REAL-TIME DATA REPLICATION TO EXTERNAL SYSTEMS

This paper introduces a comprehensive framework for real-time data replication to external systems, addressing the increasing need for seamless data synchronization across distributed environments. Utilizing advanced data replication techniques, including Change Data Capture (CDC) and distributed messaging systems, it demonstrates how organizations can achieve low-latency and high-availability data replication. The proposed approach focuses on scalability, fault tolerance, and consistency, ensuring that data remains current across diverse systems. Case studies on financial transactions and IoT data streams showcase the framework’s practical implementation and its positive effects on system performance and data integrity. The results indicate that real-time data replication enhances operational efficiency and supports data-driven decision-making in dynamic environments. Additionally, the framework’s versatility across various sectors, such as e-commerce and healthcare, highlights its potential to transform data management practices across industries. Keywords: Real-Time Data Replication, Change Data Capture (CDC), Distributed Messaging Systems, Apache Kafka, Log-Based Replication, Data Consistency, Low-Latency Replication, Fault Tolerance, Scalability, IoT Data Streams, Financial Data Replication, Data Transformation and Routing.