Unified Metadata Management Framework for Hybrid Cloud Data Analytics

  • Authors

    • Ken Iverson Professor, Harvard University, Canada. Author

    DOI:

    https://doi.org/10.67228/30715717/IJDEIC-2024PI1B7P

    Published 02-12-2024

  • Hybrid Cloud, Metadata Management, Data Analytics, Unified Framework, Cloud Data Governance, Metadata Repository, Data Lineage, Cloud Computing, Data Privacy, Cloud Integration, Data Security

    Issue

    Vol. 7 No. 1 (2024)

    Section

    Articles

    How to Cite

    [1]
    K. Iverson, “Unified Metadata Management Framework for Hybrid Cloud Data Analytics”, IJDEIC, vol. 7, no. 1, pp. 01–10, Feb. 2024, doi: 10.67228/30715717/IJDEIC-2024PI1B7P.

  • Abstract

    In the era of big data, hybrid cloud environments have become integral for data analytics, offering flexibility, scalability, and cost-efficiency. However, managing metadata across these complex, distributed systems remains a significant challenge. Metadata, which describes the data, its context, and its usage, plays a crucial role in improving data discovery, quality, and governance. In this paper, we propose a Unified Metadata Management Framework designed specifically for hybrid cloud data analytics. This framework integrates metadata from multiple cloud environments (public, private, and multi-cloud) into a cohesive, centralized system that facilitates efficient data management, governance, and analytics. We discuss the key components of the framework, including metadata repositories, metadata exchange layers, cataloging, and data lineage tracking. Additionally, the framework ensures security and privacy compliance while offering scalability and flexibility. We provide use cases from healthcare, finance, and e-commerce sectors to demonstrate its practical applications and evaluate its performance against traditional metadata management approaches. The proposed solution offers significant improvements in managing metadata across hybrid cloud infrastructures and lays the foundation for future innovations in cloud data analytics.

  • References

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