Great explanation of why Event-Driven Architecture exists instead of jumping straight into Kafka or RabbitMQ. I especially liked the point that architectural problems should drive technology choices, not the other way around.
One thing I'd add is that as teams adopt EDA, designing events themselves becomes just as important as choosing the broker. Clear event naming, versioning, idempotent consumers, and observability can make the difference between a scalable system and one that's difficult to debug.
Curious to hear your thoughts do you usually recommend starting with a simple message queue like SQS/RabbitMQ and moving to Kafka only when throughput or streaming requirements justify it?
Thanks! From my experience, yes—AI is one of the areas where Event-Driven Architecture fits really well.
We recently used it for bulk LLM processing on database records. Instead of processing 1,000 records one by one, each record became an independent event that workers processed in parallel. It made the pipeline much faster and much easier to scale.
That said, I'd still choose it only when the problem justifies it. Not every AI application needs an event-driven architecture.
Great explanation of why Event-Driven Architecture exists instead of jumping straight into Kafka or RabbitMQ. I especially liked the point that architectural problems should drive technology choices, not the other way around.
One thing I'd add is that as teams adopt EDA, designing events themselves becomes just as important as choosing the broker. Clear event naming, versioning, idempotent consumers, and observability can make the difference between a scalable system and one that's difficult to debug.
Curious to hear your thoughts do you usually recommend starting with a simple message queue like SQS/RabbitMQ and moving to Kafka only when throughput or streaming requirements justify it?