Which solution will meet these requirements?
Refactor the Lambda function code to Apache Tomcat code that runs on Amazon EC2 instances. Connect the database by using native Java Database Connectivity (JDBC) drivers.
Change the platform from Aurora to Amazon DynamoDB. Provision a DynamoDB Accelerator (DAX) cluster. Use the DAX client SDK to point the existing DynamoDB API calls at the DAX cluster.
Set up two Lambda functions. Configure one function to receive the information. Configure the other function to load the information into the database. Integrate the Lambda functions by using Amazon Simple Notification Service (Amazon SNS).
Set up two Lambda functions. Configure one function to receive the information. Configure the other function to load the information into the database. Integrate the Lambda functions by using an Amazon Simple Queue Service (Amazon SQS) queue.
Explanations:
Refactoring to Apache Tomcat on EC2 introduces additional complexity and scaling challenges compared to using Lambda. It also requires more configuration effort.
Switching to DynamoDB does not align with the need to store data in an Amazon Aurora PostgreSQL database. Additionally, using DAX may not help with database scalability.
Using SNS for integration introduces unnecessary complexity and does not effectively improve the scalability of Lambda for high-volume database operations.
Using SQS to decouple the Lambda functions provides better scalability, as Lambda can handle more requests in parallel by processing messages from the SQS queue.
I weigh that the answer is:
Set up two Lambda functions. Configure one function to receive the information. Configure the other function to load the information into the database. Integrate the Lambda functions by using an Amazon Simple Queue Service (Amazon SQS) queue.