In this comprehensive guide, the author explains a method for implementing Change Data Capture (CDC) using Apache Airflow, focusing on extracting data changes in a near-real-time, flexible, and highly available manner. The guide outlines the process of configuring Airflow to work with Google Cloud Platform (GCP), including setting up security, creating service accounts, and configuring connections between Airflow, CloudSQL, and Google Cloud Storage (GCS). It delves into building a Directed Acyclic Graph (DAG) in Airflow, configuring data partitioning and watermarking for data extraction intervals, and creating custom operators to facilitate the export of data to GCS. The document also emphasizes the importance of understanding Airflow scheduling and timing, introducing key concepts such as execution_date and schedule_interval to achieve near-real-time data synchronization. Additionally, the guide provides instructions on deploying the completed Airflow DAG to production using the Astronomer platform, highlighting the benefits of using this approach to ensure consistency, high availability, and efficient data extraction from production databases.