A Five-Layer Framework for Cost Optimization in Snowflake: Applied to P&C Insurance Workloads

Аннотация

The use of Snowflake as a cloud-native data OPEN ACCESS warehouse has dramatically changed the management SUBMITED 22 May 2025 of analytics workload for Property and Casualty (P&C) ACCEPTED 19 June 2025 PUBLISHED 07 July 2025 insurers, while simultaneously presenting serious cost VOLUME Vol.07 Issue07 2025 governance challenges. The heavy volume of searches, CITATION big data retention, and decentralized business Shreekant Malviya. (2025). A Five-Layer Framework for Cost Optimization in intelligence operations are industry-standard Snowflake: Applied to P&C Insurance Workloads. The American Journal of Interdisciplinary Innovations and Research, 7(07), 28–43. procedures that tend to lead to uncontrolled credit https://doi.org/10.37547/tajiir/Volume07Issue07-04 usage and overspending on storage. This research introduces a modular five-layer optimization framework COPYRIGHT © 2025 Original content from this work may be used under the terms focused on property and casualty insurance data, of the creative commons attributes 4.0 License. combining workload segmentation, and compute sizing with Snowflake's account usage metadata. The framework is tested and validated using Kaggle’s Insurance Agency Data, representing real-world P&C operations across 17 states. Benchmark queries simulating core insurance workloads were designed using modified TPC-H logic, a standard decision support benchmark that enables realistic performance evaluation under analytical query conditions, achieving up to 82% cost reduction and a 64% reduction in execution time without compromising the results. These results highlight the efficiency of the framework to facilitate proactive and elastic cost control. Future studies can investigate AI-driven query forecasting, scalable warehouse dynamics, and real-time anomaly detection to further advance cloud-native data ecosystem governance. Keywords: Snowflake Cost Optimization, Property & Casualty Insurance Data Workloads, Metadata-Driven Cost Control, Query Performance Tuning The American Journal of Interdisciplinary Innovations and Research 28 https://www.theamericanjournals.com/index.php/tajiir The American Journal of Interdisciplinary Innovations and Research INTRODUCTION how Snowflake’s cost control features can be systematically adapted to insurance-specific workloads. The transition to cloud data warehousing has This study addresses that gap by exploring how a revolutionized big data analysis and management in the workload-aware strategy can improve cost efficiency property and casualty insurance industry. Innovative and performance in Snowflake for P&C insurance use cloud-native platforms like Snowflake provide elastic cases. scaling and pay-as-you-go pricing, thereby enhancing the ease of analytical responsiveness [1]. This transition 2. Literature Review introduces a new operational risk of cost 2.1 Overview of Snowflake Architecture and Pricing unpredictability to Property & Casualty (P&C) Insurance. Ad hoc queries, complete data scans, and the sheer Snowflake is a new-generation data warehousing cloud amount of historical claims data can result in increased platform that has become popular due to its cloud- compute and storage consumption, especially when big native architecture as well as simplicity. In contrast to data cost governance models are not aligned with legacy systems, Snowflake is designed for the cloud and workload behavior [2]. keep storage and computation separate, resulting in higher flexibility and scalability Figure 1. It enables Despite Snowflake's wide array of features such as auto- companies to manage resources autonomously, thus suspend, materialized views, result caching, and query making it simpler to manage performance as well as telemetry, these features are often applied ineffectively costs [7]. in insurance scenarios. Past research and industry practices reveal some structural and technical sources of For example, Snowflake architecture accommodates the insurance data platform cost inefficiency, some of these ability to auto-suspend and resume so that compute include over-provisioned warehouses, underutilized resources can remain suspended when inactive and storage features, redundant business intelligence resumed as necessary. This prevents unnecessary queries, and poor resource allocation [3] [4]. expenses and enhances effectiveness compared to legacy data systems. It is also designed with a multi- Further, FinOps adoption remains limited among most cluster architecture, which allows customers not to insurers [5], [6]. Traditional practices like monthly usage encounter bottlenecks during peak periods, hence reviews or manual query monitoring are too slow and enhancing performance in teams [8], [9]. rigid to keep up with the fast-changing demands of insurance data. For example, during events like open An important aspect is caching at varying levels—query enrollment or catastrophe modeling which requires real results, metadata, and in-memory. Such caches h