The Difference Between "Working" and "Resilient" Architecture Production systems rarely stay on the happy path for long. In my experience, the shift from a basic pipeline to a resilient one lies in how you handle failure and scale. I’ve put together a look at a serverless, event-driven ETL pattern I recently designed. It focuses on moving away from brittle, monolithic scripts toward a decoupled architecture using Amazon EventBridge and SQS to ensure reliability and observability. Resilience isn't an afterthought; it’s an architectural choice. Read the full breakdown on Medium: https://lnkd.in/eZSMwFFm
Well said. A system that “works” on the happy path isn’t production-ready. Designing for failure, scale, and observability from day one is what makes architecture resilient. Event-driven patterns with EventBridge and SQS are a solid way to decouple workflows and absorb failure gracefully.
Dipal K.
4w
EDA helps scale and handle failure independently
Shihab Ullah
2w
Nice demonstration of using EventBridge and SQS effectively. On a separate note, I have created a production grade lambda architecture based AWS ELT pipeline from wiki stream data using S3Tables: https://github.com/mdshihabullah/wikistream-event-data-pipeline-aws
Very nice and well articulated article Rishu Gandhi
This is a great piece, I read the full article. What stood out most is how explicitly you designed for failure as a first-class path: DLQs, retries, idempotency, and state handling aren’t add-ons here, they’re the architecture. That’s the real difference between something that runs and something that keeps running. I also liked how the diagrams make the control flow under partial failure easy to reason about especially in event-driven pipelines where “silent success” is often the most dangerous mode. Which resilience mechanism tends to deliver the biggest reliability win in practice, DLQs, idempotency, or tighter observability around retries and timeouts?