An Introduction to Change Data Capture (CDC) Practices

The article provides an overview of Change Data Capture (CDC) practices rather than focusing on specific tools.

In typical web applications that use relational databases such as MySQL or PostgreSQL, reporting and analytics queries can be resource‑intensive, create network pressure, and delay business decisions because data is refreshed only periodically.

CDC solves this problem by capturing only the data changes—INSERT, UPDATE, DELETE operations—and streaming them to downstream systems like data warehouses, enabling near‑real‑time analytics without impacting the source database.

CDC extracts the "delta" of DML operations and applies them to target tables, providing a historical change log for each user table.

Modern CDC implementations read changes from database transaction logs, process them in memory on separate services, and deliver change events to subscribers, allowing flexible and scalable data pipelines.

Production‑Ready CDC System

Key requirements for a production CDC system include:

Preserving the exact order of changes to maintain consistent state across systems.

Ensuring at‑least‑once delivery of change events to downstream consumers.

Supporting flexible message transformation to accommodate different data formats.

When these requirements are satisfied, CDC enables scalable, low‑latency data pipelines that keep source and target systems synchronized.