Multi-Cloud Data Federation Models for Unified Business Intelligence Insights
DOI:
https://doi.org/10.63282/3050-9416.IJAIBDCMS-V6I4P134Keywords:
Multi-Cloud Data Federation, Data Virtualization, Federated Query Engine, Unified Business Intelligence (BI), Cross-Cloud Query Optimization, Predicate Pushdown, Semi-Join Reduction, Cost-Aware Query Planning, Egress Cost Modeling, Bounded-Staleness Freshness, Semantic Abstraction Layer, Global Schema Mapping, Schema Heterogeneity, Interoperability, Data Governance And Policy Propagation, Row-Level And Column-Level Security, Unified Identity And Access Management (IAM), Metadata Catalog And Lineage, Caching And Materialized Views, Distributed Query Execution, Real-Time AnalyticsAbstract
Multi-cloud data federation has emerged as a pivotal approach for enterprises seeking unified business intelligence (BI) across distributed cloud data sources. This paper presents a comprehensive study of architectures and techniques that enable data virtualization, real-time data access, and interoperability across cloud providers. We describe an academic-grade architecture for federated query engines and semantic abstraction layers that integrate heterogeneous cloud databases and data lakes into a unified virtual data layer. Implementation details of federated query processing, data integration platforms, and semantic metadata management are discussed, with emphasis on query decomposition, source connectors, and global schema mapping. We evaluate the design through analysis of query optimization strategies and a case study of a federated query engine spanning multiple public clouds. The results demonstrate that intelligent federation (e.g., push-down of operations to sources, caching, and distributed execution) can significantly reduce cross-cloud data movement and query latency, improving performance for real-time analytics. Key challenges – including network latency, distributed query optimization, security and access control, schema heterogeneity, and compliance – are examined in depth. We discuss practical solutions and best practices to mitigate these issues, such as caching strategies, learned federated optimizers, and unified identity management. The paper’s contributions provide a foundation for building multi-cloud data federation models that deliver unified BI insights with the flexibility of data virtualization and the rigor of enterprise data governance.
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