Several years of sales data must support reports that aggregate selected columns over many rows.
Transactional processing and low-latency updates of individual items are not requirements.
Which database type is most appropriate?
Use column-oriented Amazon Redshift for large analytical scans and aggregations.
Detailed explanation
DynamoDB Scan is not the primary pattern for large column aggregations and can increase read cost and latency.
DynamoDB Scan is not the primary pattern for large column aggregations and can increase read cost and latency.
An in-memory cache is not a durable source of record for long-term analytical history.
An in-memory cache is not a durable source of record for long-term analytical history.
Repeated full-row retrieval from a transactional database increases analysis load and data transfer.
Repeated full-row retrieval from a transactional database increases analysis load and data transfer.
Redshift is designed for analytical processing and can scan only the columns needed for large aggregations.
Redshift is designed for analytical processing and can scan only the columns needed for large aggregations.
Check official documentation
Compare the following points with the official documentation.
Amazon Redshift columnar storage and analyticsの公式説明を確認する。
What to confirm
トランザクション向け行指向DB、Key-Value、分析向け列指向DBの用途を比較できる。
Key points
- Amazon Redshift
- Columnar format
- Analytical workload
Notes
- Environment:
- Confirm current behavior in the official SAA-C03 guide and AWS service documentation.
Foundation review
Access unit
Analytics often selects columns, while transactions update individual rows.
Service responsibility
A cache or key-value store is not a durable analytical system of record.