MLS-C01 Data Engineering Practice Question
An e-commerce company uses Amazon Redshift for analytics. The data engineering team needs to load daily sales data from an S3 bucket that receives new files every hour. The data must be loaded into Redshift with minimal impact on query performance during the day, and they need to handle late-arriving data (files that appear after the daily load). Which approach should they use?
⚠ Common exam trap
Candidates often confuse continuous streaming (Option C) with batch incremental loading, not realizing that Kinesis Firehose is optimized for real-time streams, not for handling sporadic late-arriving files in a batch context.
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 a staging table to load data incrementally with a MERGE operation, and schedule a late-arriving data job to merge files that arrive after the daily load.
It uses a staging table to incrementally load data with a MERGE operation, which minimizes impact on query performance by avoiding full table overwrites. The separate late-arriving data job handles files that appear after the daily load, ensuring completeness without blocking ongoing queries. This approach aligns with Redshift's best practices for incremental loads and late-arriving data handling.
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 Glue ETL to copy the data from S3 to Redshift, overwriting the existing data each day.
Why it's wrong here
Overwriting loses late-arriving data and causes downtime during load.
- ✓
Use a staging table to load data incrementally with a MERGE operation, and schedule a late-arriving data job to merge files that arrive after the daily load.
Why this is correct
Staging tables allow incremental upserts and handling of late data without blocking queries.
- ✗
Stream the data from S3 using Amazon Kinesis Firehose to load into Redshift continuously.
Why it's wrong here
Firehose is for streaming, not for batch daily loads with late-arriving data handling.
- ✗
Use Amazon Redshift Spectrum to query data directly from S3 and create external tables.
Why it's wrong here
Spectrum queries data in S3 without loading, which can be slower for frequent analytics queries.
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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