Change Data Capture

Change Data Capture Overview

At its core, change data capture is about doing one thing well inside your Apache Kafka project. Once you understand the pattern, you can apply it consistently across features and teams.

Good change data capture pays off across the whole codebase: fewer surprises, easier testing, and smoother onboarding. The snippet below is a solid starting point.

// each service reacts to events and emits new ones
await consumer.subscribe({ topic: 'payment-completed' });
await consumer.run({
  eachMessage: async ({ message }) => {
    const payment = JSON.parse(message.value.toString());
    await producer.send({
      topic: 'order-confirmed',
      messages: [{ key: payment.orderId, value: JSON.stringify(payment) }],
    });
  },
});

Event-driven services stay decoupled by reacting to and emitting Kafka events.

Change Data Capture Example

import { Kafka } from 'kafkajs';

const kafka = new Kafka({ clientId: 'app', brokers: ['localhost:9092'] });
const producer = kafka.producer();
const consumer = kafka.consumer({ groupId: 'group' });

• Start from a minimal Change Data Capture example and grow it only as needed.
• Keep configuration explicit so Change Data Capture behaves the same in every environment.
• Name things clearly so teammates understand your Change Data Capture at a glance.
• Add tests around Change Data Capture early to lock in expected behaviour.

Apache Kafka Cheatsheet

Handy KafkaJS reference related to change data capture.

How Change Data Capture Works in Apache Kafka

Change Data Capture builds on Kafka's log-based design, where producers append events to partitioned topics and consumer groups read them independently, tracking their own offsets.

Event-driven services stay decoupled by reacting to and emitting Kafka events.

• Topics are split into partitions for parallelism and ordering per key.
• Producers choose a partition, usually by message key.
• Consumer groups share partitions so work scales horizontally.
• Offsets record how far each group has read.

Practical Guidance for Change Data Capture

In production, change data capture needs attention to delivery guarantees, retries, and observability. Make handlers idempotent and monitor consumer lag closely.

Common Mistakes

• Copying change data capture snippets without understanding what each line does.
• Skipping error handling and edge cases when wiring up change data capture.
• Leaving change data capture untested, so regressions slip into production.
• Over-engineering change data capture before you actually need the extra flexibility.

Key Takeaways

• Change Data Capture is a core part of working effectively with Apache Kafka.
• Start small and keep change data capture focused on a single responsibility.
• Apply consistent patterns so change data capture scales across your project.
• Test and document change data capture to keep it maintainable over time.