DATA MESH: A MODERN APPROACH TO SCALABLE CLOUD DATA ARCHITECTURE

Abstract

Data Mesh architecture represents a paradigm shift in cloud-based data management, offering organizations a scalable and decentralized approach to handling complex data ecosystems. This article explores the fundamental principles, implementation strategies, and organizational impacts of Data Mesh in cloud environments. By examining the four core principles—domain-oriented decentralization, data as a product, self-serve infrastructure, and federated computational governance—this article demonstrates how organizations can transform their data architecture to meet modern scaling challenges. The article presents a detailed analysis of the three-layer implementation model, comprising infrastructure, data product, and discovery layers while addressing critical success factors and governance considerations. Through an examination of both technical and organizational requirements, this article provides insights of the challenges and benefits of Data Mesh adoption. This article indicates that successful implementation requires a balanced approach to governance, strong executive sponsorship, and a cultural shift toward product thinking for data. This article concludes that organizations implementing Data Mesh can achieve significant improvements in scalability, data quality, and development agility, though success depends on careful consideration of organizational readiness and technical capabilities.