DEA-C01 Data Ingestion and Transformation Practice Question
A data engineer is tasked with transforming JSON data from an S3 bucket into Parquet format for efficient querying. The transformation should run on a schedule every hour. Which AWS service is best suited for this task?
⚠ Common exam trap
A common mix-up: candidates confuse Athena's ability to query Parquet with the ability to transform data into Parquet, but Athena is a query engine, not an ETL service, and cannot perform scheduled data format conversions.
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
✓
AWS Glue
AWS Glue is the best choice because it is a fully managed ETL service designed specifically for transforming and cataloging data at scale. It can natively read JSON from S3, convert it to Parquet, and run on a scheduled hourly basis using a Glue job with a trigger, without requiring server management or custom infrastructure.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
AWS Lambda
Why it's wrong here
Lambda has a 15-minute execution limit and is not designed for hourly batch ETL.
- ✗
Amazon Athena
Why it's wrong here
Athena is for querying, not for scheduled transformations.
- ✓
AWS Glue
Why this is correct
Glue provides managed ETL jobs that can be scheduled and support Parquet conversion.
- ✗
Amazon EMR
Why it's wrong here
While Amazon EMR can transform JSON to Parquet using Spark or Hive, it introduces significant overhead for a simple scheduled hourly job, as it requires provisioning and managing a cluster of EC2 instances, which is unnecessary for this lightweight, recurring task. The temptation arises because EMR is a powerful tool for large-scale, complex data processing pipelines, such as running multi-stage ETL jobs on petabytes of data, where its distributed computing capabilities would be the correct choice.
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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