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In the fast-changing environment of e-commerce, the requirement of real-time synchronization of data among different front-end applications and a centralized backend has become essential. This paper discusses the issues that lie in data management in distributed e-commerce systems and specifically emphasizes the synchronization of user interactions among multiple platforms such as customer interfaces and administrative portals. We examine three leading data handling algorithms: Event Sourcing, Change Data Capture (CDC), and Distributed Transactions, and explore their efficacy in maintaining data consistency, scalability, and fault tolerance. In a comparative analysis, we identify the strengths and weaknesses of each algorithm and offer insights into their viability in high- traffic e- commerce scenarios. Our results indicate that an event- driven architecture, together with the proper data handling algorithm, can drastically improve real-time synchronization performance, ultimately resulting in better user experience and operational effectiveness. This study adds to the existing body of knowledge on optimizing data management mechanisms in e- commerce systems, laying the groundwork for future innovation in real-time data processing.
Keywords:
E-commerce, Real-Time Synchronization, Data Handling Algorithms, Event Sourcing, Change Data Capture (CDC), Distributed Transactions, Event-Driven Architecture, Data Consistency, Scalability, Fault Tolerance, Distributed Systems, Data Management, User Experience, High-Traffic Environments, Synchronization Frameworks
Cite Article:
"Real-Time Data Synchronization Optimization: A Comparative Analysis in Distributed E-Commerce Systems", International Journal of Science & Engineering Development Research (www.ijrti.org), ISSN:2455-2631, Vol.10, Issue 4, page no.b544-b551, April-2025, Available :http://www.ijrti.org/papers/IJRTI2504167.pdf
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ISSN:
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator