DEA-C01 Data Ingestion and Transformation Practice Question
A data engineer is designing a data ingestion pipeline for JSON files landing in an Amazon S3 bucket. The pipeline must transform the data (e.g., flatten nested structures) and load it into Amazon Redshift. The transformation logic is complex and may evolve frequently. Which approach provides the MOST flexibility and ease of maintenance?
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 AWS Glue ETL jobs to transform the data and load into Redshift.
AWS Glue ETL jobs provide a serverless, code-based environment using Apache Spark, which offers flexibility for complex transformations like flattening nested JSON structures. Glue can be configured to support exactly-once semantics through Spark checkpointing and transactional writes to Redshift, making it reliable for critical data pipelines. It handles varying file sizes and can be easily updated as transformation logic evolves. Option A is incorrect because Lambda has execution time and memory limits, making it unsuitable for large JSON files or complex transformations, and achieving exactly-once requires careful idempotency design. Option B is incorrect because the Redshift COPY command loads raw JSON without transformation. Option D is incorrect because Athena is primarily for querying data in S3, not for performing ETL transformations and loading into Redshift.
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 AWS Lambda functions to transform each file and load into Redshift.
Why it's wrong here
Lambda has limits on execution time and memory for large files.
- ✗
Use the Amazon Redshift COPY command to load raw JSON directly.
Why it's wrong here
COPY does not transform data.
- ✓
Use AWS Glue ETL jobs to transform the data and load into Redshift.
Why this is correct
Glue ETL supports complex transformations and is easy to maintain.
- ✗
Use Amazon Athena to query the raw data and insert into Redshift.
Why it's wrong here
Athena is not designed for ETL transformation.
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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