DBS-C01 Workload-Specific Database Design Practice Question
A startup is building a real-time analytics dashboard on AWS. The data arrives as time-series events from IoT devices at a rate of 10,000 writes per second. Each event is approximately 1 KB. The dashboard requires sub-second query latency for the last hour of data and must support ad-hoc analytical queries on historical data spanning months. The team needs to design a cost-effective database solution. Which combination of AWS services should be used?
⚠ Common exam trap
Test-takers frequently choose ElastiCache or Redshift for real-time performance, overlooking that DynamoDB with DAX is purpose-built for high-throughput, low-latency key-value access and that S3 with Athena is the most cost-effective serverless option for infrequent analytical queries on large historical datasets.
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
✓
Amazon DynamoDB with DynamoDB Accelerator (DAX) for real-time queries, and Amazon S3 with Amazon Athena for historical analytics.
DynamoDB with DAX provides microsecond to sub-millisecond latency for real-time queries on the last hour of data, while S3 with Athena offers a cost-effective serverless solution for ad-hoc analytical queries on historical data spanning months. DynamoDB's time-to-live (TTL) feature can automatically expire data older than one hour, keeping the hot dataset small and performant, and Athena's pay-per-query pricing avoids the cost of maintaining a separate analytics cluster.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Amazon ElastiCache for Redis for real-time queries, and Amazon OpenSearch Service for historical analytics.
Why it's wrong here
ElastiCache is not designed for 10,000 writes per second of 1 KB events, and OpenSearch is not cost-effective for ad-hoc analytical queries on months of data.
- ✓
Amazon DynamoDB with DynamoDB Accelerator (DAX) for real-time queries, and Amazon S3 with Amazon Athena for historical analytics.
Why this is correct
DynamoDB handles high write throughput, DAX provides sub-second reads, and S3 with Athena allows cost-effective ad-hoc queries on historical data.
- ✗
Amazon Redshift for both real-time and historical queries, using auto-scaling and materialized views.
Why it's wrong here
Redshift is optimized for complex analytical queries but not for sub-second real-time queries on fast-changing data.
- ✗
Amazon RDS for PostgreSQL with read replicas for real-time queries, and Amazon Redshift for historical analytics.
Why it's wrong here
RDS PostgreSQL cannot handle 10,000 writes per second, and read replicas add latency. Redshift is not needed if S3+Athena suffices.
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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