A Unified Framework For Serverless-Native Data Warehousing In Cloud Environments

Abstract

The rapid evolution of cloud computing has fundamentally reshaped how data is stored, processed, and transformed into actionable intelligence. Over the last decade, the convergence of serverless computing, distributed database systems, and cloud-native data warehousing has given rise to an architectural paradigm that prioritizes elasticity, fine-grained resource allocation, and performance-aware economic governance. Yet, despite the widespread industrial adoption of serverless platforms and managed data warehouses, the academic literature continues to treat these domains as largely separate bodies of inquiry. Serverless research has traditionally focused on execution models, cold-start latencies, and cost structures, while data warehousing research has emphasized schema design, query optimization, and transaction management. The absence of a unified analytical framework has resulted in fragmented guidance for organizations attempting to build high-performance, cost-efficient, and reliable analytics infrastructures on serverless foundations.

This article addresses this gap by developing a comprehensive, theoretically grounded, and empirically informed framework for understanding how modern cloud data warehouses—particularly those based on managed platforms such as Amazon Redshift—can be systematically integrated with serverless execution, event-driven orchestration, and intelligent resource management. Drawing upon the architectural recipes and operational principles articulated in Worlikar, Patel, and Challa’s Amazon Redshift Cookbook (2025), this study situates Redshift not merely as a query engine, but as a central node in a broader serverless data ecosystem that includes function-as-a-service platforms, event buses, and distributed microservices. The Cookbook’s emphasis on modularity, workload isolation, and performance tuning provides a practical anchor for an otherwise highly abstract theoretical discourse.

Building on foundational models of serverless performance and cost (Lin et al., 2020) and recent advances in joint resource management and pricing (Tütüncüoğlu, 2024), the article constructs a conceptual bridge between economic optimization in serverless platforms and workload management in analytical databases. At the same time, it incorporates insights from multi-tenant in-memory data grids (Das and Mueller, 2017) and high-performance computing variability on clouds (El-Khamra and Kim, 2011) to explain why performance unpredictability remains a central challenge when data warehouses are deployed atop elastic, shared infrastructures.

Through a text-based, integrative methodology that synthesizes these diverse strands of literature, this article presents a set of interpretive results demonstrating that serverless-native data warehousing is not simply a cost-saving tactic but a qualitatively new mode of organizing computational labor. The discussion elaborates how event-driven patterns, saga-based persistence, and pub-sub integration architectures redefine the temporal and economic logic of analytical workloads (Amazon Web Services, 2024; Google Cloud Platform, 2025). Ultimately, the study argues that the future of cloud data warehousing lies in the co-evolution of platform economics, intelligent transaction management, and architectural recipes that embed performance awareness directly into system design, as exemplified by contemporary Redshift-centric practices (Worlikar et al., 2025).