Change Data Capture (CDC) is a powerful tool for monitoring changes in database systems. By leveraging CDC, businesses can ensure data consistency and improve synchronization. This guide explains CDC's primary methods and use cases.
Examples and Use Cases
Different CDC methods address specific needs, offering flexibility in various environments. Below are explanations and practical applications for each method.
Log-Based CDC
This method reads transaction logs to detect updates, providing real-time insights with minimal database impact.
A financial institution captures real-time updates in transaction logs to power stock trading dashboards.
Trigger-Based CDC
Trigger-based CDC captures changes using triggers tied to specific database events.
CREATE TRIGGER cdc_employees AFTER INSERT ON employees
FOR EACH ROW
INSERT INTO cdc_table (operation, data) VALUES ('INSERT', JSON_OBJECT('id', NEW.id));
HR systems track promotions by logging employee record changes.
Metadata-Based CDC
This approach focuses on detecting changes in schema elements, such as new columns or updated table structures.
A data warehousing tool synchronizes schema changes to avoid downtime during updates.
FAQ
What does CDC do?
CDC tracks data changes, enabling real-time monitoring and integration.
Why is CDC important?
It ensures data consistency, supports analytics, and reduces ETL workload.
How to set up CDC in MySQL?
Create triggers to log data changes in a tracking table.
What are the drawbacks of CDC?
Performance impact and data volume management can be challenges.
Summary
CDC optimizes data tracking for analytics and synchronization. For an in-depth tutorial, check the article Change Data Capture: A Comprehensive Guide.
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