Synergistic Cloud Architectures for Hyper-Scale Data Dissection: A Triadic Platform Evaluation

Abstract

This paper presents a comparative review and synthesis of the hyperscale data analytics cloud architectures of Amazon Web Services (AWS), Microsoft Azure and Google Cloud Platform (GCP). The review characterizes storage, processing, machine learning, data lake, governance and business intelligence services depending on what is revealed in published platform documentation, architectural descriptions, and reported use cases, rather than primary empirical experiments. The structured qualitative assessment is organized around the following issues: Scalability, Integration, Pricing Model, Governance, and Operational Complexity. The paper summarises documented use cases from various industries and elaborates on the challenges for implementation, namely cost, governance, security, performance and skills. In the end, it describes growth areas like real-time processing, AI/ML integration, multi-cloud and hybrid deployment, self-service BI, security automation, edge computing, serverless frameworks, and data mesh and data fabric models. The contribution is a synthesis that compares and offers a problem-oriented overview that is intended for practitioners and researchers, not a benchmark study or experimental evaluation that is original in nature.