Change Data Capture with Airflow - Part 2

The second part of the blog series delves into implementing Change Data Capture (CDC) with Airflow, emphasizing the synchronization of data warehouses with operational data stores to enhance business reporting and decision-making. It discusses creating a data pipeline using Airflow's Directed Acyclic Graph (DAG) for batch and near-real-time processing, highlighting the importance of modularizing tasks for efficiency and manageability. Examples include implementing Slowly Changing Dimensions Type II and using custom operators to streamline processes. The blog explores the use of cloud storage, sensor tasks for event-driven pipelines, and schema evolution to handle changes in data structure. It also touches on the challenges of handling deletions, managing large data volumes, and maintaining data integrity using logical replication and log-based synchronization. The post concludes by acknowledging the complexity of CDC and the need to tailor solutions to specific business requirements, promoting modularity, atomicity, and the ability to manage full and incremental data loads effectively.