Comparative Study of Access Control Mechanisms in Data Mesh vs. Data Fabric Architectures

  • Authors

    • Niranjan Assistant Professor, Department of IT, Acharya Narendra Dev College, New Delhi, India. Author
    • Sheetal Kumari Assistant Professor, Department of IT, Acharya Narendra Dev College, New Delhi, India. Author

    Published 2026-01-09

  • Data Mesh, Data Fabric, Access Control, Data Governance, Identity and Access Management (IAM), Zero Trust Architecture, Policy Enforcement, Decentralized Data Ownership, Metadata-Driven Security, Data Architecture

    Issue

    Vol. 1 No. 1 (2026)

    Section

    Articles

    How to Cite

    [1]
    Niranjan and S. Kumari, “Comparative Study of Access Control Mechanisms in Data Mesh vs. Data Fabric Architectures”, IJDEIC, vol. 1, no. 1, pp. 31–40, Jan. 2026, Accessed: Mar. 02, 2026. [Online]. Available: https://worldcometresearchgroup.com/index.php/ijdeic/article/view/47

  • Abstract

    The increasing complexity and volume of data in modern enterprises have led to the evolution of decentralized data architectures such as Data Mesh and Data Fabric. While both paradigms aim to democratize data access and improve scalability, their approach to data governance, particularly access control, varies significantly. This paper presents a comparative analysis of access control mechanisms in Data Mesh and Data Fabric architectures. It explores the foundational principles of both models, evaluates how they implement access policies, identity management, and compliance, and examines their adaptability to enterprise-scale requirements. By highlighting their strengths, limitations, and ideal use cases, this study aims to assist data architects and security professionals in making informed decisions when designing secure and scalable data infrastructure.

  • References

    [1] Dehghani, Zhamak. Data Mesh: Delivering Data-Driven Value at Scale. O'Reilly Media, 2022.

    [2] IBM. "What is a Data Fabric?" IBM Cloud Learn Hub, 2023.

    [3] Gartner. "Data Fabric Architecture is Key to Modernizing Data Management." Gartner Research, 2022.

    [4] Open Policy Agent. "Policy-Based Control for Cloud-Native Environments." OpenPolicyAgent.org, 2023.

    [5] Informatica. "Informatica Intelligent Data Management Cloud (IDMC)." Informatica Whitepaper, 2023.

    [6] Talend. "Talend Data Fabric: Unified Platform for Data Integration and Governance." Talend Product Overview, 2023.

    [7] Microsoft. "Decentralized Identity and Access Management." Microsoft Azure Docs, 2023.

    [8] NIST. "Zero Trust Architecture." NIST Special Publication 800-207, 2020.

    [9] AWS. "Best Practices for Implementing RBAC and ABAC in AWS Environments." AWS Whitepaper, 2022.

    [10] Data Governance Professionals Organization (DGPO). "Federated Governance in Decentralized Data Architectures." DGPO Research Brief, 2022.

    [11] Accenture. "AI in Data Governance: Automating Access Control and Classification." Accenture Insights, 2023.

    [12] McKinsey & Company. "Building Trustworthy Data Platforms with AI-Driven Governance." McKinsey Digital, 2023.

    [13] Google Cloud. "Implementing Data Mesh on Google Cloud." Google Cloud Architecture Center, 2023.

    [14] Deloitte. "Navigating the Future of Data Governance in the Age of Decentralization." Deloitte Insights, 2023.

    [15] Forrester. "Data Fabric vs. Data Mesh: Which One Is Right for You?" Forrester Report, 2023.

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