Cloud-Native Framework for Large-Scale Big Data Governance

Abstract

 The data-asset management system, data-quality assurance, lineage tracking, security compliance, and intelligent decision support now all depend on large-scale big data governance. Conventional governance systems are not appropriate for scaling with heterogeneous data sources, frequent metadata updates, and multi-tenant governance workloads since they often feature centralized metadata repositories and monolithic task engines. This study proposes a Cloud-Native Framework for Large-Scale Big Data Governance. Incorporate distributed metadata management, lineage graph coordination, policy-based access control, data quality rule execution, containerized governance services, and elastic governance job scheduling into the system. For dispersed data assets, a lightweight governance control plane will perform audit tasks, update lineage information, and manage metadata collection and quality evaluation. In comparison to the centralized governance deployment, the experimental analysis shows that the proposed framework has improved elastic scaling efficiency by 31.6%, decreased average metadata query latency by 47.3%, and increased governance task throughput from 8,600 to 18,900 tasks per minute. The study offers a workable, engineering-focused architecture for building flexible big data governance systems in cloud-native settings.