Cost Optimization in Google Big Query: Best Practices for Query Efficiency and Storage Management

Abstract

Google Big Query’s serverless architecture delivers high-speed analytics at scale, yet its pay-as-you-go pricing requires diligent management to prevent escalating costs. This paper investigates strategies to optimize query execution and storage, focusing on efficient design principles like selective column retrieval and early filtering. By implementing advanced data organization techniques such as partitioning and clustering, organizations can significantly minimize data processed.

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