Federated Data Governance Framework for Ensuring Quality-Assured Data Sharing and Integration in Hybrid Cloud-Based Data Warehouse Ecosystems through Advanced ETL/ELT Techniques

Abstract— As enterprises progressively implement hybrid cloud architectures, overseeing data across several environments poses issues in sustaining quality, consistency, and governance. This study presents a federated data governance paradigm that facilitates dependable data sharing and integration inside hybrid cloud-based data warehouse ecosystems, while maintaining the independence of particular data domains. This concept, in contrast to conventional centralized methods, integrates governance principles into Extract, Transform, Load (ETL) and Extract, Load, Transform (ELT) procedures, thereby balancing
flexibility with oversight. The framework guarantees efficient data transfer while maintaining accuracy, reliability, and regulatory compliance by incorporating data quality checks, transformation logic, and compliance procedures at each stage. Anomaly detection with machine learning and adaptive workload tuning improves responsiveness, facilitating proactive governance that adjusts to changing data environments and infrastructure limitations. Moreover, centralized supervision via metadata management, policy enforcement, and lineage tracking enhances visibility and control, allowing companies to manage distributed data assets with increased assurance. This paper illustrates, through practical applications, how federated governance
mitigates data silos, optimizes compliance initiatives, and bolsters confidence in analytical results, thereby facilitating
enterprises in deriving significant insights from their data. The suggested architecture provides a scalable and intelligent method for data management, meeting the performance, agility, and regulatory requirements of contemporary hybrid cloud  infrastructures. In order to enable long-term corporate innovation and decision-making, this research helps to build sustainable, high-quality data practices by advocating for a structured yet adaptable governance approach.

Keywords— Federated Data Governance, Hybrid Cloud Architecture, Data Quality Assurance, Data Warehouse Integration, Advanced ETL/ELT Pipelines, Metadata Management Data Lineage, Regulatory Compliance, DistributedDataManagement.