DataMesh: A Decentralized Approach to Big Data and AI/ML Management

Abstract

The digital era has led the way in an unprecedented surge in data volume and complexity, coupled with the rapid development of artificial intelligence and machine learning technologies. This paradigm shift has exposed the limitations of traditional centralized data architectures, which often struggle to deliver the agility, scalability, and domain-specific flexibility required in today's data-driven landscape. In response to these challenges, a novel decentralized approach known as Data Mesh has emerged as a potential game- changer in data management. This paper delves into the core principles, architectural framework, and practical implementation of Data Mesh, with a particular focus on its application in big data and AI/ML contexts. By examining how Data Mesh addresses issues of data ownership, accessibility, and scalability, we explore its potential to revolutionize modern ML and AI workflows. Our analysis encompasses the key benefits and challenges of this approach, supported by relevant use cases that illustrate its impact on data-driven organizations. Additionally, we offer a critical evaluation of DataMesh's limitations and propose future research directions, providing valuable insights for both academic and industry practitioners navigating the evolving terrain of large-scale data ecosystems. Keywords: Data Mesh, Big Data, Machine Learning, Artificial Intelligence, Distributed Systems, Data Architecture, Decentralization.