Automating Data Quality in Cloud Data Lakes: A Metadata-Driven Approach

Abstract

Cloud-based data lake architectures have transformed organizational data management capabilities, yet maintaining consistent data quality across distributed environments remains challenging. This work presents a comprehensive metadata-driven automation framework that addresses quality assurance through systematic validation processes. The framework integrates automated metadata extraction with rule-based validation engines to establish continuous quality monitoring capabilities. Key components include structural validation mechanisms, content assessment protocols, and automated remediation workflows. The proposed solution demonstrates how metadata repositories can serve as foundational elements for implementing scalable quality controls. Pipeline integration patterns enable real-time validation while maintaining system performance across diverse data sources. Quality metrics dashboards provide visibility into data health indicators, supporting proactive quality management. Automated remediation capabilities reduce manual intervention requirements through intelligent error classification and correction mechanisms. Governance integration ensures compliance alignment while maintaining audit trails for regulatory requirements. The framework's modular design accommodates various cloud environments and data processing patterns. Quality assessments reveal enhanced data consistency throughout enterprise systems while decreasing operational disruptions caused by validation failures. The metadata-driven framework provides robust infrastructure supporting automated quality management at an organizational scale within distributed cloud platforms.