RELIABLE ENTERPRISE DATA EXCHANGE THROUGH EVENT-DRIVEN SYNCHRONIZATION AND INCREMENTAL PROCESSING
DOI:
https://doi.org/10.15680/k84z7f74Keywords:
Event-Driven Architecture (EDA), Enterprise Data Exchange, Incremental Data Processing, Change Data Capture (CDC), Event Synchronization, Distributed Systems, Enterprise Integration, Data Consistency, Event Streaming, Message-Oriented Middleware, Asynchronous Communication, Data Replication, Hybrid Cloud Integration, Microservices, Event Processing, Data Synchronization Framework, Reliability Engineering, Scalability, Fault Tolerance, Enterprise ArchitectureAbstract
Modern enterprises increasingly depend on continuous data exchange across heterogeneous applications, cloud platforms, legacy systems, and partner ecosystems. Traditional batch-oriented integration approaches often struggle to satisfy modern business expectations for low-latency synchronization, operational resilience, scalability, and data consistency. Event-driven synchronization combined with incremental data processing has emerged as a practical architectural paradigm that enables organizations to exchange information efficiently while minimizing network utilization, processing overhead, and synchronization delays. Rather than transferring complete datasets during every integration cycle, incremental processing focuses only on newly created, modified, or deleted records, significantly improving system performance and reducing infrastructure costs
This article presents a generalized enterprise framework for implementing reliable data exchange through event-driven synchronization and incremental processing. The discussion covers architectural principles, synchronization models, event lifecycle management, change data identification strategies, messaging infrastructures, consistency mechanisms, monitoring approaches, and operational governance. It further examines techniques for ensuring fault tolerance, scalability, security, and data integrity across distributed enterprise environments. The paper also highlights implementation challenges such as duplicate event handling, out-of-order delivery, schema evolution, network failures, and recovery strategies, together with practical mitigation techniques.
In addition, the article introduces a reference architecture suitable for hybrid and multi-cloud enterprise ecosystems and compares event-driven synchronization with conventional batch integration methods. Various deployment considerations, performance optimization strategies, and operational best practices are discussed to assist architects and engineering teams in designing resilient enterprise integration platforms. The proposed framework is technology-agnostic and applicable across industries including finance, healthcare, manufacturing, retail, telecommunications, logistics, and government services.
The objective of this work is to provide researchers and practitioners with a comprehensive understanding of reliable enterprise data synchronization techniques that improve system interoperability, maintain data consistency, support near real-time business operations, and enable scalable digital transformation initiatives.
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