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Event-Driven And Serverless Data Warehousing Architectures For Cloud-Native Analytics: Integrating Microservices, Edge Processing, And Amazon Redshift-Centric Design

Dr. Bruno Varela , Faculty of Engineering, University of Tartu, Estonia

Abstract

The contemporary data landscape is increasingly defined by unprecedented volumes, velocities, and varieties of digital traces produced by distributed applications, Internet-of-Things ecosystems, media platforms, and enterprise microservices. These conditions have exposed the limitations of traditional, monolithic data warehousing architectures that were designed for batch-oriented, centrally managed, and relatively predictable workloads. In response, cloud-native data warehousing has emerged as a paradigm that integrates elastic compute, object-based storage, and highly automated orchestration mechanisms, while increasingly relying on serverless and event-driven architectures to cope with real-time analytical demands. This article develops an extensive theoretical and empirical exploration of how event-driven and serverless computing models transform the design, operation, and governance of modern data warehouses, with particular attention to Amazon Redshift as a representative industrial platform for large-scale analytical processing (Worlikar, Patel, & Challa, 2025).

Drawing upon an interdisciplinary corpus of literature on serverless computing, microservices, edge architectures, and big data analytics, this study synthesizes insights from both foundational and recent research to articulate a unified conceptual framework for cloud-native, event-driven data warehousing. The article situates serverless functions and event brokers not merely as auxiliary components but as epistemic infrastructures that reshape how data is ingested, transformed, and consumed across distributed analytical ecosystems. By linking the theoretical insights of the serverless trilemma, which highlights trade-offs between composition, scalability, and performance (Baldini et al., 2017), with empirical findings on event-driven microservice coordination and technical debt (de Toledo et al., 2021; Laigner et al., 2024), the study demonstrates how architectural decisions propagate into long-term data governance and analytical reliability.

Methodologically, the article adopts a qualitative, literature-driven analytical approach that systematically integrates technical, organizational, and socio-economic dimensions of data warehousing. Rather than offering a narrow case study, the analysis constructs a generalized interpretive model that explains how platforms such as Amazon Redshift are increasingly embedded within event-driven pipelines that include serverless ingestion, edge-based preprocessing, and asynchronous microservice orchestration (Baresi et al., 2017; Kanso & Youssef, 2017). The results reveal that the combination of Redshift’s columnar, massively parallel processing engine with serverless and event-driven front-ends enables a new form of analytical elasticity, in which data warehouses become continuously adaptive rather than statically provisioned (Worlikar et al., 2025).

The discussion critically evaluates the benefits and limitations of this architectural convergence. While event-driven and serverless designs offer unprecedented scalability, cost efficiency, and responsiveness, they also introduce new forms of complexity, opacity, and architectural technical debt that challenge traditional governance and optimization strategies (Chavan, 2021; de Toledo et al., 2021). The article concludes that the future of data warehousing will be neither purely serverless nor purely stateful, but rather a hybrid ecosystem in which platforms like Amazon Redshift operate as stable analytical cores surrounded by highly dynamic, event-driven peripheries. By providing a deeply elaborated theoretical and practical account, this study contributes to a more nuanced understanding of how cloud-native analytics infrastructures can be designed to support the next generation of data-intensive applications.

Keywords

Cloud-native data warehousing, Event-driven architecture, Serverless computing

References

Garcia, A., et al. A study of transcoding on cloud environments for video content delivery. CiteSeerX.

Baldini, I., et al. Serverless computing: Current trends and open problems. Research Advances in Cloud Computing.

Worlikar, S., Patel, H., & Challa, A. Amazon Redshift Cookbook: Recipes for building modern data warehousing solutions. Packt Publishing Ltd.

Lynn, T., et al. A preliminary review of enterprise serverless cloud computing platforms. IEEE Xplore.

Al-Ali, A. R., et al. A smart home energy management system using IoT and big data analytics approach. IEEE Transactions on Consumer Electronics.

de Toledo, S. S., et al. Identifying architectural technical debt, principal, and interest in microservices. ScienceDirect.

Gupta, P. Patterns for microservices – Sync vs Async. Medium.

Baresi, L., et al. Empowering low-latency applications through a serverless edge computing architecture. Service-Oriented and Cloud Computing.

Laigner, R., et al. An empirical study on challenges of event management in microservice architectures. arXiv.

Kanso, A., & Youssef, A. Serverless. Proceedings of the 2nd International Workshop on Serverless Computing.

Cabane, H., & Farias, K. On the impact of event-driven architecture on performance: An exploratory study. ScienceDirect.

Gessert, F., et al. Quaestor. Proceedings of the VLDB Endowment.

Chavan, A. Exploring event-driven architecture in microservices – patterns, pitfalls and best practices. ResearchGate.

Ian Rudd. Microservices architecture using Netflix tech stack – conceptual view. ResearchGate.

Lundberg, M. Evaluation of a backend for computer games using a cloud service. DIVA.

Baldini, I., et al. The serverless trilemma: Function composition for serverless computing. Proceedings of Onward! 2017.

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How to Cite

Dr. Bruno Varela. (2026). Event-Driven And Serverless Data Warehousing Architectures For Cloud-Native Analytics: Integrating Microservices, Edge Processing, And Amazon Redshift-Centric Design. International Journal of Economics Finance & Management Science, 11(01), 14–27. Retrieved from https://scientiamreearch.org/index.php/ijefms/article/view/267