Building a Real-Time Loyalty Engine on AWS: Kafka CDC, Lambda & Aurora in Production

Real-time event-driven systems are easy to sketch on a whiteboard. Idempotent, fault-tolerant, cost-efficient, and maintainable by design, not by accident. Getting them right in production is a different story entirely.

This session shares hard-won lessons from building a production serverless pipeline on AWS that processes high-volume CDC events from Kafka, evaluates business rules against accumulated state, and persists results to a relational database, all without managing servers.

We’ll cover the decisions that actually matter at this layer: how to filter noise before it reaches your business logic, how to design idempotency that survives restarts, retries, and clock drift, when to reach for DynamoDB versus a relational database in the same pipeline, how Kafka and SQS failure semantics differ fundamentally, and why that changes how you write error handling code.

The goal isn’t to walk you through one specific architecture. It’s to leave you with a set of patterns and mental models you can apply the next time you’re facing a stream of real-time events that need to be processed reliably, evaluated against business rules, and persisted without losing a single one.

Takeaways:

• A practical approach to end-to-end idempotency across Kafka, SQS FIFO, DynamoDB, and Aurora

• When DynamoDB and Aurora belong in the same serverless pipeline and why

• How to build a fully serverless event-driven pipeline that doesn’t fall apart when it actually matters

• A generalized gate/counter pattern for real-time rule evaluation against accumulated state