Change Data Capture (CDC) is a critical technique for modern data pipelines, enabling real-time replication of database changes, which is essential for data lakes, analytics platforms, and event-driven applications. However, setting up a CDC pipeline is only the beginning; ensuring its reliability through automated validation within a CI/CD pipeline is crucial to prevent data replication issues like missing records or corruption, which can cause inaccurate insights. This guide provides a framework using CircleCI to simulate data changes, validate their correct reflection in a destination system, and catch issues before they impact production. It utilizes technologies such as PostgreSQL, Debezium, Apache Kafka, Spark, Apache Hudi, and MinIO. The process involves orchestrating validation workflows, including baseline and change validations, and integrating these checks into a CI pipeline to automate the process. The guide emphasizes the importance of testing and validation in maintaining data consistency and reliability and suggests extending the validator to handle more complex scenarios for a robust, production-ready CDC pipeline.