Reconfiguring Event-Driven Serverless Data Warehousing: Architectural, Operational, And Analytical Implications For Cloud-Native Analytics

Abstract

The convergence of event-driven architectures, serverless computing, and cloud-native data warehousing has produced one of the most consequential shifts in contemporary data engineering. Over the last decade, enterprises have moved from monolithic extract–transform–load pipelines toward elastic, decoupled, and highly automated data flows that are capable of responding to real-time business signals. This transformation has been driven not only by the maturation of Function-as-a-Service platforms and managed streaming frameworks, but also by the evolution of modern analytical databases that abstract infrastructure while preserving performance and governance. Within this context, the architectural synthesis between serverless orchestration layers and scalable data warehouses such as Amazon Redshift has become a focal point of both industrial practice and scholarly inquiry, as illustrated in the comprehensive design patterns articulated by Worlikar, Patel, and Challa (2025) and the broader theoretical foundations of serverless computing articulated by Castro et al. (2019), Fox (2017), and Baldini et al. (2017).

This article develops a deeply elaborated analytical framework for understanding how event-driven serverless systems can be integrated with cloud-native data warehouses to support near real-time, resilient, and economically efficient analytics. Rather than treating serverless platforms and data warehouses as separate layers, the study conceptualizes them as a single socio-technical system in which execution models, storage semantics, and orchestration logics co-evolve. Drawing on the architectural insights of Redshift-centric data warehousing practices (Worlikar et al., 2025), the empirical observations of serverless platform behavior (Wang et al., 2018), and the critical perspectives on the limitations of stateless execution (Hellerstein et al., 2019; Zhang, 2019), the article proposes that event-driven serverless pipelines fundamentally reshape how data latency, reliability, and governance are negotiated.

Methodologically, the study adopts a qualitative and design-oriented synthesis of the literature, combining multivocal sources from industrial blogs, open-source frameworks, and peer-reviewed research. Event-based integration patterns (Naeem et al., 2008), serverless design patterns (Taibi et al., 2020), and security-by-design approaches (Hong et al., 2018) are analyzed in relation to streaming infrastructures such as Kafka, Flink, Flume, and Airflow, which together constitute the connective tissue between ephemeral compute and persistent analytical storage. The findings reveal that while serverless data warehousing pipelines offer unprecedented elasticity and cost transparency, they also introduce new forms of architectural fragility associated with cold starts, hidden state, and operational observability.

The article concludes that the future of cloud analytics lies not in replacing traditional data warehouses, but in re-embedding them within event-driven serverless ecosystems that continuously ingest, transform, and materialize data in response to business events. By situating Amazon Redshift within this broader paradigm, the study contributes a theoretically grounded and practically relevant account of how organizations can design data infrastructures that are simultaneously scalable, agile, and analytically robust.