Streaming data from MySQL to real-time processing systems like Apache Kafka has become a critical architecture for modern data-driven applications. As enterprises demand immediate insights and event-driven workflows, integrating MySQL databases with Kafka enables scalable, fault-tolerant, and low-latency data pipelines. This post dives deep into the technical aspects of setting up this integration, focusing on Change Data Capture (CDC), Kafka Connect, and ensuring data consistency for intermediate and advanced users.

Why Stream Data from MySQL to Kafka?

MySQL remains one of the most popular relational databases, powering millions of applications. However, traditional batch ETL processes introduce latency, making it challenging to perform real-time analytics or trigger immediate actions on data changes. Apache Kafka, a distributed streaming platform, excels in handling continuous streams of data with high throughput and low latency. By streaming MySQL data changes into Kafka topics, you unlock:

  • Real-time analytics and monitoring
  • Event-driven microservices architectures
  • Decoupled and scalable data pipelines
  • Improved fault tolerance and replayability

Core Concepts: Change Data Capture (CDC) and Kafka Connect

At the heart of MySQL to Kafka streaming lies Change Data Capture (CDC), a technique that captures row-level changes (inserts, updates, deletes) in MySQL and streams them to Kafka topics. CDC ensures that every data mutation in MySQL is reflected downstream in real time.

Kafka Connect is the official framework for scalable and reliable Kafka integrations. Using Kafka Connect with Debezium – an open-source CDC connector – allows you to capture MySQL binlog events and publish them directly into Kafka without custom coding.

Setting Up MySQL for CDC

To enable CDC, you must configure MySQL to generate binary logs (binlog), which record all database changes. Key configurations include:

  • Setting binlog_format=ROW to capture row-level changes instead of statement-level logs.
  • Enabling binary logging by setting log_bin=mysql-bin.
  • Configuring a unique server ID with server_id to identify the MySQL instance.
  • Granting replication privileges to the Kafka Connect user: 
    GRANT REPLICATION SLAVE, REPLICATION CLIENT ON *.* TO 'debezium'@'%' IDENTIFIED BY 'password';

These settings ensure Debezium can stream changes reliably from the MySQL binlog.

Deploying Kafka Connect with Debezium MySQL Connector

After MySQL is prepared, deploy Kafka Connect in distributed mode for production readiness. Then, configure the Debezium MySQL connector with a JSON configuration file specifying:

  • MySQL connection details (hostname, port, user, password)
  • The database whitelist or blacklist
  • Kafka topic naming strategies
  • Offsets storage and error handling policies

Example configuration snippet:

{  
  "name": "mysql-connector",  
  "config": {  
    "connector.class": "io.debezium.connector.mysql.MySqlConnector",  
    "database.hostname": "mysql-host",  
    "database.port": "3306",  
    "database.user": "debezium",  
    "database.password": "password",  
    "database.server.id": "184054",  
    "database.server.name": "mysql-server",  
    "database.include.list": "inventory",  
    "database.history.kafka.bootstrap.servers": "kafka:9092",  
    "database.history.kafka.topic": "schema-changes.inventory"  
  }  
}

Once the connector is started, it captures MySQL binlog events and streams them into Kafka topics with a structured event schema.

Consuming and Processing MySQL Data in Kafka

Kafka topics now contain real-time data change events in formats like JSON or Avro. Downstream consumers can leverage this data for:

  • Real-time dashboards and alerting systems
  • Enrichment and transformation pipelines using Kafka Streams or ksqlDB
  • Feeding data lakes or Elasticsearch for search indexing
  • Triggering business workflows in microservices

Because Kafka supports replay and partitioning, you can scale consumers horizontally and reprocess data as needed.

Handling Schema Evolution and Data Consistency

One technical challenge is managing schema changes in MySQL tables. Debezium integrates with Kafka Schema Registry to track schema versions, ensuring consumers adapt to evolving data structures without downtime.

Additionally, exactly-once processing semantics can be achieved by combining Kafka transactional APIs with idempotent consumers. This is critical for maintaining data consistency in downstream applications.

Performance Considerations and Best Practices

To maximize throughput and reliability:

  • Tune MySQL binlog retention to balance storage and CDC lag.
  • Optimize Kafka partitions and replication factors for fault tolerance and parallelism.
  • Monitor Kafka Connect worker metrics and configure retry policies for resilience.
  • Secure data with SSL/TLS and authentication mechanisms for both MySQL and Kafka.

Regularly test failover scenarios and monitor lag between MySQL changes and Kafka topic offsets to ensure real-time guarantees.

Conclusion

Integrating MySQL with Apache Kafka unlocks powerful real-time data processing capabilities essential for modern architectures. By leveraging CDC with Debezium and Kafka Connect, developers can build scalable, fault-tolerant pipelines that reflect database changes instantly. With proper configuration, schema management, and performance tuning, this integration forms the backbone of event-driven microservices, real-time analytics, and next-generation data platforms.

Embrace this integration strategy to transform your MySQL data into actionable streaming insights, driving faster decisions and innovative applications.