Modernizing Data Warehouses Through Event-Driven Cloud Computing

Abstract

Contemporary organizations increasingly depend on data-intensive decision-making, yet the velocity, variety, and volume of digital traces produced by modern applications have outpaced the capabilities of monolithic data warehouses. The convergence of microservices, event-driven architectures, real-time streaming, and serverless computing has therefore catalyzed a paradigmatic shift toward cloud-native data warehousing. This article develops a comprehensive theoretical and analytical account of how event-driven and microservice-based systems can be architected to support scalable, secure, and low-latency analytics pipelines, with particular emphasis on the operationalization of cloud data warehouses such as Amazon Redshift as described by Worlikar, Patel, and Challa (2025). Drawing on a wide spectrum of literature that spans distributed systems, requirements engineering, security, and real-time messaging, the article constructs an integrated framework that links upstream event generation, streaming infrastructures, and downstream analytical persistence. The abstract problem addressed is the persistent gap between transactional microservice workloads and analytical platforms, a gap that manifests as data inconsistency, latency, governance challenges, and escalating operational complexity (Laigner et al., 2021; Koyuncu & Şahin, 2020). By synthesizing event sourcing patterns (Lewis & Fowler, 2020), messaging infrastructures such as Kafka (Kreps et al., 2011) and Kinesis (Lakshman & Malik, 2020), and the pragmatic recipes for Redshift-centric pipelines articulated by Worlikar et al. (2025), this research proposes a cohesive methodological approach for building intelligence-driven data warehouses. The contribution is not an empirical experiment but a rigorous, literature-grounded analytical study that interprets existing practices as a coherent architectural paradigm. Results are presented as interpretive findings that demonstrate how event-driven ingestion, schema-evolution strategies, and serverless compute together enable near-real-time business intelligence, predictive analytics, and DevOps efficiency (Kumar, 2019). The discussion situates these findings within broader scholarly debates on cloud security (Rong et al., 2013), quality of service (Alhamazani et al., 2014), and agile requirements evolution (Kasauli et al., 2021), ultimately arguing that modern data warehouses must be treated as dynamic, software-defined platforms rather than static repositories. The article concludes that the synthesis of event-driven microservices and cloud-native warehousing, when guided by disciplined design and governance, constitutes the most viable path toward resilient, future-proof analytics infrastructures.