Change data capture (CDC) is a method for detecting and recording changes in database records and moving these changes to various storage systems to ensure data consistency and support different applications. It efficiently synchronizes data across systems using log-based and timestamp-based tracking and is beneficial in scenarios like transitioning from monolithic to microservices architectures and developing event-driven applications. A tutorial demonstrates building a data pipeline to transfer sales data from a MySQL database to a BigQuery data warehouse using Debezium for capturing changes, Redpanda as a Kafka replacement for data streaming, and Apache Flink for data processing. The pipeline ensures that only relevant and current data is transferred, enhancing performance and scalability for real-time analytics. Moreover, the tutorial provides a step-by-step guide for setting up Docker services, configuring Kafka Connect, and using Apache Flink to preprocess and transfer data to BigQuery, with the entire codebase available on a GitHub repository.