DEA-C01 Data Ingestion and Transformation Practice Question
A company is building a data lake on Amazon S3. The data sources include relational databases, streaming data, and log files. The data engineer needs to ensure that the data ingestion pipeline can handle schema evolution, support both batch and streaming, and provide a unified metadata catalog. Which THREE services should the engineer use? (Choose three.)
⚠ Common exam trap
Watch out — candidates often confuse Amazon Athena as a metadata catalog or ingestion service, but it is only a query engine that reads from S3 and relies on Glue for metadata, so it does not fulfill the ingestion or catalog requirements.
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 correct because it provides a unified metadata catalog (the AWS Glue Data Catalog) that stores schema information for data stored in Amazon S3. It supports schema evolution by allowing you to update the catalog schema as data formats change, and it integrates with both batch (AWS Glue ETL jobs) and streaming (AWS Glue Streaming ETL) ingestion pipelines, making it the central service for metadata management in a data lake.
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 Glue
Why this is correct
Provides schema discovery, catalog, and batch ETL.
- ✗
Amazon DynamoDB
Why it's wrong here
DynamoDB is a NoSQL database, not for data lake ingestion.
- ✗
Amazon Athena
Why it's wrong here
Athena is for querying, not ingestion.
- ✓
Amazon S3
Why this is correct
Central storage for the data lake.
- ✓
Amazon Kinesis Data Firehose
Why this is correct
Ingests streaming data into S3.
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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