What should a solutions architect recommend for communication between the microservices?
Create an Amazon Simple Queue Service (Amazon SQS) queue. Add code to the data producers, and send data to the queue. Add code to the data consumers to process data from the queue.
Create an Amazon Simple Notification Service (Amazon SNS) topic. Add code to the data producers, and publish notifications to the topic. Add code to the data consumers to subscribe to the topic.
Create an AWS Lambda function to pass messages. Add code to the data producers to call the Lambda function with a data object. Add code to the data consumers to receive a data object that is passed from the Lambda function.
Create an Amazon DynamoDB table. Enable DynamoDB Streams. Add code to the data producers to insert data into the table. Add code to the data consumers to use the DynamoDB Streams API to detect new table entries and retrieve the data.
Explanations:
Amazon SQS provides a reliable and scalable way to decouple microservices, allowing producers to send messages to a queue that consumers can process asynchronously. This fits well with the requirement that the order of results does not matter.
Amazon SNS is designed for pub/sub messaging but does not guarantee message delivery to consumers, which may lead to data loss if a consumer is not available. It’s less suitable for this use case where reliable processing is needed.
Using an AWS Lambda function to pass messages introduces additional complexity and latency. It may not efficiently handle high-throughput scenarios, as Lambda is better suited for event-driven architectures rather than direct service-to-service communication.
While DynamoDB Streams can be used for real-time processing, it adds unnecessary complexity for the communication between microservices. The primary role of DynamoDB is data storage, not message passing, making it less optimal than SQS for this purpose.