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Designing Elastic And Cost-Efficient Cloud Data Warehouses Using Serverless Technologies
Prof. Stefan Petrov , University of Valparaíso, ChileAbstract
The accelerating convergence between serverless computing and cloud-native data warehousing has become one of the most consequential architectural transformations in contemporary information systems. Over the past decade, enterprises have moved from monolithic, on-premise data warehouses toward elastic, cloud-hosted analytical platforms that promise virtually unlimited scalability, fine-grained cost control, and rapid innovation. In parallel, the emergence of serverless computing has introduced an execution paradigm in which infrastructure management is almost entirely abstracted away from developers, enabling applications to scale transparently while charging only for actual consumption. While these two trends have often been discussed independently, their interaction is reshaping how data warehouses are designed, optimized, governed, and economically justified. This article develops a comprehensive theoretical and empirical synthesis of serverless-enabled cloud data warehousing architectures, with particular attention to how such models influence performance predictability, cost governance, operational resilience, and analytical agility.
Grounded in the existing body of scholarly work on serverless systems and cloud data platforms, and anchored by detailed architectural guidance from Worlikar, Patel, and Challa’s Amazon Redshift Cookbook (Worlikar et al., 2025), the study examines how data warehousing workloads can be decomposed into event-driven, function-based services that coexist with persistent, columnar storage engines. Drawing upon performance evaluations of production serverless environments, economic analyses of consumption-based billing, and workload characterizations from large-scale cloud providers, the paper constructs an interpretive framework that explains why certain analytical tasks benefit from serverless execution while others remain better suited to provisioned clusters (Lee et al., 2018; Shahrad et al., 2020; Adzic & Chatley, 2017). By integrating these perspectives, the article demonstrates that serverless data warehousing is not a wholesale replacement of traditional architectures but a layered, hybrid model in which ephemeral compute complements long-lived data services.
Methodologically, the research adopts a qualitative, literature-driven analytical approach, synthesizing results from peer-reviewed studies, industry-grounded technical literature, and design patterns documented in modern data warehouse engineering practice. This approach enables a nuanced interpretation of performance trade-offs, particularly around cold-start latency, concurrency management, and data locality, which are critical to analytical workloads that often demand both throughput and predictability (Lloyd et al., 2018; Eismann et al., 2021). The findings indicate that when orchestrated carefully, serverless components can significantly improve the elasticity and cost efficiency of extract-transform-load processes, ad hoc query bursts, and real-time data enrichment pipelines, while persistent warehouse engines such as Amazon Redshift continue to provide optimized query execution, indexing, and storage management (Worlikar et al., 2025).
The discussion further situates these results within broader debates about cloud governance, security, and multi-cloud interoperability. Serverless architectures introduce new challenges for observability, compliance, and zero-trust security models, especially when analytical workflows span multiple providers and edge environments (Hassan et al., 2021; Enhancing cloud security…, 2016). By critically engaging with these issues, the paper argues that future data warehousing strategies must evolve from purely infrastructural considerations toward holistic, policy-driven frameworks that balance agility with control. Ultimately, the article contributes a theoretically grounded and practically informed account of how serverless computing is redefining the boundaries of cloud data warehousing, offering both scholars and practitioners a roadmap for navigating this rapidly changing landscape.
Keywords
Serverless computing, Cloud data warehousing, Amazon Redshift
References
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