Change Data Capture (CDC)
Utilize Kafka for Change Data Capture to stream database changes in real-time for various use cases.
Change Data Capture (CDC) is a free Apache Kafka & Stream Processing Fundamentals lesson on CoddyKit — lesson 2 of 4. You can read the complete lesson below for free — then practise it hands-on in the browser with a built-in code editor and a 24/7 AI tutor. It is part of the Apache Kafka & Stream Processing Fundamentals learning path, one of 4 lessons in the course, and your progress syncs across the web and the CoddyKit app.
What is Change Data Capture?
Imagine needing to know every time a record in your database is updated, inserted, or deleted, in real-time. This is where Change Data Capture (CDC) comes in!
CDC is a software design pattern used to track and capture changes made to data in a database. It focuses on identifying and capturing only the data that has changed, rather than performing full scans.
Real-Time Data with CDC & Kafka
Combining CDC with Kafka unlocks powerful capabilities for real-time data processing and integration:
- Real-time Analytics: Update dashboards and reports instantly.
- Data Synchronization: Keep multiple databases or data stores consistent.
- Event Sourcing: Reconstruct the full history of changes for auditing or debugging.
- Microservices: Enable services to react to changes in other services' data without direct database access.
Log-Based CDC Explained
The most common and efficient CDC method, especially with Kafka, is log-based CDC. Databases like PostgreSQL, MySQL, and SQL Server maintain a transaction log (or write-ahead log - WAL).
This log records every change made to the database. Log-based CDC tools read these logs directly, without impacting the database's performance, to extract changes.
CDC Flow to Kafka
A typical CDC architecture with Kafka involves:
- Source Database: The database where changes originate.
- CDC Connector/Tool: Reads the database's transaction log.
- Kafka Connect: A framework for connecting Kafka with other systems.
- Kafka Topic: Where the captured change events are published.
- Consumers: Applications that read and process the change events from Kafka.
Debezium: Open-Source CDC
Debezium is a popular open-source distributed platform for Change Data Capture. It provides a set of Kafka Connect connectors that monitor specific database systems.
When changes occur in your database, Debezium streams these changes as events to Kafka topics. It supports various databases like PostgreSQL, MySQL, MongoDB, and SQL Server.
Debezium PostgreSQL Connector
To set up Debezium, you'd typically configure a connector via Kafka Connect's REST API. Here's a simplified example of a Debezium PostgreSQL connector configuration:
{
"name": "pg-connector",
"config": {
"connector.class": "io.debezium.connector.postgresql.PostgresConnector",
"database.hostname": "localhost",
"database.port": "5432",
"database.user": "postgres",
"database.password": "password",
"database.dbname": "mydb",
"topic.prefix": "dbserver1",
"table.include.list": "public.customers"
}
}This config tells Debezium to monitor the mydb database on localhost:5432 and send changes from the public.customers table to Kafka topics prefixed with dbserver1.
What a CDC Event Looks Like
When Debezium captures a change, it publishes an event to Kafka. This event usually contains structured information:
before: The state of the record before the change (for updates/deletes).after: The state of the record after the change (for inserts/updates).op: The operation type (cfor create,ufor update,dfor delete,rfor read/snapshot).source: Metadata about the database, table, and transaction.
These events are often serialized as JSON or Avro.
Processing Change Events
A Kafka consumer application can read these CDC events and react to them. For instance, you might update a search index, invalidate a cache, or trigger another microservice.
Here's a basic Java consumer example illustrating how you might parse a Debezium event:
public class CdcConsumer {
public static void main(String[] args) {
// This is a simplified example.
// In reality, use KafkaConsumer and JSON/Avro parsing.
String jsonEvent = "{ \"payload\": { \"op\": \"c\", \"after\": { \"id\": 1, \"name\": \"Alice\" } } }";
// Imagine parsing jsonEvent here
String operationType = getOperationFromJson(jsonEvent, "op");
String newName = getOperationFromJson(jsonEvent, "name");
if ("c".equals(operationType)) {
System.out.println("New customer created: " + newName);
} else if ("u".equals(operationType)) {
System.out.println("Customer updated: " + newName);
}
}
// Placeholder for JSON parsing logic
private static String getOperationFromJson(String json, String key) {
if (key.equals("op")) return "c"; // Simulate 'c' operation
if (key.equals("name")) return "Alice"; // Simulate 'name'
return null;
}
}Practical CDC Use Cases
CDC with Kafka is incredibly versatile. Some common use cases include:
- Data Warehousing: Populate data warehouses with only changed data for efficient ETL (Extract, Transform, Load).
- Cache Invalidation: Automatically clear or update caches when underlying data changes.
- Audit Logs: Maintain a complete, immutable history of all data changes for compliance.
- Search Indexing: Keep search indexes (e.g., Elasticsearch) up-to-date with real-time database changes.
CDC Knowledge Check
Which of the following is a primary benefit of using log-based Change Data Capture (CDC) with Kafka?
CDC with Kafka: A Powerful Pattern
We've explored how Change Data Capture (CDC) is a powerful pattern for streaming database changes in real-time to Kafka. Using tools like Debezium, you can efficiently capture inserts, updates, and deletes from your databases.
This enables a wide array of real-time use cases, from data synchronization and analytics to event sourcing and microservice communication. Understanding CDC is key to building responsive, data-driven applications with Kafka.
Frequently asked questions
Is the “Change Data Capture (CDC)” lesson free?
Yes — the full text of “Change Data Capture (CDC)” is free to read here on the web, and the Apache Kafka & Stream Processing Fundamentals course includes 4 lessons in total. To practise it interactively (a built-in code editor and a 24/7 AI tutor) and unlock the rest of the Apache Kafka & Stream Processing Fundamentals course, upgrade to CoddyKit PRO.
What will I learn in “Change Data Capture (CDC)”?
Utilize Kafka for Change Data Capture to stream database changes in real-time for various use cases. You practise Apache Kafka & Stream Processing Fundamentals with hands-on code you run directly in the browser, and a 24/7 AI tutor answers your questions as you work through the lesson.
Do I need any experience to start Apache Kafka & Stream Processing Fundamentals?
No prior experience is required. Apache Kafka & Stream Processing Fundamentals on CoddyKit is structured for beginners through advanced learners; this is — lesson 2 of 4, so you can start here or from the beginning and move at your own pace.
How long does the “Change Data Capture (CDC)” lesson take?
Most CoddyKit lessons take about 5–10 minutes. Each one is bite-sized and interactive, so you make steady progress and pick up exactly where you left off across the web and the app.
Can I write and run code in this Apache Kafka & Stream Processing Fundamentals lesson?
Yes. Every Apache Kafka & Stream Processing Fundamentals lesson includes a built-in code editor, so you write and run real code right in your browser and get instant AI feedback — no local setup required.