Data Mesh: A systematic Literature Review on a Paradigm Shift in Big Data Solutions

Abstract

In the evolving landscape of technology and data-driven decision-making, Big Data has prompted a reevaluation of traditional monolithic data architectures. The Data Mesh paradigm advocates for a decentralized approach, treating data as self-contained products and making them accessible. This shift offers a promising solution, providing a more agile and scalable alternative to traditional centralized data management. However, the practical implementation of Data Mesh poses challenges, particularly in the effective mapping of data across diverse products to minimize duplication. This study aims to comprehensively address this research gap by pursuing three key objectives: understanding the evolution of the Data Mesh concept, exploring different Data Mesh architectural approaches, and developing a practical framework for efficient data mapping. Employing a systematic literature review methodology, the study navigates the landscape of Data Mesh, identifying influential literature through Scopus, making a PRISMA methodology and conducting a bibliometric analysis using VOSViewer. The resultant framework is designed to streamline data mapping processes, minimize redundancy, and align closely with the evolving needs of data management within organizations, ensuring scalability and efficiency.