Metadata-driven data pipelines: A scalable architecture for cloud-native enterprise data integration

Paulraj, Terance Joe Heston Joseph (2025) Metadata-driven data pipelines: A scalable architecture for cloud-native enterprise data integration. World Journal of Advanced Engineering Technology and Sciences, 15 (3). pp. 1232-1240. ISSN 2582-8266

Metadata-driven data pipelines represent a transformative approach to cloud-native data engineering, addressing the limitations of traditional hand-coded solutions that struggle with complexity and scale. This architectural pattern decouples transformation logic from execution by storing pipeline definitions as structured metadata, which is dynamically interpreted at runtime. The resulting framework enables organizations to automate pipeline development, enforce consistent standards, and adapt rapidly to changing business requirements. In cloud environments characterized by distributed teams and evolving data schemas, this approach delivers significant advantages in development velocity, operational efficiency, and governance capabilities. By externalizing pipeline logic into configurable metadata, organizations can streamline source onboarding, ensure compliance, and establish the foundation for advanced data initiatives, including AI-driven analytics and self-service data access.