The accelerating digitization of economic and social activity has transformed data into a central productive resource, demanding analytical infrastructures capable of storing, integrating, and processing unprecedented volumes of heterogeneous information at scale. Cloud-native data warehousing has emerged as a foundational response to this demand, enabling elastic, distributed, and service-oriented analytical platforms that diverge fundamentally from traditional on-premise data warehouse architectures. Within this rapidly evolving landscape, Amazon Redshift has become one of the most influential and widely deployed systems, shaping both industry practices and academic understandings of cloud data warehousing. This research article develops a comprehensive theoretical and analytical study of cloud-native data warehousing with a particular emphasis on Amazon Redshift, situating it within broader debates about cloud computing, big data platforms, and modern analytics pipelines. Drawing extensively on the technical, architectural, and operational insights articulated in Worlikar, Patel, and Challa’s Amazon Redshift Cookbook (2025), the study integrates practitioner-oriented design patterns with scholarly frameworks of distributed systems, service-oriented computing, and data warehousing theory. The article argues that Redshift represents not merely an incremental technological upgrade but a paradigmatic shift toward simplified, managed, and deeply integrated analytical infrastructures that fundamentally alter how organizations conceptualize data storage, query processing, governance, and scalability.

Through a methodologically rigorous synthesis of documentation, scholarly literature, and architectural case studies, the research analyzes Redshift’s core design principles, including its columnar storage model, massively parallel processing architecture, decoupled storage and compute layers, concurrency scaling mechanisms, and tight integration with the Amazon Web Services ecosystem. These features are examined comparatively against alternative cloud data warehouse and analytics platforms offered by Microsoft Azure and Google Cloud, as well as against open-source big data frameworks and legacy on-premise data warehouse systems. The analysis demonstrates that Redshift’s architectural philosophy reflects broader trends in cloud computing toward abstraction, automation, and elasticity, while also revealing tensions between vendor-specific optimization and multi-cloud interoperability. The results indicate that while Redshift achieves high levels of performance, operational simplicity, and economic efficiency for many workloads, it also raises critical questions about data lock-in, governance complexity, and the long-term sustainability of highly specialized proprietary ecosystems.

The discussion extends these findings by situating Redshift within ongoing theoretical debates about data warehouse as a service, platformization, and the political economy of cloud infrastructure. By critically engaging with both supportive and skeptical perspectives in the literature, the article outlines how Redshift both exemplifies and complicates the promise of cloud-native analytics. It concludes that understanding Redshift’s role in modern data ecosystems requires moving beyond purely technical evaluations toward a more holistic appreciation of how cloud data warehouses reshape organizational power, knowledge production, and the future trajectory of digital economies.

Cloud data warehousing, Amazon Redshift, big data analytics

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