Explain Kafka vs RabbitMQ vs SQS.

Apache Kafka, RabbitMQ, and Amazon SQS are messaging systems that help backend applications communicate asynchronously, but each is optimized for different use cases.

When to Use

• Kafka: Best for real-time event streaming, high-throughput pipelines, and analytics (e.g., fintech fraud detection or IoT telemetry).
• RabbitMQ: Ideal for microservice task queues, workflows, and complex routing (e.g., e-commerce order processing).
• SQS: Perfect for simple, serverless decoupling on AWS with auto-scaling and minimal operational overhead.

Example

An online store might stream click events to Kafka for analytics, use RabbitMQ to handle payment workflows, and rely on SQS to queue confirmation emails reliably.

Want to go deeper?

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Why Is It Important

Picking the right tool ensures your system balances throughput, reliability, and scalability, avoiding costly redesigns later.

Interview Tips

Frame answers around trade-offs: Kafka’s throughput vs. RabbitMQ’s routing flexibility vs. SQS’s managed simplicity. Use real-world examples for credibility.

Trade-offs

• Kafka: High throughput, but complex cluster ops.
• RabbitMQ: Flexible routing, but lower scalability.
• SQS: Fully managed, but AWS lock-in and less control.

Pitfalls

Don’t use Kafka for lightweight tasks, avoid RabbitMQ for massive event streams, and remember SQS delivers at-least-once, so handle duplicates.