Semantic Data Transformation in Multi-Cloud Data Warehousing

  • Authors

    • Chen Ming Software Architect, Huawei, China. Author

    DOI:

    https://doi.org/10.67228/30715717/IJDEIC-2021PII3K9W

    Published 09-14-2021

  • Semantic Data Transformation, Multi-Cloud, Data Warehousing, Ontology Mapping, Data Integration, Cloud Interoperability, Knowledge Graphs, Data Harmonization, Data Governance, ETL in Cloud

    Issue

    Vol. 4 No. 2 (2021)

    Section

    Articles

    How to Cite

    [1]
    C. Ming, “Semantic Data Transformation in Multi-Cloud Data Warehousing”, IJDEIC, vol. 4, no. 2, pp. 01–12, Sep. 2021, doi: 10.67228/30715717/IJDEIC-2021PII3K9W.

  • Abstract

    In the era of digital transformation, organizations are increasingly leveraging multi-cloud strategies to harness the power of diverse cloud service providers. However, the heterogeneity of data schemas, storage formats, and semantic models across cloud platforms presents significant challenges for unified data analysis. Semantic data transformation offers a robust approach to harmonizing disparate datasets by mapping them to a common ontology or semantic model. This paper explores the methodologies, frameworks, and tools enabling semantic data transformation in multi-cloud data warehousing environments. It proposes an architecture for integrating semantic mediation, discusses best practices, and evaluates performance, scalability, and interoperability. Case studies and experimental results demonstrate the practical viability and impact of semantic transformation in achieving data consistency, governance, and advanced analytics across cloud platforms.

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