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MLA-C01 Practice Question: A data engineer needs to integrate a new…
A data engineer needs to integrate a new streaming data source into an existing ML pipeline. The data arrives as JSON records and must be transformed to Parquet format, partitioned by date, and stored in Amazon S3. The engineer also needs to catalog the data for querying with Amazon Athena. Which service should be used to perform the transformation and cataloging?
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 ETL job
AWS Glue ETL jobs can read streaming data (e.g., from Kinesis), transform it (e.g., JSON to Parquet), write to S3 with partitioning, and update the Glue Data Catalog for Athena to query. This is a managed, serverless solution.
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 ETL job
Why this is correct
Glue ETL can process streaming data (via Glue streaming ETL), convert to Parquet, partition, and catalog the output.
- ✗
Amazon EMR with Spark Streaming
Why it's wrong here
Amazon EMR with Spark Streaming is powerful for real-time, large-scale data transformation, making it tempting for the streaming JSON to Parquet conversion and partitioning. However, EMR does not natively provide the integrated cataloging functionality for Amazon Athena within the AWS Glue Data Catalog. While it excels at processing, a separate service like AWS Glue Crawler would still be needed to perform the required cataloging, failing to meet the dual requirement of transformation and cataloging from a single service.
- ✗
Amazon Kinesis Data Analytics
Why it's wrong here
Kinesis Data Analytics performs real-time SQL analytics but does not natively catalog data or write partitioned Parquet to S3.
- ✗
Amazon SageMaker Data Wrangler
Why it's wrong here
Data Wrangler is for interactive data preparation, not for building production streaming pipelines.
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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