CDC: The Three Families

Concepts covered: paCdc

Change Data Capture, abbreviated CDC, is the discipline of capturing every insert, update, and delete that lands in an operational database, in order, and surfacing them for downstream consumers. CDC is what turns a transactional database into a streaming source without the application code knowing anything has changed. Three families of CDC dominate. They differ in where the capture happens and what it costs. Family Comparison Trigger-Based CDC Trigger-based CDC is the oldest pattern. The DBA installs database triggers on every table of interest. Each trigger fires on INSERT, UPDATE, or DELETE and writes a row to a side table that records what happened. A pipeline polls the side table, drains it, and writes to downstream. The pattern works on any database that supports triggers, captures

About This Interactive Section

This section is part of the Ingestion Patterns: Intermediate lesson on DataDriven, a free data engineering interview prep platform. Each section includes explanations, worked examples, and hands-on code challenges that execute in real time. SQL queries run against a live PostgreSQL database. Python runs in a sandboxed Docker container. Data modeling problems validate against interactive schema canvases. All content is framed around what data engineering interviewers actually test at companies like Meta, Google, Amazon, Netflix, Stripe, and Databricks.

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