Intelligent Data Synchronization Techniques for Hybrid Data Platforms

  • Authors

    • Jacques Arsac Professor of Computer Science, University of Paris, France. Author
    • Gérard Huet Senior Research Scientist, INRIA, France. Author

    DOI:

    https://doi.org/10.67228/30715717/IJDEIC-2025PI3B6K

    Published 04-04-2025

  • Hybrid Data Platforms, Intelligent Data Synchronization, Distributed Databases, Cloud Computing, Change Data Capture, Artificial Intelligence, Data Replication, Conflict Resolution, Event-Driven Architecture, Edge Computing, Machine Learning, Distributed Systems

    Issue

    Vol. 8 No. 1 (2025)

    Section

    Articles

    How to Cite

    [1]
    J. Arsac and G. Huet, “Intelligent Data Synchronization Techniques for Hybrid Data Platforms”, IJDEIC, vol. 8, no. 1, pp. 01–15, Apr. 2025, doi: 10.67228/30715717/IJDEIC-2025PI3B6K.

  • Abstract

    The rapid growth of enterprise systems and cloud computing has transformed data management across hybrid environments integrating on-premise databases, private clouds, and public cloud infrastructures. However, challenges such as data consistency, latency, conflict resolution, security, and fault tolerance remain critical in distributed heterogeneous systems. Traditional synchronization methods are often inadequate for dynamic real-time workloads. This study reviews intelligent data synchronization techniques for hybrid data platforms, emphasizing AI- and machine learning-based approaches that enhance synchronization efficiency, scalability, and reliability. The proposed framework includes four layers: Data Acquisition, Intelligent Synchronization Engine, Adaptive Conflict Management, and Distributed Analytics. Predictive learning algorithms optimize synchronization timing and resource allocation, while adaptive conflict resolution mechanisms minimize inconsistencies. Experimental results show that intelligent synchronization methods reduce delay, improve throughput, enhance scalability, and strengthen failure recovery compared to traditional approaches. The study concludes that AI-driven synchronization is essential for real-time analytics, distributed transactions, and scalable cloud-native applications in modern enterprise environments.

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