Which solution meets these requirements?
Persist the messages to Amazon Kinesis Data Analytics. Configure the consumer applications to read and process the messages.
Deploy the ingestion application on Amazon EC2 instances in an Auto Scaling group to scale the number of EC2 instances based on CPU metrics.
Write the messages to Amazon Kinesis Data Streams with a single shard. Use an AWS Lambda function to preprocess messages and store them in Amazon DynamoDB. Configure the consumer applications to read from DynamoDB to process the messages.
Publish the messages to an Amazon Simple Notification Service (Amazon SNS) topic with multiple Amazon Simple Queue Service (Amazon SOS) subscriptions. Configure the consumer applications to process the messages from the queues.
Explanations:
Amazon Kinesis Data Analytics is primarily for processing and analyzing streaming data, not for message ingestion or decoupling applications. It does not directly facilitate message consumption by multiple services.
Deploying on EC2 with Auto Scaling can help with scalability, but it does not decouple the components effectively. It ties the message ingestion tightly to EC2 instances and does not handle sudden spikes in message volume efficiently.
While using Kinesis Data Streams is a scalable solution, a single shard is a limitation that could create bottlenecks under high load (100,000 messages per second). Additionally, processing through DynamoDB could introduce delays.
Using Amazon SNS with multiple SQS subscriptions effectively decouples the message producers from consumers and allows for scalable message processing. This architecture can handle sudden spikes in message volume efficiently by distributing messages across
It appears that the answer is:
Publish the messages to an Amazon Simple Notification Service (Amazon SNS) topic with multiple Amazon Simple Queue Service (Amazon SOS) subscriptions. Configure the consumer applications to process the messages from the queues.