AWS Certified Solutions Architect – Associate (SAA-C03)Design Cost-Optimized ArchitecturesMedium
A financial services company is developing a new application that will process high volumes of financial transactions. The application requires a database that can handle millions of requests per second, with consistent single-digit millisecond latency. Data must be highly available and durable, and the solution needs to be cost-optimized for both provisioned capacity and on-demand scaling. Which AWS database service is the most cost-effective solution for these requirements?
- AAmazon RDS for PostgreSQL with Provisioned IOPS
- BAmazon DynamoDB with On-Demand capacity mode
- CAmazon Aurora Serverless v2
- DAmazon Redshift with Concurrency Scaling
Show answer & explanationAnswer & explanation
Correct answer: B. Amazon DynamoDB with On-Demand capacity mode
Amazon DynamoDB with On-Demand capacity mode offers consistent single-digit millisecond latency, high availability, and durability, while automatically adjusting to workload changes, making it cost-effective for fluctuating high-volume transaction processing without over-provisioning.
Why the other options are wrong
- A. Amazon RDS for PostgreSQL can achieve high performance but typically requires more manual scaling and can be less cost-effective for extreme, fluctuating request rates compared to DynamoDB On-Demand.
- C. Amazon Aurora Serverless v2 is cost-effective for intermittent or unpredictable workloads, but for consistent millions of requests per second, DynamoDB's architecture is generally more suited and cost-optimized for that specific scale and type of workload (NoSQL key-value store).
- D. Amazon Redshift is a data warehousing service optimized for analytical queries, not high-volume transactional processing with single-digit millisecond latency requirements.
DynamoDB On-Demand Capacity
A DynamoDB capacity mode that charges for the data reads and writes your application performs, automatically scaling to accommodate workload changes.
- Pay-per-request pricing model.
- Automatically scales up and down for fluctuating workloads.
- Eliminates the need for capacity planning.
- Suitable for unpredictable traffic patterns.
Memory trick: Dynamic Demands, Optimal Dollars.