The contemporary evolution of data-intensive applications is increasingly shaped by the convergence of microservice architectures, event-driven computing paradigms, and serverless execution models. Within this transformation, data warehousing platforms are no longer passive repositories of historical data but have become active, real-time analytical backbones that mediate operational decision-making, organizational learning, and algorithmic automation. This article develops a comprehensive theoretical and empirical synthesis of how event-driven microservices and serverless infrastructures reshape modern cloud data warehousing, with particular attention to performance, scalability, architectural governance, and technical debt. Drawing upon established architectural theory, empirical studies of microservice event management, and platform-specific design patterns, the analysis demonstrates that the shift toward asynchronous, event-centric data pipelines introduces both unprecedented analytical agility and new forms of architectural fragility.

The study is grounded in the architectural patterns and operational recipes articulated for Amazon Redshift-based warehousing environments, which illustrate how distributed compute, decoupled ingestion, and materialized analytical views can be orchestrated into a coherent analytical ecosystem (Worlikar et al., 2025). These platform-specific insights are integrated with broader research on event-driven microservices, including performance trade-offs, failure propagation, and operational complexity (Cabane & Farias, 2024; Laigner et al., 2024; Chavan, 2021). Serverless computing literature further contextualizes these transformations by highlighting the socio-technical consequences of ephemeral execution, cost elasticity, and infrastructural abstraction (Baldini et al., 2017; Castro et al., 2019; Hellerstein et al., 2018).

By synthesizing these insights, the article contributes a theoretically grounded and practically relevant framework for understanding how modern data warehousing can be designed to support both real-time analytics and long-term organizational learning. The implications extend beyond technical optimization, suggesting that the future of data warehousing is inseparable from broader questions of organizational governance, software sustainability, and the political economy of cloud computing.

Event-driven architecture, microservices, serverless computing

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