Optimizing Snowflake Enterprise Data Platform Cost Through Predictive Analytics and Query Performance Optimization
Year: 2024
Type: article
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.
Author: Shreesha Hegde Kukkuhalli
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W4407036024