
Evolving Event Schemas Without Breaking Consumers
How to add fields, rename properties, and restructure events in async systems without coordinating deployments or silently breaking downstream consumers.
Tags
8 articles

How to add fields, rename properties, and restructure events in async systems without coordinating deployments or silently breaking downstream consumers.

How the transactional outbox pattern eliminates the dual-write problem and guarantees reliable event publishing without coupling your database to your message broker.

Practical lessons from designing and operating microservices at scale — event-driven communication, service boundaries, and the patterns that survived production.

Build real-time data pipelines using Apache Kafka with TypeScript consumers and producers, covering partitioning strategies, consumer groups, exactly-once semantics, and dead letter queues for reliable event streaming.

Design event mesh architectures that route events across multiple clouds and regions with dynamic topic routing, protocol bridging, schema governance, and eventually consistent delivery guarantees.

Build CDC pipelines that stream database changes to downstream systems in real time, covering log-based capture with Debezium, event formatting, schema evolution handling, and exactly-once delivery guarantees.

A step-by-step tutorial for building an event-driven microservice using Node.js, RabbitMQ, and TypeScript — covering event publishing, consumption, dead letter queues, and idempotent processing.

Event-driven systems decouple producers from consumers through asynchronous message passing — but eventual consistency, ordering guarantees, and idempotency introduce real complexity.