CDC Patterns

Concepts covered: paCdc

What They Want to Hear 'I use WAL-based CDC because it has near-zero impact on the source database. Debezium reads the Postgres WAL or MySQL binlog, streams change events to Kafka, and my pipeline consumes from Kafka to apply inserts, updates, and deletes to the target. I avoid trigger-based CDC because triggers add latency to every write on the source and are fragile at scale.' This is the answer that shows you have run CDC in production and understand the operational tradeoffs.

About This Interactive Section

This section is part of the Keeping Data Fresh: 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.

How DataDriven Lessons Work

DataDriven combines four interview rounds (SQL, Python, Data Modeling, Pipeline Architecture) with adaptive difficulty and spaced repetition. Easy problems get harder as you improve. Weak concepts resurface until you master them. Your readiness score tracks progress across every topic interviewers test. Every lesson section ends with problems you solve by writing and running real code, not by picking multiple-choice answers.