I’ve been thinking about event-driven architectures lately because they solve so many real-world problems we face in distributed systems. When services need to communicate without tight coupling, when we need resilience against failures, and when we want systems that can scale independently—event-driven patterns provide elegant solutions. That’s why I want to share my experience building these systems with Apache Kafka and Spring Cloud Stream.

Have you ever wondered how large systems handle millions of events while maintaining data consistency across services?

Let me show you how we can build a simple but powerful event-driven system. We’ll start with a basic setup using Spring Cloud Stream and Kafka. Here’s how you define a message producer:

@Bean
public Supplier<OrderEvent> orderProducer() {
    return () -> {
        OrderEvent event = new OrderEvent(UUID.randomUUID(), "NEW_ORDER");
        log.info("Producing order event: {}", event);
        return event;
    };
}

And here’s the consumer side:

@Bean
public Consumer<OrderEvent> orderConsumer() {
    return event -> {
        log.info("Consuming order event: {}", event);
        // Process the order logic here
    };
}

What happens when something goes wrong in your event processing? Do you have a strategy for handling failures?

Distributed tracing becomes crucial when debugging these systems. With Spring Cloud Sleuth and Zipkin, we can track requests across service boundaries. Here’s how you might configure tracing:

spring:
  zipkin:
    base-url: http://localhost:9411
  sleuth:
    sampler:
      probability: 1.0

The beauty of this approach is how it automatically correlates traces across services. When an order event moves from the order service to inventory service and then to notification service, we get a complete picture of the transaction flow.

But what about error handling? Kafka’s dead letter topics provide an excellent mechanism for dealing with problematic messages:

@Bean
public Consumer<Message<OrderEvent>> orderProcessor() {
    return message -> {
        try {
            OrderEvent event = message.getPayload();
            processOrder(event);
        } catch (Exception e) {
            log.error("Processing failed, sending to DLQ", e);
            // Message will be automatically routed to DLQ
            throw new RuntimeException("Processing error", e);
        }
    };
}

This pattern ensures that one bad message doesn’t block your entire stream while giving you a place to inspect and reprocess failed events.

Monitoring is another critical aspect. Have you considered how you’ll know if your event-driven system is healthy?

With Micrometer and Prometheus, we can track important metrics like message processing rates, error counts, and latency:

@Bean
public MeterRegistryCustomizer<MeterRegistry> metricsCommonTags() {
    return registry -> registry.config().commonTags(
        "application", "order-service",
        "environment", "production"
    );
}

Building event-driven microservices requires thinking differently about data flow and service boundaries. It’s not just about technology choices—it’s about designing systems that are resilient, scalable, and maintainable.

The patterns we’ve discussed here form the foundation of modern distributed systems. They enable teams to work independently while maintaining system reliability. What challenges have you faced with event-driven architectures?

I’d love to hear your thoughts and experiences with these patterns. If you found this useful, please share it with others who might benefit, and feel free to leave comments about your own journey with event-driven systems.