Building a CDC Data Pipeline with Debezium and Kafka

Change Data Capture (CDC) has become an essential pattern for modern data architectures, enabling real-time data synchronization between systems without the overhead of batch processing or manual data extraction. When you need to capture database changes and stream them reliably to downstream consumers, combining Debezium with Apache Kafka creates a powerful, production-ready solution. This article explores how to build a robust CDC pipeline using these technologies, from initial setup through practical implementation considerations.

Understanding the CDC Architecture

Debezium functions as a distributed platform built on top of Apache Kafka Connect. It monitors your database transaction logs and produces change events for every row-level insert, update, and delete operation. Rather than querying tables repeatedly or relying on timestamp columns, Debezium reads directly from the database’s write-ahead log (WAL) or binary log, ensuring you capture every change with minimal impact on database performance.

The architecture consists of three primary components working in concert. First, the source database maintains its transaction log as part of normal operations. Second, Debezium connectors read these logs and transform the changes into standardized event formats. Third, Kafka serves as the durable message broker, storing these events in topics where downstream applications can consume them at their own pace.

This log-based approach provides significant advantages over traditional query-based CDC methods. You eliminate the need for intrusive triggers, avoid polling overhead, and capture deletions reliably—something timestamp-based approaches struggle with. The transaction log already exists for database durability, so you’re leveraging existing infrastructure rather than adding new components to your database layer.

Setting Up Your Database for CDC

Before deploying Debezium connectors, you must configure your source database to retain transaction logs appropriately. The specific requirements vary by database system, but the fundamental principle remains constant: the database must keep logs long enough for Debezium to process them, even during connector downtime or maintenance windows.

For PostgreSQL, you’ll need to enable logical replication by setting wal_level to logical in your postgresql.conf file. You’ll also need to create a replication slot and a publication for the tables you want to capture. Here’s what a basic setup looks like:

ALTER SYSTEM SET wal_level = logical;
-- Restart PostgreSQL after this change

CREATE PUBLICATION debezium_publication FOR ALL TABLES;

MySQL requires binary logging with row-based format. In your MySQL configuration, ensure these settings are present:

server-id = 223344
log_bin = mysql-bin
binlog_format = ROW
binlog_row_image = FULL
expire_logs_days = 7

The binlog_row_image setting is particularly important—it ensures that Debezium receives complete before and after images of changed rows, which is essential for update events. The expire_logs_days parameter controls log retention, giving you a buffer period if your connector experiences downtime.

You’ll also need to create a dedicated database user for Debezium with appropriate permissions. For PostgreSQL, this user needs replication privileges and SELECT permissions on the tables being captured. For MySQL, the user requires REPLICATION SLAVE, REPLICATION CLIENT, and SELECT privileges on the relevant schemas.

Configuring the Debezium Connector

With your database prepared, the next step involves deploying and configuring the Debezium connector. Debezium connectors run as tasks within Kafka Connect, which can operate in standalone mode for development or distributed mode for production environments. Distributed mode provides fault tolerance and scalability, allowing you to run multiple worker nodes that coordinate through Kafka itself.

The connector configuration is submitted as a JSON document to the Kafka Connect REST API. Here’s an example configuration for a PostgreSQL connector:

{
  "name": "inventory-connector",
  "config": {
    "connector.class": "io.debezium.connector.postgresql.PostgresConnector",
    "database.hostname": "postgres-db.example.com",
    "database.port": "5432",
    "database.user": "debezium_user",
    "database.password": "secure_password",
    "database.dbname": "inventory",
    "database.server.name": "inventory_server",
    "table.include.list": "public.customers,public.orders,public.products",
    "plugin.name": "pgoutput",
    "publication.name": "debezium_publication"
  }
}

The database.server.name parameter deserves special attention—it forms the prefix for all Kafka topic names that this connector creates. Each table gets its own topic following the pattern {server.name}.{schema}.{table}. This logical server name also serves as a namespace, allowing you to run multiple connectors against different databases without topic name collisions.

The connector begins by taking a consistent snapshot of your existing data, ensuring that Kafka topics contain the complete current state before streaming subsequent changes. This initial snapshot can be resource-intensive for large tables, but Debezium provides several mechanisms to manage this process. You can configure snapshot mode to handle different scenarios—whether you want to snapshot all tables, only new tables, or skip snapshotting entirely if you’re adding CDC to an already-running system.

Understanding Event Structure and Schema Evolution

Debezium produces richly structured events that contain not just the changed data, but also metadata about the change itself. Each event includes a before and after representation of the row, the type of operation (create, update, or delete), the transaction timestamp, and the log position where the change occurred. This comprehensive structure enables consumers to rebuild state, maintain caches, or implement complex event processing logic.

