Analysis:
The solution must handle variable sales volumes, preserve transaction information, and store data in an Amazon Aurora database with minimal operational overhead. UsingAPI Gateway, AWS Lambda, and SQSis the best option because it provides scalability, reliability, and resilience.
Why Option A is Correct:
API Gateway: Serves as an entry point for transaction data in a serverless, scalable manner.
AWS Lambda: Processes the transactions and sends them to Amazon SQS for queuing.
Amazon SQS: Buffers the transaction data, ensuring durability and resilience against spikes in transaction volume.
Second Lambda Function: Processes messages from the SQS queue and updates the Aurora database, decoupling the workflow for better scalability.
Dead-Letter Queue (DLQ): Ensures failed transactions are logged for later debugging or reprocessing.
Why Other Options Are Not Ideal:
Option B:
Using an ALB with ECS on EC2 introduces operational overhead, such as managing EC2 instances and scaling ECS tasks.Not cost-effective.
Option C:
EKS is highly operationally intensive and requires Kubernetes cluster management, which is unnecessary for this use case.Too complex.
Option D:
Amazon Data Firehose and DMS are not designed for real-time transactional workflows. They are better suited for data analytics pipelines.Not suitable.
AWS References:
Amazon API Gateway:AWS Documentation - API Gateway
AWS Lambda:AWS Documentation - Lambda
Amazon SQS:AWS Documentation - SQS
Amazon Aurora:AWS Documentation - Aurora
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