Zero-ETL Architectures and Serverless Data Warehousing: Automated Multi-Region Expansion Strategies for Cloud-Native Analytics

The rapid evolution of cloud-native analytics has fundamentally transformed how organizations manage and analyze data across distributed environments. Traditional extract, transform, load (ETL) pipelines have long been the backbone of data integration, yet they introduce significant latency, operational complexity, and maintenance overhead. Zero-ETL architectures represent a paradigm shift that eliminates these intermediate transformation layers by enabling direct data access and real-time synchronization between operational databases and analytical systems. Concurrently, serverless data warehousing (SDW) has emerged as a transformative approach that decouples compute from storage, provides automatic scaling, and operates on a pay-per-query pricing model. This review examines the convergence of zero-ETL architectures and SDW technologies, with particular emphasis on automated multi-region expansion strategies that enable global data dist ribution , disaster recovery, and regulatory compliance. We analyze the architectural patterns, te chnological implementations, and operational considerations that define modern cloud-native analytics platforms. The integration of zero-ETL with serverless infrastructure presents unique opportunities for reducing data movement, minimizing transformation costs, and enabling near-real-time analytics across geographically distributed deployments. This paper synthesizes recent advances in automated replication mechanisms, consistency models, query federation techniques, and cost optimization strategies that characterize contemporary multi-region data architectures.