A New Pattern for Managing Massive Datasets in the Enterprise through Data Fabric and Data Mesh
DOI:
https://doi.org/10.63282/3050-9246.IJETCSIT-V1I4P106Keywords:
Data Fabric, Data Mesh, Enterprise Data Management, Big Data, Decentralized Data, Data Governance, Data Architecture, Hybrid Cloud, Data Democratization, Data Integration, Data Scalability, Metadata Management, Federated Governance, Data as a Product, Self-Service Platforms, Data Ownership, Automation, Data Interoperability, Data Lineage, Data Quality, Real-Time Analytics, Data Orchestration, Domain-Driven Design, Compliance, Security, AI-Driven Insights, Cloud-Native ToolsAbstract
The increasing volume and complexity of corporate data have revealed that traditional designs are not good enough for managing scalability, governance, and flexibility. This has led to the need for new solutions like Data Fabric and Data Mesh. Data Fabric focuses on creating a unified, automated architecture that connects data across hybrid and multi-cloud environments. This ensures smooth integration, strict control & easy access for users. Automating workflows lowers expenses & makes sure that the information is too accurate and follows the rules. On the other hand, Data Mesh moves to a decentralized structure where domain teams control the information and regard it as a product. This strategy uses self-service platforms & specialized expertise to make teams more flexible, creative & more cooperative. This lets them manage their data better without having to rely on their centralized limits. Both paradigms deal with these important problems in modern data management, but their actual value comes from how well they work together. This article looks at the basic principles, architectural features & ways to put both paradigms into practice, giving us a sense of how they may work in these business settings. This shows that combining Data Fabric with Data Mesh creates an architecture that can grow, change & be used by everyone, allowing businesses to derive greater value from huge datasets while still meeting the demands of the market
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References
[1] Raptis, T. P., Passarella, A., & Conti, M. (2019). Data management in industry 4.0: State of the art and open challenges. IEEE Access, 7, 97052-97093.
[2] Kamath, C. (2001). On mining scientific datasets. Data mining for scientific and engineering applications, 1-21.
[3] Sai Prasad Veluru. “Hybrid Cloud-Edge Data Pipelines: Balancing Latency, Cost, and Scalability for AI”. JOURNAL OF RECENT TRENDS IN COMPUTER SCIENCE AND ENGINEERING ( JRTCSE), vol. 7, no. 2, Aug. 2019, pp. 109–125
[4] Nookala, G. (2020). Automation of privileged access control as part of enterprise control procedure. Journal of Big Data and Smart Systems, 1(1).
[5] Cox, M., & Ellsworth, D. (1997, August). Managing big data for scientific visualization. In ACM siggraph (Vol. 97, No. 1, pp. 21-38). MRJ/NASA Ames Research Center.
[6] Manda, Jeevan Kumar. "Securing Remote Work Environments in Telecom: Implementing Robust Cybersecurity Strategies to Secure Remote Workforce Environments in Telecom, Focusing on Data Protection and Secure Access Mechanisms." Focusing on Data Protection and Secure Access Mechanisms (April 04, 2020) (2020).
[7] Rubin, D. B. (1997). Estimating causal effects from large data sets using propensity scores. Annals of internal medicine, 127(8_Part_2), 757-763.
[8] Patel, Piyushkumar. "Bonus Depreciation Loopholes: How High-Net-Worth Individuals Maximize Tax Deductions." Distributed Learning and Broad Applications in Scientific Research 5 (2019): 1405-19.
[9] Immaneni, J., & Salamkar, M. (2020). Cloud migration for fintech: how kubernetes enables multi-cloud success. International Journal of Emerging Trends in Computer Science and Information Technology, 1(3), 17-28.
[10] Kamath, C. (2009). Scientific data mining: a practical perspective. Society for Industrial and Applied Mathematics.
[11] Jani, Parth. "UM Decision Automation Using PEGA and Machine Learning for Preauthorization Claims." The Distributed Learning and Broad Applications in Scientific Research 6 (2020): 1177-1205.
