Join several years of sales across multiple tables while many BI users run aggregates.
Use column-oriented scans and manage concurrent analytical SQL workloads.
Which data store is most appropriate?
Use Amazon Redshift as a columnar, distributed analytical data warehouse for large scans and joins.
Detailed explanation
A volatile cache does not durably manage years of analytical data.
A volatile cache does not durably manage years of analytical data.
DynamoDB excels at key access but is not intended for arbitrary large joins and aggregates.
DynamoDB excels at key access but is not intended for arbitrary large joins and aggregates.
A FIFO queue provides ordered messaging, not historical SQL analytics.
A FIFO queue provides ordered messaging, not historical SQL analytics.
Redshift is designed for distributed columnar scans, joins, and aggregations.
Redshift is designed for distributed columnar scans, joins, and aggregations.
Check official documentation
Compare the following points with the official documentation.
Amazon Redshiftの列指向、MPP、分散、ワークロード管理の公式説明を確認する。
What to confirm
分析ウェアハウスとキー・バリューDB・キュー・キャッシュを区別できる。
Key points
- Redshift
- Columnar storage
- MPP
- Analytical SQL
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
Analytical workload
Join size, scan volume, aggregation, and concurrency influence the store choice.
Load and query design
COPY layout, distribution, sort keys, and workload management work together.