Cloud computing and big data technologies have evolved rapidly, with significant effects on enterprise datamanagement and analytical data processing. The contemporary cloud data warehouses offer scalable, highperformance solutions that enable organizations to handle vast amounts of structured and semi-structured data andsupport advanced analytics and business intelligence applications. The most popular services in this area will beSnowflake, Google BigQuery, and Amazon Redshift, each with its own architecture, scaling profile, and optimizationstrategies. Infrastructure and data strategy decisions have become increasingly important as more analytical workloadsmove to the cloud, and to make informed choices, it is imperative to assess the performance capabilities of theseplatforms.In this paper, we provide a comparative performance benchmark analysis of three popular cloud data warehousesystems: Snowflake, Google BigQuery, and Amazon Redshift. The study analyses the most important performancemetrics, including time to query execution, scalability, resource consumption, and cost-performance, under normalanalytical workloads. The study assesses the effects of architectural characteristics of serverless computing, distributedquery processing, and storage-compute separation on system performance, using standardized benchmarkingscenarios to simulate enterprise analytics.The results emphasize the advantages and disadvantages of each platform under different workload conditions andshow the impact of architectural differences on analytical performance and scalability. The findings provideinformation to help the organization choose the right cloud data warehouse solutions to support its data-intensiveworkloads. Finally, the research has contributed to the growing literature on cloud-based data analytics infrastructureand has provided useful guidance to enterprise data architects, analytics engineers, and cloud infrastructure decisionmakers.