Optimizing Snowflake Enterprise Data Platform Cost Through Predictive Analytics and Query Performance Optimization

Author(s)	Shreesha Hegde Kukkuhalli
Country	United States
Abstract	The rapid adoption of cloud-based data platforms, such as Snowflake, has led to significant benefits in terms of scalability, flexibility, and performance for modern enterprises. However, managing costs in such environments remains a challenge, especially as data volumes and query complexities increase. This paper explores a comprehensive strategy to optimize Snowflake costs through the implementation of predictive analytics and performance optimization techniques. By leveraging machine learning models to forecast resource utilization and employing query optimization techniques, organizations can reduce operating expenses without compromising performance. The results from experiments demonstrate a significant reduction in costs and improved system efficiency.
Keywords	Snowflake, Cloud Cost Optimization, Predictive Analytics, Performance Tuning, Enterprise Data Management, Machine Learning, Query Optimization, Cloud Computing.
Published In	Volume 15, Issue 4, October-December 2024
Published On	2024-12-02
DOI	https://doi.org/10.5281/zenodo.14473872

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