Retrieve recent sensor values by device ID and time range.
Write at high volume and remove or archive data automatically after its retention period.
Which data-store design is most appropriate?
Use a DynamoDB composite key for device and time-range queries and configure TTL for expiry handling.
Detailed explanation
Redshift suits analytics but does not provide a low-latency per-device query and TTL deletion model.
Redshift suits analytics but does not provide a low-latency per-device query and TTL deletion model.
A device partition key and time sort key support range Query, while TTL handles expiry asynchronously.
A device partition key and time sort key support range Query, while TTL handles expiry asynchronously.
Many small objects and full scans are inefficient for low-latency device queries.
Many small objects and full scans are inefficient for low-latency device queries.
Log retention does not provide a keyed query or application data deletion policy.
Log retention does not provide a keyed query or application data deletion policy.
Check official documentation
Compare the following points with the official documentation.
DynamoDB sort key、Query、TTL、時系列データ設計の公式説明を確認する。
What to confirm
ファイル・分析・ログ保存と、時系列キーアクセスDBの違いを説明できる。
Key points
- DynamoDB
- Composite key
- Time series
- TTL
Notes
- Environment: AWS公式DEA-C01試験ガイドと各サービスの公式ドキュメントの確認
- Confirm current AWS service behavior in the official DEA-C01 documentation.
Foundation review
Time-series access
Partition distribution, time precision, and range boundaries determine the key design.
Retention behavior
TTL is asynchronous, so archival, deletion visibility, and downstream consistency need separate handling.