CLOUD-NATIVE COLUMNAR DATA WAREHOUSES: COMPARATIVE ARCHITECTURAL ANALYSIS OF AMAZON REDSHIFT, AZURE SYNAPSE, AND GOOGLE BIGQUERY IN MODERN ANALYTICS ECOSYSTEMS

Abstract

Cloud-native data warehousing has emerged as a foundational paradigm for contemporary analytics, driven by the explosive growth of digital data, the globalization of enterprise operations, and the increasing sophistication of business intelligence and machine learning workloads. The convergence of scalable cloud infrastructure with advanced column-oriented database architectures has enabled a new generation of analytical systems that promise elasticity, high performance, and operational simplicity. Yet, despite widespread adoption of platforms such as Amazon Redshift, Microsoft Azure Synapse Analytics, and Google BigQuery, there remains significant conceptual ambiguity and methodological inconsistency in how these systems are evaluated, designed, and governed within modern data ecosystems. This research addresses that gap by developing a theoretically grounded and empirically informed framework for understanding cloud-native data warehouses as socio-technical systems that integrate architectural principles, economic models, and organizational practices.

Drawing on a synthesis of foundational database theory, cloud computing literature, and platform-specific technical documentation, this study situates contemporary data warehouses within the historical evolution of column-oriented systems, massively parallel processing, and distributed query execution. The analysis is deeply informed by practical design knowledge articulated in Worlikar, Patel, and Challa’s Amazon Redshift Cookbook, which provides a detailed exposition of how modern data warehousing patterns are implemented in production-scale environments and how architectural decisions interact with workload characteristics, governance constraints, and cost optimization strategies (Worlikar et al., 2025). By embedding such practitioner-oriented insights within a broader theoretical discourse, the research bridges the persistent divide between academic models of database systems and the operational realities of cloud platforms.

Methodologically, the study employs a qualitative comparative architecture analysis that systematically examines Redshift, Synapse, and BigQuery across multiple dimensions including storage abstraction, query execution models, workload isolation, scalability mechanisms, and data governance. Rather than relying on benchmark metrics or synthetic performance tests, the analysis interprets architectural behaviors through the lens of design trade-offs articulated in both scholarly and industrial literature, recognizing that performance, reliability, and cost are co-produced by technology and organizational context. This approach enables a richer understanding of why ostensibly similar systems often yield divergent outcomes when deployed in real enterprises.

The results demonstrate that although all three platforms rely on columnar storage and distributed processing, they embody fundamentally different philosophies of control and abstraction. Redshift emphasizes explicit architectural tuning and cluster-based resource management, Synapse integrates tightly with broader enterprise data ecosystems through hybrid transactional-analytical processing models, and BigQuery advances a serverless, highly abstracted paradigm that redefines the relationship between users and infrastructure. These differences have profound implications for data governance, reproducibility, cost predictability, and the epistemology of analytics itself.

The discussion extends these findings into a theoretical critique of cloud data warehousing, arguing that contemporary platforms are not merely technological artifacts but institutional infrastructures that shape how organizations know, measure, and act upon their data. By articulating limitations, unresolved tensions, and future research trajectories, this article provides a comprehensive foundation for both scholars and practitioners seeking to understand and advance the state of cloud-native analytics.