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 DynamoDProvision 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 increases management overhead and does not inherently improve scalability compared to using Lambda functions.
Changing to DynamoDB with DAX improves performance for read-heavy workloads but does not directly address the need for handling high volumes of data loading into a relational database like Aurora.
While using two Lambda functions can improve scalability, using SNS may not effectively manage the load and could introduce complexities in message delivery and ordering.
Setting up two Lambda functions with SQS allows for asynchronous processing and helps manage high volumes of data more efficiently, improving scalability and minimizing configuration effort.