DEA-C01 Data Operations and Support Practice Question
A company runs a daily batch process that reads data from Amazon S3, transforms it with AWS Glue, and loads it into Amazon Redshift. The process takes 6 hours, but the business requires completion within 4 hours. Which design change would MOST reduce runtime?
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
✓
Load data directly from S3 to Redshift using COPY command, then transform in Redshift
Loading data directly from S3 to Redshift using the COPY command eliminates the AWS Glue transformation step, which is the primary bottleneck. The COPY command is optimized for high-speed bulk loads, and performing transformations within Redshift (e.g., using SQL or stored procedures) can often be faster than an external ETL tool. Option A (increasing Glue workers) may help parallelism but does not address the overhead of the Glue job itself. Option C (S3 Select) reduces the data volume scanned by Glue but still requires the Glue transformation. Option D (columnar storage) is already the default in Redshift and does not reduce the Glue job runtime.
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 Glue workers
Why it's wrong here
This may speed up transformation but not the load into Redshift.
- ✓
Load data directly from S3 to Redshift using COPY command, then transform in Redshift
Why this is correct
COPY is highly efficient for bulk loading, and in-database transformation can be faster than Glue.
- ✗
Use S3 Select to filter data before Glue
Why it's wrong here
This reduces data volume but does not address the bottleneck of loading into Redshift.
- ✗
Switch to columnar storage in Redshift
Why it's wrong here
Redshift is already columnar.
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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