Metadata-Centric Semantic Layer Management in Multi-Domain Pipelines

Abstract

In modern data-driven ecosystems, multi-domain data pipelines present significant challenges in terms of interoperability, scalability, governance, and semantic consistency. Traditional data integration methods often falter when faced with the heterogeneity and velocity of domain-specific data. This paper proposes a metadata-centric approach to managing the semantic layer in multi-domain data pipelines. By elevating metadata to a first-class citizen, organizations can dynamically model, interpret, and govern data semantics across domains without excessive data movement or manual schema alignment. We introduce a reference architecture and management framework that leverages active metadata, semantic annotations, knowledge graphs, and policy-driven governance to enable scalable, federated analytics. Case studies and experimental evaluation demonstrate the effectiveness of the approach in improving query performance, data lineage traceability, and cross-domain interoperability. The proposed methodology aligns with modern data mesh principles and promotes sustainable data infrastructure design.