Automated Metadata Governance Frameworks for Large-Scale Cloud Data Warehouse Migrations
DOI:
https://doi.org/10.63282/3050-9416.IJAIBDCMS-V1I1P105Keywords:
Metadata governance, cloud migration, data warehouse, automated lineage, data qualityAbstract
Large-scale data warehouse migrations to cloud platforms present significant challenges in maintaining metadata consistency, data lineage, and governance compliance. This paper presents a simulation-based evaluation of automated metadata governance frameworks specifically designed for enterprise cloud data warehouse migrations. Using controlled simulation environments, we evaluate the feasibility of integrating machine learning-based metadata extraction, automated lineage mapping, and real-time governance enforcement to ensure seamless migration while maintaining data quality and regulatory compliance. Our simulation framework employs synthetic enterprise datasets, standardized migration scenarios, and automated validation protocols to assess the potential of automated metadata governance approaches. We implemented a controlled testing environment that simulates complex schema transformations, referential integrity maintenance, and real-time governance dashboard functionality throughout simulated migration processes. The simulation study evaluates metadata governance across multiple enterprise migration scenarios involving synthetic datasets equivalent to 50TB+ of data and 10,000+ database objects. Results from controlled experiments demonstrate 78% potential reduction in manual metadata reconciliation efforts, 92% accuracy in automated lineage mapping, and 100% compliance maintenance during simulated migration phases. The simulation successfully handled complex schema transformations and maintained referential integrity across 15,847 synthetic database objects, providing insights into the feasibility and limitations of automated metadata governance in large-scale cloud migrations
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