The contemporary data ecosystem is defined by the explosive growth of heterogeneous, high-velocity, and high-volume datasets that are increasingly processed within cloud-native data warehousing platforms. These environments promise unprecedented scalability, elasticity, and analytical sophistication, yet they simultaneously introduce profound security and privacy challenges that extend well beyond the concerns of traditional on-premise data management. Distributed architectures, multi-tenant infrastructures, and complex data life cycles generate an intricate threat surface that demands systematic, theoretically grounded, and empirically informed approaches to protection. This article develops a comprehensive and original analysis of security and privacy in cloud-based big data warehousing by synthesizing perspectives from distributed systems theory, big data security scholarship, and modern data warehouse engineering practices. In particular, the architectural and operational principles articulated in contemporary cloud data warehouse platforms, as exemplified by Amazon Redshift, are treated not merely as engineering choices but as socio-technical constructs that reconfigure trust, accountability, and risk within data-driven organizations (Worlikar, Patel, & Challa, 2025).

The study begins by situating cloud-native data warehouses within the historical evolution of distributed systems, tracing how reliability, fault tolerance, and security principles originally developed for tightly controlled enterprise networks have been transformed by the rise of virtualized, globally distributed cloud infrastructures (Birman, 2005; Tanenbaum & van Steen, 2007). It then integrates big data security and privacy research that highlights the vulnerability of the entire data life cycle, from ingestion and storage to analytics and sharing (Koo, Kang, & Kim, 2020; Venkatraman & Venkatraman, 2019). Through a qualitative, literature-driven methodological design, this article interprets how architectural components such as shared-nothing clusters, columnar storage, massively parallel processing, and serverless elasticity alter the classical assumptions of access control, encryption, auditing, and trust boundaries.

By grounding its analysis in both distributed systems theory and modern data warehouse practice, including the operational recipes and architectural patterns discussed by Worlikar et al. (2025), this research contributes a holistic framework for understanding and governing security and privacy in the era of cloud-based analytics. The article concludes by outlining implications for system designers, data governance professionals, and researchers, emphasizing that future progress will depend not only on stronger cryptography or access controls but also on transparent architectures, accountable service models, and ethically informed data practices.

Cloud-native data warehousing, Big data security, Distributed systems

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