBC reads using hash partitioning; this can improve performance.
- ✓
Partition the source table by year and use pushdown predicates in the Glue job.
Why this is correct
This reduces the data scanned by filtering on partition columns.
- ✗
Increase the number of DPUs for the Glue job to 100.
Why it's wrong here
More DPUs can help but are limited by source parallelism and may not reduce time enough.
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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