Objective: This article synthesizes contemporary theory and applied practice in cloud data warehousing (CDW) performance and cost optimization, producing a unified, publication-ready review that integrates architectural insights, query and storage tuning, elasticity management, platform-specific techniques, and evaluation frameworks. The aim is to produce a rigorous, conceptually rich exposition that clarifies trade-offs, surfaces research gaps, and presents an extensible framework for future empirical work.

Background: The last decade has seen rapid migration of analytical workloads to cloud-native data warehouses such as Snowflake, Amazon Redshift, and Google BigQuery, producing novel demands on architecture, cost governance, and performance tuning (Adhikari & Kambhampati, 2023; Malik & Bhatia, 2022; BigQuery Documentation, 2023). While vendor documentation and practical reports provide platform-specific guidance (Amazon Web Services, 2024; BigQuery Documentation, 2023), the academic literature offers comparative analysis and theoretical underpinnings for query optimization, caching, and elasticity (Li & Martin, 2020; Narayanan & Chu, 2020). Despite this, there remains a fragmentation in how performance and cost are jointly optimized, how benchmarks reflect real workloads, and how practitioners operationalize theoretical recommendations (Rao & Karim, 2018; Chen & Li, 2022).

Methods: This research employs a rigorous theoretical synthesis approach, integrating findings across peer-reviewed studies, vendor guides, benchmarking standards, and applied case studies. The methodology explicates architectural primitives, cost models, query behavior, and optimization levers while mapping them to evaluation metrics and benchmark methodologies (TPC Council, 2023). It also develops conceptual frameworks for workload-aware elasticity, storage-compute decoupling trade-offs, and cost-tier strategies adapted to industry workloads such as insurance and finance (Malviya, 2025).

Results: The synthesis reveals a coherent set of principles: (1) decoupled storage-compute architectures enable fine-grained cost control but shift complexity to workload placement and data organization (Adhikari & Kambhampati, 2023; Tanaka & Foster, 2021); (2) query optimization benefits from integrated profiling, adaptive materialization, and cost-aware planner hints, with significant platform-specific variations (Lee & Martin, 2020; Li & Martin, 2020); (3) cost governance requires multi-dimensional policies—reservation commitments, auto-suspend scaling, and query-level throttling—to be combined with workload classification (Chen & Li, 2022; Shreekant Malviya, 2025); (4) benchmark standardization is necessary for fair comparative evaluation but must accommodate synthetic and real-world mixes (TPC Council, 2023; Rao & Karim, 2018).

Conclusions: This article advances a unified conceptual architecture and an actionable taxonomy of optimization strategies that balance performance and cost in CDW. It identifies priority areas for empirical validation—fine-grained workload-aware autoscaling algorithms, cross-platform cost modeling, and benchmark augmentation—and outlines methodological recommendations for future research and industry practice.

Cloud data warehousing, performance optimization, cost management, query tuning

Adhikari, R., & Kambhampati, C. (2023). Cloud Data Warehousing: Architecture, Techniques, and Challenges. Journal of Cloud Computing: Advances, Systems and Applications, 12(1), 45-68. https://doi.org/10.1186/s13677-023-00487-w

Amazon Web Services. (2024). Amazon Redshift: Performance Optimization Guide. Retrieved from https://aws.amazon.com/redshift/

BigQuery Documentation. (2023). Optimizing Performance in BigQuery. Retrieved from https://cloud.google.com/bigquery/docs/optimization

Chen, Y., & Li, J. (2022). Cost Management Strategies for Cloud Data Warehousing. International Journal of Cloud Computing and Services Science, 11(4), 221-234. https://doi.org/10.11591/ijcsi.2022.11.4.22

Gagne, B., & Thomas, M. (2023). Scalability in Cloud Data Warehousing: Best Practices and Techniques. Data Management Review, 9(3), 132-145. https://doi.org/10.1098/dmr.2023.09.03

Gupta, H., & Sharma, M. (2021). A Comparative Study of Traditional and Cloud-Native Data Warehousing Platforms. International Journal of Computer Applications, 183(35), 1–6.

Malik, A., & Bhatia, R. (2022). Cloud Data Warehousing: Performance Optimization in Snowflake. Journal of Cloud Computing, 11(1), 55–73.

Shreekant Malviya. (2025). A Five-Layer Framework for Cost Optimization in Snowflake: Applied to P&C Insurance Workloads. The American Journal of Interdisciplinary Innovations and Research, 7(07), 28–43. https://doi.org/10.37547/tajiir/Volume07Issue07-04

Patil, A., & Zade, A. (2023). Performance Tuning in Big Data Environments: RDBMS vs. Snowflake. Data Engineering Journal, 9(4), 204–219.

Li, X., & Martin, P. (2020). Query Optimization in Hybrid Cloud Databases. ACM Transactions on Database Systems, 45(2), 17–36.

TPC Council. (2023). TPC Benchmark™ DS Overview. Retrieved from https://www.tpc.org/tpcds/

Rao, D., & Karim, A. (2018). Cost and Performance Analysis of Cloud Data Warehouses. International Journal of Cloud Applications, 6(3), 155–172.

James, T., & Ortega, M. (2019). Indexing Techniques in Relational Databases: A Review. Database Technology Review, 13(2), 45–63.

Narayanan, V., & Chu, H. (2020). Optimizing SQL Performance in Cloud-based Platforms. Journal of Data Engineering, 22(1), 89–104.

Lee, J. H., & Martin, P. (2020). The Role of Query Profiling in Performance Tuning. ACM Transactions on Database Systems, 45(4), 29–52.

Singh, R., & Das, S. (2021). Elastic Compute in Modern Data Warehousing. Cloud Systems Review, 17(1), 134–150.

Tanaka, M., & Foster, B. (2021). Caching and Storage Optimization in Snowflake. Journal of Cloud Optimization, 10(3), 200–218.