Boosting AWS Glue ETL Job Performance When Loading to Redshift
A company is using AWS Glue ETL to transform and load data from Amazon S3 to Amazon Redshift. The data engineer notices that the job is taking longer than expected. Which TWO actions can improve the job performance?
Quick Answer
The correct answer is to partition the source data in S3 and increase the number of DPUs for the AWS Glue job. Partitioning the source data in S3 reduces the amount of data scanned by Glue, allowing it to process only relevant subsets in parallel, while increasing DPUs allocates more distributed processing units to the ETL job, directly improving parallelism and throughput when loading to Redshift. On the AWS Certified Data Engineer Associate DEA-C01 exam, this question tests your understanding of how Glue’s serverless architecture scales—common traps include confusing S3 Transfer Acceleration (which only speeds uploads) or Redshift Spectrum (which is for querying, not Glue ETL). Remember that Glue job performance is about Glue-side resources and data organization, not Redshift instance size. A useful memory tip: “Partition and DPU—two levers for Glue throughput.”
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 data in S3.
Options B and C are correct because partitioning the source data in S3 reduces the amount of data scanned by Glue, improving I/O efficiency, and increasing the number of DPUs adds more parallelism for transformations. Option A is incorrect because Redshift Spectrum is for querying data in S3 directly from Redshift, not for Glue ETL jobs. Option D is incorrect because S3 Transfer Acceleration speeds up uploads to S3 but does not affect Glue job performance during ETL processing. Option E is incorrect because larger Redshift node types do not impact Glue job execution; they only affect Redshift query performance.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use Amazon Redshift Spectrum to query data directly.
Why it's wrong here
Spectrum is not used within Glue ETL.
- ✓
Partition the source data in S3.
Why this is correct
Partitioning reduces data scanned.
- ✓
Increase the number of DPUs for the Glue job.
Why this is correct
More DPUs increase parallelism.
- ✗
Enable S3 Transfer Acceleration.
Why it's wrong here
TA is for faster uploads to S3, not for Glue jobs.
- ✗
Use a larger Redshift node type.
Why it's wrong here
Does not affect Glue job 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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Same concept, more angles
1 more way this is tested on DEA-C01
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A company is using AWS Glue ETL to process data from Amazon RDS for MySQL to Amazon S3. The job runs daily and takes 2 hours to complete. The engineer wants to improve performance without increasing cost significantly. Which TWO actions should the engineer take? (Choose TWO.)
medium- A.Switch to a smaller worker type (e.g., G.1X instead of G.2X).
- B.Use Spark DataFrames instead of DynamicFrames.
- C.Enable 'Auto Scaling' in the Glue job configuration.
- ✓ D.Add a partition column to the source table based on a date column.
- ✓ E.Increase the number of Glue DPUs.
Why D: Adding a partition column (e.g., based on a date column) to the source table enables AWS Glue to use partition pruning during the read phase. This reduces the amount of data scanned and processed by the ETL job, directly improving performance without increasing cost. Partitioning is a common optimization for large datasets in RDS or S3-based sources.
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This DEA-C01 practice question is part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the DEA-C01 exam.