Background: The rapid shift from on-premises, traditional data warehousing to cloud-native platforms has introduced a complex interplay between architectural paradigms, operational elasticity, and cost models. Cloud data warehouses such as Snowflake and Amazon Redshift represent two dominant approaches—one emphasizing decoupled storage and compute with fully managed elasticity, and the other offering a blend of tightly integrated cluster-based compute with cloud-native extensions. Comparative evaluation of performance and cost across realistic decision-support workloads remains critical for practitioners and researchers seeking to align technical design with business constraints. This study synthesizes theoretical foundations, empirical tuning methods, and benchmark-driven evaluation strategies into an integrated framework for adaptive performance and cost optimization. (Gupta & Sharma, 2021; Malik & Bhatia, 2022; Rao & Karim, 2018)

Objective: The work aims to (1) present a structured, reproducible methodology for comparing Snowflake and Redshift across typical analytical workloads; (2) explicate system-level tuning levers, query optimization practices, and storage/caching strategies; and (3) provide a layered decision framework that maps workload characteristics to optimal configuration patterns and cost-control techniques. (Patil & Zade, 2023; Li & Martin, 2020; Tanaka & Foster, 2021)

Methods: We synthesize insights from benchmark literature (TPC-DS), vendor documentation, and peer-reviewed studies to propose a five-layer analytical framework: Workload Characterization, Storage & Compression, Compute Resource Management, Query & Index Optimization, and Cost Governance. Each layer is described with prescriptive tactics and expected trade-offs. The framework incorporates query profiling, adaptive resource scaling strategies, materialization and caching policies, and schema/encoding choices, grounded in both Snowflake and Redshift operational models. (TPC Council, 2023; Snowflake, 2024; AWS, 2024)

Results: Descriptive analysis highlights that decoupled storage-compute architectures—epitomized by Snowflake—tend to offer superior elasticity and concurrency handling for bursty, multi-tenant analytic workloads, while cluster-based Redshift with Spectrum and concurrency scaling can achieve cost advantages under predictable, sustained throughput when tuned carefully. Storage encodings and micro-partitioning choices materially affect I/O and query latency across both platforms. Query profiling and predicate-pushdown strategies consistently yield order-of-magnitude improvements for complex star-schema TPC-DS style queries when combined with tailored sort and distribution strategies (Redshift) or clustering/caching (Snowflake). (Malik & Bhatia, 2022; Rao & Karim, 2018; Tanaka & Foster, 2021; Narayanan & Chu, 2020)

Conclusions: There is no singular "best" platform; rather, workload signatures and organizational priorities determine optimal choices. The proposed five-layer framework enables systematic selection and tuning, balancing performance and cost across common analytical scenarios. Adoption of this framework supports transparency in architectural trade-offs and produces predictable operational outcomes when applied with rigorous profiling and continuous cost monitoring. Future work should empirically validate the framework across diverse industry datasets and explore automated tuning systems that map telemetry to configuration recommendations. (Singh & Das, 2021; Malviya, 2025)

Cloud data warehousing, Snowflake, Amazon Redshift, query optimization

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.

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. 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.

Malviya, S. (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

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

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