In today’s data-driven landscape, organizations often require real-time data synchronization across multiple heterogeneous databases and systems. Change Data Capture (CDC) pipelines enable this by tracking and streaming database changes as they happen. Building a multi-source CDC pipeline—one that ingests change events from multiple distinct database sources—can be complex but highly rewarding.

This post explores how to architect such a pipeline using Debezium, an open-source CDC platform, and Apache Kafka, the leading distributed event streaming platform. We will dive deep into the technical setup, challenges, and best practices to build a scalable, fault-tolerant, and performant multi-source CDC pipeline.

Debezium acts as a CDC connector by monitoring database transaction logs (like MySQL binlogs, PostgreSQL WAL, MongoDB oplogs) and producing change events. These events are published to Kafka topics representing each database table or collection, enabling downstream systems to react in near real-time.

Apache Kafka serves as the backbone messaging system, providing durability, scalability, and stream processing capabilities. Using Kafka Connect, the Debezium connectors can be deployed in a distributed and scalable fashion.

Key benefits of combining Debezium with Kafka include:

  • Decoupling data producers and consumers
  • Fault tolerance with Kafka’s durable log storage
  • Scalability to handle high throughput from multiple sources
  • Exactly-once or at-least-once processing semantics with proper configuration

Designing a Multi-Source CDC Architecture

To build a multi-source CDC pipeline, consider the following architectural components:

  • Multiple Debezium Connectors: One connector per database source, configured to capture change events from that specific source.
  • Kafka Topics per Source and Table: Each Debezium connector writes to topics named by convention, e.g., dbserver1.inventory.customers.
  • Kafka Connect Distributed Cluster: Manages connector lifecycle and scalability.
  • Schema Registry: Use Confluent Schema Registry or similar to manage Avro/JSON schemas for CDC events, ensuring compatibility.
  • Stream Processing Layer: Optional Kafka Streams or ksqlDB for transformations, filtering, or enrichment.
  • Sink Connectors or Consumers: To deliver data into downstream systems such as Elasticsearch, data lakes, or analytics platforms.

Step-by-Step Setup of Multi-Source CDC Pipeline

  1. Prepare Your Databases
    Enable CDC prerequisites on all source databases. For example, enable binary logging with the correct format and permissions in MySQL or logical replication in PostgreSQL.

  2. Configure Debezium Connectors
    For each source, deploy a Debezium connector with unique connector.name and database.server.name. This allows events to be segregated by source in Kafka.

    Example snippet for MySQL connector:

    {
  "name": "mysql-connector-1",
  "config": {
    "connector.class": "io.debezium.connector.mysql.MySqlConnector",
    "database.hostname": "mysql1.example.com",
    "database.port": "3306",
    "database.user": "debezium",
    "database.password": "password",
    "database.server.id": "101",
    "database.server.name": "mysql1",
    "database.include.list": "inventory",
    "include.schema.changes": "false"
  }
}

  3. Deploy Kafka Connect in Distributed Mode
    Launch a Kafka Connect cluster configured to run Debezium connectors. This ensures high availability and load balancing.

  4. Enable Schema Registry Integration
    Configure Debezium to serialize events using Avro or JSON Schema with Schema Registry, facilitating schema evolution and compatibility.

  5. Monitor and Manage Connectors
    Use Kafka Connect REST API or UI tools to monitor connector status, restart failed connectors, and scale workers.

Handling Challenges in Multi-Source CDC Pipelines

  • Event Ordering and Consistency
    CDC events from different sources are independent streams. Consumers must handle eventual consistency and potential out-of-order events across sources.

  • Schema Evolution
    As source schemas evolve, Debezium and Schema Registry ensure backward/forward compatibility, but careful versioning and testing are critical.

  • Idempotency and Exactly-Once Processing
    Use Kafka transactional APIs or downstream idempotent consumers to avoid duplicates caused by retries.

  • Resource Management
    Multiple connectors increase load on Kafka Connect and Kafka brokers. Proper partition sizing, connector tuning, and monitoring are essential.

Best Practices for Production-Ready Multi-Source CDC Pipelines

  • Isolate Connectors per Source
    Keep connectors independent to simplify debugging and scaling.

  • Use Unique Kafka Topics Namespaces
    Naming conventions like <server-name>.<database>.<table> prevent topic collisions.

  • Implement Centralized Monitoring and Alerting
    Leverage tools like Prometheus and Grafana to monitor connector lag, error rates, and throughput.

  • Secure Your Pipeline
    Enable SSL/TLS for Kafka and Kafka Connect, and use ACLs to restrict access.

  • Test Failover and Recovery
    Simulate connector failures and Kafka broker outages to validate pipeline resilience.

Conclusion

Building a multi-source CDC pipeline with Debezium and Apache Kafka unlocks powerful real-time data integration capabilities across diverse systems. By carefully designing connectors, managing schemas, and scaling the Kafka ecosystem, organizations can achieve scalable, fault-tolerant, and consistent data streaming architectures.

This approach not only simplifies data synchronization but also lays the foundation for advanced analytics, monitoring, and event-driven applications. Embrace the power of CDC and Kafka to transform how your organization handles data in motion.