DEA-C01 Data Ingestion and Transformation Practice Question
A data engineer needs to transform CSV files arriving in an S3 bucket into Parquet format and store them in another S3 bucket. The transformation is simple and on-demand, triggered by data arrival. Which solution is the MOST cost-effective and requires the least operational overhead?
⚠ Common exam trap
The DEA-C01 exam often tests the misconception that AWS Glue is always the best choice for ETL, but for simple, event-driven transformations with minimal overhead, Lambda is more cost-effective and operationally simpler than Glue's managed Spark environment.
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
✓
Use S3 Events to trigger an AWS Lambda function that transforms the data
Using S3 Events to trigger an AWS Lambda function is the most cost-effective and operationally lightweight solution for simple, on-demand CSV-to-Parquet transformations. Lambda scales automatically with each S3 PUT event, incurs no idle cost, and requires no cluster management, making it ideal for event-driven, low-volume transformations.
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 streaming
Why it's wrong here
EMR with Spark streaming provisions a persistent cluster billing per hour, and streaming suits continuous ingestion rather than on-demand arrival-triggered batches. EMR is genuinely right for large-scale, long-running or complex Spark workloads; here an event-driven serverless transform avoids idle cluster cost and administration.
- ✗
Use Amazon Athena to create a new table with Parquet format
Why it's wrong here
Athena queries data in place and writes results to a query-result location; it cannot rewrite an S3 object into Parquet in another bucket as a defined ETL target. Athena is genuinely correct for ad-hoc SQL over S3, but format conversion needs a job that reads CSV and writes Parquet.
- ✗
Use AWS Glue ETL jobs scheduled to run every hour
Why it's wrong here
Hourly scheduled Glue jobs run regardless of whether files arrived, paying for empty runs and adding up to an hour of latency. Glue is genuinely correct for managed, complex or high-volume ETL; an S3 event triggering a job matches on-demand arrival with no idle scheduling overhead.
- ✓
Use S3 Events to trigger an AWS Lambda function that transforms the data
Why this is correct
S3 event notifications invoke Lambda directly on object arrival, so no cluster runs between executions and no polling infrastructure is needed. Lambda's per-invocation billing suits sporadic, on-demand CSV-to-Parquet conversion, and the service manages scaling and patching, satisfying the minimal operational overhead constraint.
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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