Change Data Capture (CDC) enables real-time data tracking and integration across systems. It is a vital tool for businesses that require up-to-date information for analytics, synchronization, and auditing. Let’s explore its core methods and examples.
Examples in action
CDC implementation varies based on specific needs and database environments. Here, we break down the primary methods of CDC and provide examples for each.
Log-Based CDC
Log-based CDC reads transaction logs to detect data changes, making it suitable for real-time updates.
An e-commerce platform updates customer purchase records across its CRM system in near real time using log-based CDC.
Trigger-Based CDC
Trigger-based CDC uses database triggers to automatically log changes in specified tables.
CREATE TRIGGER cdc_update AFTER UPDATE ON orders
FOR EACH ROW
INSERT INTO cdc_table (operation, data) VALUES ('UPDATE', JSON_OBJECT('order_id', NEW.order_id));
A logistics system captures order shipment updates by logging changes to the orders table.
Metadata-Based CDC
This method tracks changes in database schemas, such as column additions or table modifications.
A SaaS platform monitors table schema changes to ensure compatibility with third-party integrations.
FAQ
What is CDC?
CDC captures and tracks data changes using log-based, trigger-based, or metadata-based techniques.
How is CDC useful?
It ensures real-time data synchronization, efficient auditing, and reduced ETL workloads.
How can CDC be implemented in MySQL?
Using triggers to log changes into a dedicated table.
What are CDC’s challenges?
It can impact performance and requires managing data volume and latency effectively.
Summary
CDC simplifies real-time data synchronization and auditing. Learn more by exploring the article Change Data Capture: A Comprehensive Guide.