You need complex analytic SQL across billions of rows. Which store fits?
Why B is the answer
Amazon Redshift
Redshift is a columnar data warehouse.
In detail
- Redshift is built for complex analytics at scale.
- Choose DynamoDB for key lookups instead.
Use case
An illustrative scenario, not a real incident.
Analysts run complex joins across billions of rows, and Redshift's columnar design fits.
See it in different ways
flowchart TD
A{"Access pattern"} -->|"Analytics across columns"| B["Amazon Redshift"]
A -->|"Key lookups at low latency"| C["Amazon DynamoDB"]
A -->|"Files and a data lake"| D["Amazon S3"]
A -->|"Relational transactions"| E["Amazon RDS or Aurora"]mindmap
root((Stores))
Redshift
Complex analytics
DynamoDB
Fast key lookups
S3
Data lake
Athena
SQL on S3flowchart TD
Q{"What is the workload?"}
Q -->|"Analytic SQL"| A0["Amazon Redshift"]
Q -->|"Key lookups"| A1["Amazon DynamoDB"]Key point: Redshift suits complex analytic SQL.
Why the other options are wrong
- A. Amazon SQS, with one message per row
- SQS is a queue.
- C. Amazon DynamoDB, with a scan for each report
- Scanning DynamoDB is costly.
- D. Amazon ElastiCache, with a key for each report
- ElastiCache is a cache.