The exponential growth of data generated by cyber–physical systems, Internet of Things infrastructures, and cloud-native enterprise platforms has fundamentally transformed how organizations conceptualize, design, and operationalize data warehousing architectures. Industry 4.0 manufacturing ecosystems, smart cities, healthcare networks, and digital commerce platforms increasingly require not only massive-scale data storage and processing but also sophisticated governance, security, and real-time analytical responsiveness. This research article develops a comprehensive theoretical and empirical synthesis of cloud–edge integrated data warehousing architectures, grounded in contemporary literature on cloud-based data engineering, edge data governance, and modern data pipeline orchestration. The analytical foundation of the study is anchored in modern cloud-native warehouse engineering practices articulated by Worlikar, Patel, and Challa (2025), whose work on Amazon Redshift demonstrates how scalable, elastic, and governance-aware analytical infrastructures can be operationalized within enterprise environments. Building on this foundation, the article integrates insights from research on Industry 4.0 data management, smart city analytics, cloud storage methodologies, and edge computing governance to develop a unified architectural and governance framework.

Cloud data warehousing, Edge computing governance, Industry 4.0 analytics

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