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

Google BigQuery, a fully-managed, serverless data warehouse, harnesses Google’s infrastructure to deliver high-speed SQL queries at scale, making it an essential tool for large-scale data analytics \cite{BigQueryOverview}. However, its pay-as-you-go pricing model can lead to rapidly escalating costs without diligent management \cite{BigQueryPricing, Kumar2018}. This paper investigates strategies for optimizing query execution and storage management in BigQuery to control expenses while preserving performance. We explore efficient query design principles, such as selective column retrieval and early filtering, alongside advanced data organization techniques like partitioning and clustering to minimize data scanned \cite{Knapp2019, Felts2020, Lee2020}. Further, we examine the role of materialized views and approximate aggregation functions in reducing computational costs, as well as the benefits of automation tools, query caching, and BI Engine for enhancing efficiency \cite{Chandrasekaran2021, GoogleDocsBI, Zhang2022, Liu2021}. Proactive cost management is addressed through real-time monitoring, governance policies, and optimized slot allocation \cite{GCPMonitoring, Garcia2023, Martin2021}. Real-world examples illustrate the impact of these techniques, such as a 40 percent cost reduction achieved by a media company through partitioning and clustering \cite{CaseStudyMedia}. By blending technical optimizations with strategic oversight, this study provides actionable insights for organizations to maximize BigQuery’s capabilities while minimizing operational costs, offering a scalable blueprint for cost-efficient data analytics in enterprise environments.