Revolutionizing Data Warehouse Migration with Multi-Cloud Computing
DOI:
https://doi.org/10.32996/jcsts.2025.7.7.14Keywords:
Multi-Cloud Computing, Data Warehouse Migration, Cross-Cloud Optimization, Distributed Data Integration, AI-Driven Workload ManagementAbstract
This article explores the transformative potential of Multi-Cloud Computing (MCC) in revolutionizing data warehouse migration strategies across heterogeneous environments. MCC architectures enable seamless data movement from diverse source systems including relational databases, NoSQL repositories, and streaming platforms into modern cloud data warehouses while optimizing resources across multiple cloud providers. The article examines comprehensive aspects of MCC implementation, from initial data source integration through destination warehouse optimization, AI-driven workload management, and sophisticated governance frameworks. By abstracting underlying infrastructure differences between cloud platforms, MCC creates unified control planes that intelligently route data processing based on performance, cost, and compliance requirements rather than provider limitations. The article demonstrates how distributed processing engines working across cloud boundaries achieve substantial improvements in migration performance while reducing source system impact and operational costs. Advanced capabilities, including automated schema mapping, intelligent transformation optimization, comprehensive lineage tracking, and cross-cloud security frameworks, collectively address traditional migration challenges that have historically imposed significant risk and cost on organizations. This article establishes MCC as not merely a technical architecture but a strategic approach enabling data agility in increasingly complex multi-cloud environments, ultimately transforming how organizations conceptualize and implement data warehouse migrations.