MLS-C01 Data Engineering Practice Question
A company uses Amazon Redshift as a data warehouse. They need to load 50 TB of clickstream data from S3 into Redshift daily. The data arrives in 5-minute intervals as gzipped CSV files. The target table has a sort key and a distribution key. The load must complete within 2 hours. Which approach is MOST efficient?
⚠ Common exam trap
The trap here is that candidates may overcomplicate the solution by choosing AWS Glue or staging tables, not realizing that Redshift's COPY command is purpose-built for high-speed parallel ingestion from S3 and is the most efficient method for bulk data loads.
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 a manifest file and gzip compression.
The COPY command is the most efficient way to load large volumes of data into Amazon Redshift because it uses the cluster's massively parallel processing (MPP) architecture to read data directly from S3 in parallel across all nodes. With a manifest file, you can specify multiple gzipped CSV files, and the gzip compression reduces network I/O and storage overhead. This approach can easily load 50 TB within 2 hours, especially when the target table has a sort key and distribution key, as COPY automatically leverages these for optimal data distribution and sorting during the load.
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 to transform the data and write to Redshift using JDBC.
Why it's wrong here
Glue is slower than native COPY for large volumes.
- ✗
Use a staging table and then merge using a stored procedure.
Why it's wrong here
Staging adds extra steps and time.
- ✗
Use a series of INSERT statements from a Lambda function.
Why it's wrong here
INSERT is not efficient for 50 TB.
- ✓
Use the COPY command with a manifest file and gzip compression.
Why this is correct
COPY is optimized for bulk loading from 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 |
Go deeper
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