DEA-C01 Data Ingestion and Transformation Practice Question
A data engineer is building a data ingestion pipeline that reads JSON files from Amazon S3 and loads them into an Amazon Redshift table using COPY commands. The files are gzip compressed and contain nested JSON. The engineer wants to minimize transformation steps. Which approach should the engineer use?
⚠ Common exam trap
The trap here is that candidates often overcomplicate the solution by assuming nested JSON requires an ETL tool like Glue or Athena, when Redshift's COPY command with 'auto' or 'jsonpaths' can handle nested structures natively, minimizing transformation steps as explicitly requested.
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 the COPY command with the 'auto' option to ingest JSON directly.
The COPY command with the 'auto' option can directly ingest gzip-compressed JSON files from S3 into Redshift, automatically inferring the schema and handling nested structures without requiring intermediate transformation steps. This minimizes transformation steps by leveraging Redshift's native JSON parsing capability, which supports both 'auto' and 'jsonpaths' options for nested data.
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 Athena to query the JSON and INSERT INTO Redshift.
Why it's wrong here
Athena is not optimized for bulk load; COPY is better.
- ✗
Use Kinesis Data Firehose to transform and load into Redshift.
Why it's wrong here
Firehose is for streaming, not batch S3 files.
- ✓
Use the COPY command with the 'auto' option to ingest JSON directly.
Why this is correct
COPY with 'auto' automatically parses JSON.
- ✗
Use AWS Glue ETL to flatten the JSON and write to S3 as CSV, then COPY from CSV.
Why it's wrong here
Adds complexity; COPY can handle JSON directly.
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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