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Answer: Store JSON in Amazon S3, process with an AWS Lambda-triggered Python function, and store results in Amazon Aurora.
The correct answer is B. This solution involves placing the JSON documents in an Amazon S3 bucket and using an AWS Lambda function to automatically process the documents as they arrive. This setup maximizes scalability because Lambda functions can scale automatically in response to incoming events. Additionally, it minimizes operational overhead since there are no servers to manage, and S3 provides high availability and durability for the stored data. The results can then be stored in an Amazon Aurora DB cluster, which is highly available and scalable as well.
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A company seeks to migrate a Python application processing JSON documents to AWS Cloud, requiring a solution that ensures high availability, scalability, and minimal operational overhead. Which approach should be adopted?
A
Utilize Amazon S3 for JSON storage, process documents with Python on EC2 instances, and use Amazon Aurora for results storage.
B
Store JSON in Amazon S3, process with an AWS Lambda-triggered Python function, and store results in Amazon Aurora.
C
Use Amazon EBS with Multi-Attach for JSON storage, process with Python on EC2, and store results in Amazon RDS.
D
Queue JSON in Amazon SQS, process messages with a Python container on Amazon ECS with EC2, and store results in Amazon RDS.