Metadata-Driven Data Transformation in Data Mesh Architectures

Abstract

In the evolving landscape of data architectures, the Data Mesh paradigm has emerged as a scalable and decentralized approach to managing complex data ecosystems. However, as data domains grow increasingly autonomous, ensuring consistency, discoverability, and transformation standardization across domains becomes challenging. This paper proposes a metadata-driven approach to data transformation within Data Mesh architectures. By leveraging active metadata such as data lineage, transformation rules, quality metrics, and semantic definitions data domains can automate and orchestrate transformations in a consistent, governed, and scalable manner. The paper explores architectural patterns, implementation considerations, and case studies demonstrating the benefits of this approach in enhancing interoperability, governance, and agility in enterprise data systems.