Comparative Insights on Centralized and Individual Models Using Snowflake and Google Big Query

Abstract

Data sharing plays a vital role in today’s digital ecosystem, allowing businesses, governments, and individuals to exchange information on a massive scale. Cloud-native data platforms have significantly transformed traditional data management practices by introducing scale architectures, decoupled storage and compute models, cost-efficiency and resource governance. This paper investigates two distinct paradigms, (Centralized and Individual) data sharing models. Centralized architecture offers consolidated governance, multi-tenant scalability, and advanced analytics enablement, whereas individual data control underlines autonomy, privacy-preserving access, and federated governance. The comparative explores both paradigms through the capabilities of two leading platforms: Snowflake and Google Big Query (GBQ). This analysis highlights operations implications, governance complexity and data collaboration potentials. This paper also covers the pathways for organizations to adopt hybrid data sharing strategies with balance agility, regulatory compliance and efficiency in multi cloud environment.