Modern Data Warehousing in the Cloud: Evaluating Performance and Cost Trade-offs in Hybrid Architectures

Abstract

This article investigates the design and optimization of cloud-based data warehouses with a focus on performance, scalability, and cost-efficiency through the development of a hybrid warehousing model. The article presents a comprehensive comparative analysis of leading cloud data warehouse platforms, including Snowflake, Google BigQuery, and Amazon Redshift, using standardized query workloads and storage benchmarks to evaluate their performance characteristics under diverse analytical scenarios. The proposed hybrid architecture combines high-performance in-memory storage for frequently accessed data with cost-effective object storage for historical data, implementing intelligent data tiering strategies that dynamically allocate resources based on access patterns and query characteristics. Through extensive benchmarking using the BigDataBench framework across multiple workload categories, the article demonstrates that hybrid architectures can achieve significant cost reductions while maintaining acceptable performance levels for the majority of analytical workloads. The article reveals critical trade-offs between query response times, system throughput, and operational costs, providing enterprise architects with empirical evidence for platform selection and architectural design decisions. Article implementations validate that organizations can reduce storage costs by up to two-thirds while maintaining query performance within acceptable thresholds through intelligent data lifecycle management and adaptive migration strategies. This article contributes to the evolving field of cloud data warehousing by establishing a decision framework that maps workload characteristics to optimal platform choices and architectural patterns, enabling organizations to navigate the complex landscape of modern data analytics infrastructure.