A Comparative Analysis of Microsoft Fabric and Snowflake: Architectural Paradigms and Performance Metrics in Modern Cloud Data Platforms

Abstract: This article presents a comprehensive comparative analysis of two leading cloud data platforms: Microsoft Fabric and Snowflake. As organizations increasingly rely on cloud-based solutions for data management and analytics, understanding the strengths and limitations of these platforms becomes crucial. This article examines the architectural foundations, functional capabilities, performance metrics, and cost considerations of both platforms. We explore Microsoft Fabric's integrated service model within the Azure ecosystem and Snowflake's multi-cluster shared data architecture, assessing their approaches to data integration, storage, analytics, and machine learning integration. Through empirical performance comparisons and an evaluation of scalability mechanisms, we provide insights into the operational efficiencies of each platform. Additionally, we analyze the security features, compliance standards, and pricing models to offer a holistic view of the total cost of ownership. Our findings reveal distinct advantages in Microsoft Fabric's end-to-end integration and Snowflake's performance in multicloud environments, while also highlighting areas for potential improvement in both platforms. This article aims to serve as a valuable resource for organizations navigating the complex landscape of cloud data solutions, offering evidence-based criteria for platform selection aligned with specific business needs and technological ecosystems