[12] Arugula, Balkishan, and Sudhkar Gade. “Cross-Border Banking Technology Integration: Overcoming Regulatory and Technical Challenges”. International Journal of Emerging Research in Engineering and Technology, vol. 1, no. 1, Mar. 2020, pp. 40-48
[13] Zhou, Z. H., Chawla, N. V., Jin, Y., & Williams, G. J. (2014). Big data opportunities and challenges: Discussions from data analytics perspectives [discussion forum]. IEEE Computational intelligence magazine, 9(4), 62-74.
[14] Veluru, Sai Prasad. "Threat Modeling in Large-Scale Distributed Systems." International Journal of Emerging Research in Engineering and Technology 1.4 (2020): 28-37.
[15] Praveena, M. A., & Bharathi, B. (2017, February). A survey paper on big data analytics. In 2017 International Conference on Information Communication and Embedded Systems (ICICES) (pp. 1-9). IEEE.
[16] Allam, Hitesh. Exploring the Algorithms for Automatic Image Retrieval Using Sketches. Diss. Missouri Western State University, 2017.
[17] Shoshani, A., & Rotem, D. (Eds.). (2009). Scientific data management: challenges, technology, and deployment. CRC Press.
[18] Patel, Piyushkumar. "The Role of AI in Forensic Accounting: Enhancing Fraud Detection Through Machine Learning." Distributed Learning and Broad Applications in Scientific Research 5 (2019): 1420-35.
[19] Post, F. H., Nielson, G., & Bonneau, G. P. (Eds.). (2002). Data visualization: The state of the art.
[20] Mohammad, Abdul Jabbar. “Sentiment-Driven Scheduling Optimizer”. International Journal of Emerging Research in Engineering and Technology, vol. 1, no. 2, June 2020, pp. 50-59
[21] Chen, M., Mao, S., & Liu, Y. (2014). Big data: A survey. Mobile networks and applications, 19, 171-209.
[22] Manda, Jeevan Kumar. "AI And Machine Learning In Network Automation: Harnessing AI and Machine Learning Technologies to Automate Network Management Tasks and Enhance Operational Efficiency in Telecom, Based On Your Proficiency in AI-Driven Automation Initiatives." Educational Research (IJMCER) 1.4 (2019): 48-58.
[23] Jani, Parth. "Real-Time Patient Encounter Analytics with Azure Databricks during COVID-19 Surge." The Distributed Learning and Broad Applications in Scientific Research 6 (2020): 1083-1115. -aug
[24] Dobre, C., & Xhafa, F. (2014). Parallel programming paradigms and frameworks in big data era. International Journal of Parallel Programming, 42(5), 710-738.
[25] Immaneni, J. (2020). Building MLOps Pipelines in Fintech: Keeping Up with Continuous Machine Learning. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 1(2), 22-32.
[26] Ramnath, S., Haghighi, P., Kim, J. H., Detwiler, D., Berry, M., Shah, J. J., ... & Menzel, S. (2019, August). Automatically generating 60,000 CAD variants for big data applications. In International Design Engineering Technical Conferences and Computers and Information in Engineering Conference (Vol. 59179, p. V001T02A006). American Society of Mechanical Engineers.
[27] Shaik, Babulal. "Network Isolation Techniques in Multi-Tenant EKS Clusters." Distributed Learning and Broad Applications in Scientific Research 6 (2020).
[28] Belaud, J. P., Negny, S., Dupros, F., Michéa, D., & Vautrin, B. (2014). Collaborative simulation and scientific big data analysis: Illustration for sustainability in natural hazards management and chemical process engineering. Computers in Industry, 65(3), 521-535.
[29] Manda, J. K. "Big Data Analytics in Telecom Operations: Exploring the application of big data analytics to optimize network management and operational efficiency in telecom, reflecting your experience with analytics-driven decision-making in telecom environments." EPH-International Journal of Science and Engineering, 3.1 (2017): 50-57.
[30] Kitchin, R., & Lauriault, T. P. (2015). Small data in the era of big data. GeoJournal, 80, 463-475.
[31] Foster, I., Zhao, Y., Raicu, I., & Lu, S. (2008, November). Cloud computing and grid computing 360-degree compared. In 2008 grid computing environments workshop (pp. 1-10). Ieee.
