Sarcouncil Journal of Multidisciplinary

Abstract: As data volumes grow exponentially and traditional centralized architectures strain under the weight of modern enterprise demands, Data Mesh has emerged as a paradigm-shifting solution to scale data infrastructure and improve data ownership. This article explores the conceptual evolution, practical design principles, and implementation challenges associated with Data Mesh, a decentralized approach to data architecture that treats data as a product and distributes ownership to domain-specific teams. Unlike traditional monolithic data lakes or warehouses managed by centralized teams, Data Mesh advocates for federated governance, self-serve platform tooling, and domain-oriented decentralized data ownership. These principles enable organizations to unlock agility, scale, and quality by bringing data engineering responsibilities closer to subject matter experts. Drawing on production-proven use cases, the article dissects how Data Mesh resolves bottlenecks in ingestion, transformation, and discoverability, particularly in large-scale, multi-business-unit enterprises. The technological underpinnings essential for Data Mesh success include metadata-driven pipeline automation, policy-as-code enforcement, and scalable schema evolution. Frameworks such as Delta Lake, Apache Iceberg, and tools like AWS Lake Formation are examined in the context of enabling decentralized yet interoperable data products. Through real-world implementation insights, including experiences building modular, reusable frameworks such as Intelligent Data Foundation (IDF) on AWS, the article demonstrates how these serve as the foundation for Data Mesh success. Best practices for governance, lineage tracking, and cost-aware data ownership in distributed teams are outlined, contributing to the growing body of scholarly literature by clarifying the architectural, organizational, and cultural shifts required for sustainable Data Mesh adoption.