Question
A company has an application that ingests incoming messages. Dozens of other applications and microservices then quickly consume these messages. The number of messages varies drastically and sometimes increases suddenly to 100,000 each second. The company wants to decouple the solution and increase scalability.
Which solution meets these requirements?
- A. Persist the messages to Amazon Kinesis Data Analytics. Configure the consumer applications to read and process the messages.
- B. Deploy the ingestion application on Amazon EC2 instances in an Auto Scaling group to scale the number of EC2 instances based on CPU metrics.
- C. 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.
- D. Publish the messages to an Amazon Simple Notification Service (Amazon SNS) topic with multiple Amazon Simple Queue Service (Amazon SQS) subscriptions. Configure the consumer applications to process the messages from the queues. Correct
Explanation
The correct answer is D because Amazon SNS can handle high message throughput and fan out messages to multiple subscribers, such as SQS queues, which can then be processed by consumer applications. This setup decouples the system and allows for scalable consumption of messages.
