COST OPTIMIZATION STRATEGIES IN CLOUD DATA WAREHOUSING: A COMPARATIVE STUDY OF AWS REDSHIFT AND SNOWFLAKE

International Journal of Core Engineering & Management Volume-8, Issue-02, 2025 ISSN No: 2348-9510 1 COST OPTIMIZATION STRATEGIES IN CLOUD DATA WAREHOUSING: A COMPARATIVE STUDY OF AWS REDSHIFT AND SNOWFLAKE Ujjawal Nayak Experian Information Solutions, Inc. Costa Mesa, CA, USA Abstract Optimizing costs in cloud data warehousing is crucial as enterprises seek scalable, efficient, and economically sustainable solutions. AWS Redshift and Snowflake are among the leading platforms used for cloud data warehousing, each offering unique strengths. This paper explores strategies to achieve cost optimization in AWS Redshift and Snowflake, including data ingestion efficiency, storage optimization, query performance tuning, resource management, and effective monitoring. Index Terms—AWS Redshift, Snowflake, Cost Optimization, Cloud Data Warehousing, Performance Tuning, Resource Management. I. INTRODUCTION Cloud data warehousing solutions such as AWS Redshift and Snowflake are widely adopted due to their scalability, flexibility, and analytical power. However, managing operational costs effectively remains a critical challenge. Efficient cost management in these platforms involves comprehensive strategies that optimize resource usage without compromising performance [1], [2]. II. STRATEGIES FOR AWS REDSHIFT COST OPTIMIZATION A. Data Compression and Distribution Keys Selecting appropriate compression encodings and distribution keys reduces storage costs and improves query performance [3]. B. Efficient Data Ingestion Using COPY commands for bulk loading and AWS Glue for data transformation optimizes ingestion workflows [4]. C. Query Optimization Regularly analyzing query plans and performance through Redshift’s Query Analyzer and International Journal of Core Engineering & Management Volume-8, Issue-02, 2025 ISSN No: 2348-9510 2 recommending automated improvements ensures resources are efficiently utilized [5]. D. Auto Scaling and Cluster Management Utilizing Redshift's Elastic Resize and concurrency scaling capabilities allows dynamic scaling of clusters according to workload demands [6], [7]. E. Data Archiving and Lifecycle Management Leveraging Amazon S3 for data archiving and Redshift Spectrum for querying archived data significantly reduces storage expenses [8]. Fig 1: AWS Redshift Data Load Flow III. STRATEGIES FOR SNOWFLAKE COST OPTIMIZATION A. Warehouse Size and Auto-Suspend Selecting appropriate warehouse sizes and configuring auto-suspend periods can dramatically reduce idle compute costs [9]. B. Efficient Data Storage Management Leveraging Snowflake’s automatic compression and micro-partitioning reduces storage costs [10]. C. Optimized Query Design Creating generic views and using query profiling to optimize queries minimizes resource usage [11]. D. Resource Monitoring and Management Continuous monitoring of resource utilization through Snowflake’s built-in Resource Monitors allows proactive adjustments [12]. E. ELT Pipeline Optimization Utilizing efficient Extract-Load-Transform (ELT) approaches reduces processing overhead [13]. Fig 2: Snowflake Data Load Flow International Journal of Core Engineering & Management Volume-8, Issue-02, 2025 ISSN No: 2348-9510 3 IV. LIMITATIONS/CHALLENGES Challenges include complexity in migrating legacy ETL processes, ensuring compliance with data security standards, managing costs during deployment and scaling, and potential performance degradation [14]. V. FUTURE SCOPE Future work includes AI-driven resource prediction, automated query analysis tools, enhanced data governance frameworks, and real-time analytics [15]. VI. COMPARATIVE ANALYSIS AND RECOMMENDATIONS Table I: Feature/Strategy Comparison Feature/Strategy AWS Redshift Snowflake Data Compression Manual selection needed Automatic compression Scaling Elastic Resize, Concurrency Scaling Instant auto-scaling Query Optimization Manual tuning, Query Analyzer Automatic query profiling Data Ingestion COPY command, AWS Glue Snowpipe, External Stages Storage Management Amazon S3 Archiving Automatic micro-partitioning Resource Monitoring AWS CloudWatch integration Built-in Resource Monitors Cost Management Reserved Instances, Predictable cost Pay-per-use pricing ETL/ELT Pipeline ETL-based traditional approach ELT-based modern approach AWS Redshift suits predictable workloads with extensive AWS integrations, while Snowflake offers flexibility and scalability. Continuous evaluation of workloads and operational needs is recommended [16]. VII. CONCLUSION Cost optimization in AWS Redshift and Snowflake is achievable through strategic resource management, optimized data practices, and monitoring, maximizing ROI. International Journal of Core Engineering & Management Volume-8, Issue-02, 2025 ISSN No: 2348-9510 4 REFERENCES 1. AWS Redshift Documentation, 2024. https://aws.amazon.com/redshift/ 2. Snowflake Documentation, 2024. https://docs.snowflake.com 3. AWS Redshift Compression Guide, 2024. https://docs.aws.amazon.com/redshift/latest/dg/c_Compression_encodings.html 4. AWS Glue Documentation, 2024. https://docs.aws.amazon.com/glue/latest/dg/whatis-glue.html 5. AWS Redshift Query Analyzer Guide, 2024. https://aws.amazon.com/redshift/features/query-analyzer/ 6. AWS Elastic Resize Documentation, 2023. https://docs.aws.amazon.com/redshift/latest/mgmt/elastic-resize.html 7. 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