Which solution will meet these requirements?
Place the JSON documents in an Amazon S3 bucket. Run the Python code on multiple Amazon EC2 instances to process the documents. Store the results in an Amazon Aurora DB cluster.
Place the JSON documents in an Amazon S3 bucket. Create an AWS Lambda function that runs the Python code to process the documents as they arrive in the S3 bucket. Store the results in an Amazon Aurora DB cluster.
Place the JSON documents in an Amazon Elastic Block Store (Amazon EBS) volume. Use the EBS Multi-Attach feature to attach the volume to multiple Amazon EC2 instances. Run the Python code on the EC2 instances to process the documents. Store the results on an Amazon RDS DB instance.
Place the JSON documents in an Amazon Simple Queue Service (Amazon SQS) queue as messages. Deploy the Python code as a container on an Amazon Elastic Container Service (Amazon ECS) cluster that is configured with the Amazon EC2 launch type. Use the container to process the SQS messages. Store the results on an Amazon RDS DB instance.
Explanations:
While this solution utilizes Amazon S3 and Aurora, running multiple EC2 instances increases operational overhead and does not maximize scalability as effectively as serverless options.
This solution leverages Amazon S3 for storage and AWS Lambda for processing, which is highly scalable and minimizes operational overhead, making it ideal for processing documents as they arrive. Aurora DB is also a good choice for storage.
Using EBS volumes limits scalability and flexibility, as EBS volumes cannot be shared among multiple EC2 instances efficiently. This setup would also increase operational overhead compared to serverless options.
While SQS provides a good message queuing solution, deploying containers on ECS with EC2 requires management of the underlying infrastructure, which increases operational overhead and may not be as scalable as serverless solutions like Lambda.
I estimate that the answer is:
Place the JSON documents in an Amazon S3 bucket. Create an AWS Lambda function that runs the Python code to process the documents as they arrive in the S3 bucket. Store the results in an Amazon Aurora DB cluster.