Here’s what a typical update event looks like:

{
  "before": {
    "id": 1001,
    "name": "John Doe",
    "email": "john@example.com",
    "status": "active"
  },
  "after": {
    "id": 1001,
    "name": "John Doe",
    "email": "john.doe@example.com",
    "status": "active"
  },
  "source": {
    "version": "2.1.0.Final",
    "connector": "postgresql",
    "name": "inventory_server",
    "ts_ms": 1698765432000,
    "snapshot": "false",
    "db": "inventory",
    "sequence": "[\"23456789\",\"23456800\"]",
    "schema": "public",
    "table": "customers",
    "txId": 567,
    "lsn": 23456800
  },
  "op": "u",
  "ts_ms": 1698765432150
}

The before and after fields show exactly what changed, while the source section provides detailed lineage information. The op field indicates the operation type: ‘c’ for create, ‘u’ for update, ‘d’ for delete, or ‘r’ for read (used during initial snapshots).

Schema evolution presents an important consideration for production CDC pipelines. When your database schema changes—adding columns, modifying types, or renaming fields—Debezium must handle these changes gracefully. By default, Debezium integrates with Confluent Schema Registry to manage Avro schemas, providing backward and forward compatibility guarantees. When a schema changes, a new version is registered, and consumers can handle both old and new event formats appropriately.

Monitoring and Operational Considerations

Operating a CDC pipeline in production requires careful attention to several operational aspects. Connector lag—the delay between when a change occurs in the database and when it appears in Kafka—is your primary health metric. Debezium exposes JMX metrics that track this lag at both the connector and table level, allowing you to set up alerts when lag exceeds acceptable thresholds.

Database log retention becomes critical for connector reliability. If a connector goes down for an extended period and the database purges its transaction logs before the connector recovers, you’ll need to perform a new snapshot to regain consistency. Setting retention periods requires balancing storage costs against your recovery time objectives. A common approach is to retain logs for at least twice your expected maximum downtime.

Kafka topic configuration also impacts pipeline reliability and performance. Setting appropriate retention periods on your CDC topics ensures you can rebuild consumer state if needed. Compaction is particularly useful for CDC topics—Kafka’s log compaction maintains the latest value for each key while removing intermediate updates, effectively giving you a changelog that can reconstruct current database state.

Network and database load management deserves attention during initial deployment. The initial snapshot phase reads large volumes of data from your database and writes it to Kafka. You can throttle this process using connector configuration parameters like max.batch.size and poll.interval.ms to prevent overwhelming either your database or your Kafka cluster.

Handling Transformations and Filtering

Debezium supports Single Message Transformations (SMTs) that allow you to modify events as they flow through the connector. These lightweight transformations can filter events, route them to different topics, mask sensitive fields, or flatten nested structures. While you should generally prefer keeping raw CDC events and performing transformations in dedicated stream processing applications, SMTs provide useful capabilities for common scenarios.

For example, you might want to filter out changes to specific columns that don’t matter to downstream consumers, or you might need to route events from different tables to a single topic based on a common attribute. Here’s how you might configure a transformation to extract a nested field:

"transforms": "unwrap",
"transforms.unwrap.type": "io.debezium.transforms.ExtractNewRecordState",
"transforms.unwrap.drop.tombstones": "false",
"transforms.unwrap.delete.handling.mode": "rewrite"

The ExtractNewRecordState transformation is particularly common—it simplifies the event structure by extracting just the after value for creates and updates, making events easier to consume for applications that don’t need the full before/after comparison.

Integrating with Downstream Systems

Once your CDC pipeline is streaming events into Kafka, you’ll need to consume them in downstream applications. The consumption pattern depends on your use case. For materializing database replicas or maintaining search indexes, you’ll typically process events sequentially to maintain consistency. For analytics or event-driven microservices, you might consume events in parallel across partitions for higher throughput.

When building consumer applications, implement idempotency carefully. CDC events can be redelivered if consumers restart or rebalance, so your processing logic should handle duplicate events gracefully. The source.lsn or equivalent log position field provides a natural idempotency key—you can track the last processed position and skip events you’ve already handled.

For database replication scenarios, pay attention to transaction boundaries. Debezium provides transaction metadata that allows you to group events from the same source transaction, ensuring you maintain transactional consistency in your target system. This is particularly important when changes span multiple tables with foreign key relationships.

Conclusion

Building a CDC data pipeline with Debezium and Kafka provides a robust foundation for real-time data integration. By leveraging database transaction logs, you achieve low-latency change capture with minimal impact on source systems, while Kafka’s durability and scalability ensure reliable delivery to downstream consumers.

The combination of Debezium’s rich event structure, operational monitoring capabilities, and integration with the broader Kafka ecosystem makes it a production-ready solution for modern data architectures. Whether you’re building data lakes, maintaining search indexes, or enabling event-driven microservices, this CDC approach provides the real-time data movement capabilities that contemporary applications demand.