DEA-C01 Data Store Management Practice Question
A company uses Amazon Redshift for analytics. The data engineer notices that queries are slow due to many small inserts. Which technique would improve write performance?
⚠ Common exam trap
It's easy for candidates to confuse performance tuning for reads (DISTKEY/SORTKEY) or general scaling (adding nodes) with the specific write performance bottleneck caused by many small inserts, overlooking the COPY command as the primary solution for bulk data loading.
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 to load data from Amazon S3.
The COPY command is the recommended way to load data into Amazon Redshift because it performs bulk inserts in parallel across all nodes, leveraging the cluster's distributed architecture. Small individual INSERT statements cause high overhead due to transaction logging and commit processing, leading to slow write performance. By loading data from Amazon S3 using COPY, you bypass these per-row overheads and achieve optimal throughput.
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 COPY command to load data from Amazon S3.
Why this is correct
Bulk loading is more efficient than small inserts.
- ✗
Define DISTKEY and SORTKEY on the table.
Why it's wrong here
These optimize queries, not inserts.
- ✗
Increase the number of nodes in the cluster.
Why it's wrong here
Scaling out doesn't optimize small inserts.
- ✗
Configure workload management (WLM) queues.
Why it's wrong here
WLM manages concurrency, not insert performance.
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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