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

Cost Optimization in Google BigQuery: Best Practices for Query Efficiency and Storage Management | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Case Report Cost Optimization in Google BigQuery: Best Practices for Query Efficiency and Storage Management Tushar Gohil This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6493083/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract 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. Google BigQuery Cost Optimization Query Effciency Storage Management Cloud Computing Data Warehousing Big Data Partitioning and Clustering Cloud Cost Management Data Analytics Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6493083","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Case Report","associatedPublications":[],"authors":[{"id":502245511,"identity":"f83eac9c-06ac-43b2-9ccb-5eb5e6dcd968","order_by":0,"name":"Tushar Gohil","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8klEQVRIiWNgGAWjYHACNgglwdh44ANc0AC3eh4kLQ0HZ0BEiNbCwHCYB64FD7CXSH/24OceBrt+6eaGwzZ/7OTsGZgffmAouIPbFokcc8OeZwzJM+ccbDic25ZszMPAZizBYPAMnxY2CZ4DDMkGNxKBWhoOJPYwMJgB/XIYj5b0Z5J/YFos/hyo72Fg/0ZAS4KZNNAWO7AWBrYDCTwMPARsOfPGTFrmgESC5IzEhoO9bcmGPYd5iiUS8Ghhbwc67M0BG3t+ifSHD378sZNnb2/f+OHDH9xaoEAisQHOZgbiBEIagMCeCDWjYBSMglEwUgEAXzlPexh2OF4AAAAASUVORK5CYII=","orcid":"","institution":"Sarvajanik College of Engineering and Technology","correspondingAuthor":true,"prefix":"","firstName":"Tushar","middleName":"","lastName":"Gohil","suffix":""}],"badges":[],"createdAt":"2025-04-21 06:38:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6493083/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6493083/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":90940474,"identity":"17feade2-c770-4fa1-9ab8-92f2aaaa7b86","added_by":"auto","created_at":"2025-09-09 18:01:29","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":383801,"visible":true,"origin":"","legend":"","description":"","filename":"CostOptimizationinGoogleBigQueryBestPracticesforQueryEfficiencyandStorageManagement.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6493083/v1_covered_f5e3124a-4345-4634-bbe2-0559d65461a9.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Cost Optimization in Google BigQuery: Best Practices for Query Efficiency and Storage Management","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Google BigQuery, Cost Optimization, Query Effciency, Storage Management, Cloud Computing, Data Warehousing, Big Data, Partitioning and Clustering, Cloud Cost Management, Data Analytics","lastPublishedDoi":"10.21203/rs.3.rs-6493083/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6493083/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":" 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.","manuscriptTitle":"Cost Optimization in Google BigQuery: Best Practices for Query Efficiency and Storage Management","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-21 15:54:41","doi":"10.21203/rs.3.rs-6493083/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c742d76d-23c7-4016-b6ba-2175e660e1f4","owner":[],"postedDate":"August 21st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-09-09T17:53:20+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-21 15:54:41","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6493083","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6493083","identity":"rs-6493083","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}