Which design should a solutions architect recommend to provide a more scalable solution?
Use Amazon Kinesis Data Streams to ingest the data. Process the data using AWS Lambda functions.
Use Amazon API Gateway on top of the existing application. Create a usage plan with a quota limit for the third-party vendor.
Use Amazon Simple Notification Service (Amazon SNS) to ingest the data. Put the EC2 instances in an Auto Scaling group behind an Application Load Balancer.
Repackage the application as a container. Deploy the application using Amazon Elastic Container Service (Amazon ECS) using the EC2 launch type with an Auto Scaling group.
Explanations:
Using Amazon Kinesis Data Streams allows for scalable ingestion of data in near-real time, handling spikes in data volume. AWS Lambda functions can process data in a serverless manner, automatically scaling with the load, ensuring that data is not lost during peak times.
While Amazon API Gateway can help manage traffic and create usage plans, it does not inherently provide a scalable way to process incoming data, especially during high-volume spikes. It may still result in 503 errors if the underlying EC2 instances cannot handle the load.
Amazon SNS can be used for message ingestion, but it would not directly process the data. Without a processing mechanism that scales effectively, the EC2 instances may still become overwhelmed, leading to 503 errors. The use of an Auto Scaling group is good, but alone it may not address immediate processing capacity.
Repackaging the application as a container and deploying it with ECS can improve scalability. However, this option alone does not address the immediate need for buffering and processing the incoming data during spikes. It would require additional setup for load balancing and effective data ingestion.
I plan that the answer is:
Use Amazon Kinesis Data Streams to ingest the data. Process the data using AWS Lambda functions.