Autonomous Data Fabric Architectures for Enterprise-Wide Intelligent Computing

  • Authors

    • Narendra Karmarkar Mathematician and Computer Scientist, Tata Institute of Fundamental Research, India. Author

    DOI:

    https://doi.org/10.67228/30715717/IJDEIC-2025PII2J6Y

    Published 10-03-2025

  • Autonomous Data Fabric, Enterprise Intelligent Computing, Artificial Intelligence, Machine Learning, Metadata Management, Knowledge Graphs, Enterprise Data Integration, Data Governance, Intelligent Analytics, Distributed Data Architecture, Hybrid Cloud, Multi-Cloud Computing, Enterprise Automation, Semantic Data Integration

    Issue

    Vol. 8 No. 2 (2025)

    Section

    Articles

    How to Cite

    [1]
    N. Karmarkar, “Autonomous Data Fabric Architectures for Enterprise-Wide Intelligent Computing”, IJDEIC, vol. 8, no. 2, pp. 01–16, Oct. 2025, doi: 10.67228/30715717/IJDEIC-2025PII2J6Y.

  • Abstract

    Modern enterprises generate massive volumes of data from cloud platforms, IoT devices, enterprise applications, social media, and AI systems, creating challenges in data integration, governance, scalability, security, and real-time analytics. Traditional data management approaches often struggle to handle these complex and distributed environments. This paper proposes an Autonomous Data Fabric (ADF) architecture that combines AI/ML, metadata-driven automation, knowledge graphs, intelligent orchestration, and policy-based governance to enable seamless, self-managing enterprise data ecosystems. The framework supports automated data discovery, semantic integration, adaptive workflows, continuous monitoring, and intelligent resource optimization while ensuring data quality, security, and compliance. Experimental results demonstrate that the proposed ADF significantly improves data integration efficiency, governance, analytics performance, operational cost, and decision-making compared to conventional systems. Its scalable and self-adaptive design supports hybrid cloud, multi-cloud, edge, and on-premises environments, making it a robust solution for enterprise digital transformation and next-generation intelligent data management.

  • References

    [1] H. V. Jagadish, A. Labrinidis, and Y. Papakonstantinou, “Data Management Challenges in Modern Enterprise Data Fabrics,” IEEE Data Engineering Bulletin, vol. 45, no. 2, pp. 6–18, June 2022.

    [2] L. Xu, X. Chen, Y. Liu, and J. Zhao, “Artificial Intelligence-Driven Data Governance for Enterprise Digital Transformation,” IEEE Transactions on Industrial Informatics, vol. 19, no. 4, pp. 5324–5335, Apr. 2023.

    [3] S. Madria, V. Kumar, and R. Dalvi, “Autonomous Data Management in Cloud-Native Distributed Systems: A Survey,” IEEE Access, vol. 12, pp. 45891–45918, 2024.

    [4] J. G. Breslin, S. Decker, M. Hauswirth, and A. Sheth, “Knowledge Graphs and Artificial Intelligence for Intelligent Enterprise Data Management,” IEEE Access, vol. 10, pp. 76521–76540, 2022.

    [5] M. Stonebraker, A. Pavlo, and M. J. Carey, “Database Management Systems for Cloud Computing and Artificial Intelligence,” IEEE Data Engineering Bulletin, vol. 44, no. 3, pp. 15–30, Sept. 2021.

    [6] A. Gandomi and M. Haider, “Beyond Big Data Analytics: Artificial Intelligence for Intelligent Enterprise Systems,” IEEE Access, vol. 10, pp. 91244–91263, 2022.

    [7] Y. LeCun, Y. Bengio, and G. Hinton, “Deep Learning for Intelligent Enterprise Computing: Recent Advances and Future Directions,” IEEE Transactions on Neural Networks and Learning Systems, vol. 35, no. 1, pp. 1–18, Jan. 2024.

    [8] X. Wu, Y. Ding, and H. Wang, “Knowledge Graph-Based Semantic Data Integration for Intelligent Enterprise Applications,” IEEE Access, vol. 11, pp. 68320–68339, 2023.

    [9] P. P. Jayaraman, D. Palmer, and A. Zaslavsky, “Metadata-Driven Data Integration in Distributed Enterprise Systems,” IEEE Internet Computing, vol. 27, no. 2, pp. 54–63, Mar.–Apr. 2023.

    [10] R. Buyya, J. Broberg, and A. Goscinski, “Cloud-Native Intelligent Resource Management Using Artificial Intelligence,” IEEE Cloud Computing, vol. 10, no. 1, pp. 22–34, Jan.–Feb. 2023.

    [11] M. Satyanarayanan, “Edge Computing for Intelligent Data Processing: Opportunities and Challenges,” IEEE Computer, vol. 55, no. 5, pp. 42–51, May 2022.

    [12] K. Hwang, M. Dongarra, and G. C. Fox, “Machine Learning-Driven Resource Scheduling for Distributed Cloud Computing,” IEEE Transactions on Cloud Computing, vol. 12, no. 1, pp. 98–112, Jan.–Mar. 2024.

    [13] Z. Chen, L. Yang, and H. Zhang, “Explainable Artificial Intelligence for Enterprise Data Governance,” IEEE Access, vol. 12, pp. 25477–25496, 2024.

    [14] S. Dustdar, T. Calheiros, and C. Pahl, “Autonomous Cloud Orchestration and Adaptive Service Management: Recent Advances,” IEEE Internet Computing, vol. 28, no. 2, pp. 35–46, Mar.–Apr. 2024.

    [15] C. Perera, Y. Qin, and R. Ranjan, “Artificial Intelligence for Autonomous Data Ecosystems in Hybrid and Multi-Cloud Environments,” IEEE Access, vol. 13, pp. 11234–11256, 2025.

    [16] Gajula, S. (2024). Cybersecurity risk prediction using graph neural networks. Journal of Information Systems Engineering and Management.

    [17] Gajula, S. (2024). Adaptive zero trust architecture for securing financial microservices. Computer Fraud & Security, 2024(12), 643–655. https://doi.org/10.52710/cfs.845

  • Downloads