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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.

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