DEA-C01 Data Ingestion and Transformation Practice Question
A company uses AWS Glue DataBrew to clean and transform data for analytics. The source data is in Parquet format in Amazon S3. The transformation includes filtering rows and adding calculated columns. What is the MOST cost-effective way to run these transformations on a schedule?
⚠ Common exam trap
The trap here is that candidates may over-engineer the solution by choosing Glue ETL or EMR, assuming that Parquet processing requires custom Spark code, when DataBrew's visual recipes can handle filtering and calculated columns without any code and at lower cost.
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 Glue DataBrew recipe and schedule the job using a cron expression
AWS Glue DataBrew is purpose-built for visual data preparation, and scheduling a DataBrew recipe job with a cron expression directly meets the requirement to run filtering and column calculations on Parquet data in S3 without writing code. This is the most cost-effective approach as it avoids provisioning or managing compute resources beyond the serverless DataBrew job runs.
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 EMR with Spark
Why it's wrong here
EMR adds cluster management overhead and cost.
- ✓
Create a Glue DataBrew recipe and schedule the job using a cron expression
Why this is correct
DataBrew supports scheduling directly.
- ✗
Create an AWS Lambda function triggered by S3 events
Why it's wrong here
Lambda has time limits and is not designed for interactive data preparation.
- ✗
Use AWS Glue ETL with PySpark
Why it's wrong here
Glue ETL is more expensive and complex for simple transformations.
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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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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