Capture Database Changes into Flink with Debezium CDC (2026)

Debezium’s magic isn’t just capturing changes; it’s about treating your database as a real-time event stream, transforming static data into a dynamic, flowing source of truth.

Let’s see this in action. Imagine a PostgreSQL database with a products table.

CREATE TABLE products (
    id SERIAL PRIMARY KEY,
    name VARCHAR(255) NOT NULL,
    price DECIMAL(10, 2)
);

INSERT INTO products (name, price) VALUES ('Laptop', 1200.00);
UPDATE products SET price = 1150.00 WHERE name = 'Laptop';
DELETE FROM products WHERE name = 'Laptop';

Debezium, configured as a Kafka Connect source connector, watches the PostgreSQL Write-Ahead Log (WAL). When a change occurs (an INSERT, UPDATE, or DELETE), Debezium reads the corresponding WAL entry. It then translates this low-level database operation into a structured, event-driven message.

Here’s a simplified representation of what Debezium might publish to a Kafka topic (e.g., dbserver1.public.products):

For the INSERT:

{
  "schema": { ... },
  "payload": {
    "before": null,
    "after": {
      "id": 1,
      "name": "Laptop",
      "price": "1200.00"
    },
    "source": { ... },
    "op": "c", // 'c' for create
    "ts_ms": 1678886400000
  }
}

For the UPDATE:

{
  "schema": { ... },
  "payload": {
    "before": {
      "id": 1,
      "name": "Laptop",
      "price": "1200.00"
    },
    "after": {
      "id": 1,
      "name": "Laptop",
      "price": "1150.00"
    },
    "source": { ... },
    "op": "u", // 'u' for update
    "ts_ms": 1678886410000
  }
}

For the DELETE:

{
  "schema": { ... },
  "payload": {
    "before": {
      "id": 1,
      "name": "Laptop",
      "price": "1150.00"
    },
    "after": null,
    "source": { ... },
    "op": "d", // 'd' for delete
    "ts_ms": 1678886420000
  }
}

This stream of events is then consumed by Apache Flink. Flink, with its powerful stream processing capabilities, can react to these changes in real-time. You can build applications that:

• Synchronize data: Keep caches, search indexes, or other data stores up-to-date.
• Audit changes: Log every modification for compliance or debugging.
• Trigger downstream processes: Initiate workflows based on specific data updates.
• Build real-time dashboards: Visualize data as it changes, not just in batch intervals.

The core problem Debezium solves is the disconnect between a transactional database and the need for real-time data processing. Databases are optimized for ACID transactions, not for efficiently broadcasting every single change. Debezium bridges this gap by leveraging the database’s own internal logging mechanisms (like PostgreSQL’s WAL or MySQL’s binlog). It acts as a specialized reader that understands these logs and transforms them into a standardized, event-driven format, typically JSON, which can then be published to a message queue like Kafka. Flink then subscribes to these Kafka topics, treating the database changes as an immutable, ordered stream of events.

Internally, Debezium consists of a connector (specific to each database type, e.g., debezium-connector-postgres) and an offset storage mechanism. The connector monitors the database logs, reads change events, and converts them into a common format. The offset storage keeps track of which log position (offset) has been processed, allowing the connector to resume from where it left off if it restarts. Flink then uses Kafka’s consumer group mechanism to read from these topics, processing each event. Flink’s state management capabilities are crucial here, allowing it to maintain consistent application state even in the face of late-arriving data or failures.

The source field within the Debezium payload is more than just metadata; it contains crucial information about the origin of the change, including the database name, schema name, table name, and the specific transaction ID. This allows Flink applications to precisely understand where the change originated and to filter or route events based on this context, which is essential for complex, multi-database integration scenarios.

When you’re working with Debezium and Flink, the concept of "schema evolution" is paramount. If you alter your database schema (e.g., add a column to a table), Debezium can be configured to automatically publish schema change events. Flink applications must then be robust enough to handle these schema changes, potentially by dynamically updating their internal representations of the data or by using schema registry solutions like Confluent Schema Registry to manage Avro or Protobuf schemas.

You’ll soon need to configure how Flink handles schema evolution for your Debezium-sourced streams.