In an era increasingly reliant on data, seamless integration and synchronization between various systems are crucial for maintaining consistency, accuracy, and sustainability of information. Change Data Capture (CDC) is a highly effective method for detecting data changes in real-time, allowing systems to record each modification without reprocessing the entire dataset. By integrating CDC with an Event-Driven Architecture, systems can process only data changes, reducing server load and ensuring data consistency across systems. This study examines a data synchronization system built using CDC and Event-Driven Architecture on a dataset of 1,000,000 records. The test results indicate strong performance, with the fastest average processing time recorded at 2.6622 milliseconds per data entry and a data loss rate of 0. These findings demonstrate that the combination of CDC and Event-Driven Architecture offers an efficient, fast, and scalable solution for real-time data synchronization on a large scale.
