SAP-C02 Practice Question: Accelerate Workload Migration and Modernization
A company is migrating a large-scale data warehouse from on-premises to Amazon Redshift. The current on-premises solution uses a proprietary columnar storage format. The company needs to minimize data transformation effort. Which approach should the company take?
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 to convert the proprietary format to Apache Parquet and load into Amazon Redshift.
AWS Glue can crawl the proprietary columnar storage format and convert it to Apache Parquet, a columnar format efficiently loaded into Amazon Redshift via COPY from S3, minimizing data transformation effort. Option A is incorrect because the COPY command cannot directly load from on-premises storage; data must first be staged in Amazon S3 in a supported format. Option C is incorrect because AWS DMS is designed for migrating relational databases, not proprietary file formats. Option D is incorrect because Amazon Kinesis Data Firehose is for streaming real-time data, not batch migration of existing large-scale 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 the Amazon Redshift COPY command to load data directly from the on-premises storage.
Why it's wrong here
COPY command requires data in a supported format in S3.
- ✓
Use AWS Glue to convert the proprietary format to Apache Parquet and load into Amazon Redshift.
Why this is correct
AWS Glue can transform data into columnar formats optimized for Redshift.
- ✗
Use AWS Database Migration Service (DMS) to migrate the data directly to Redshift.
Why it's wrong here
DMS is for database migrations, not for proprietary storage formats.
- ✗
Use Amazon Kinesis Data Firehose to stream the data to Redshift.
Why it's wrong here
Kinesis Data Firehose ingests streaming data, not static files from a proprietary columnar format, so it cannot directly load the existing on-premises dataset without first converting it into a stream-compatible format, which contradicts the requirement to minimise transformation effort. It is tempting because Firehose is commonly used for near-real-time ingestion into Redshift, and would be correct if the source were a continuous stream of structured records rather than a bulk migration of pre-existing columnar data.
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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