Efficient and Scalable Data Pipelines: The Core of Data Processing in Gig Economy Platforms

Efficient and Scalable Data Pipelines: The Core of Data Processing in Gig Economy Platforms | 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 Research Article Efficient and Scalable Data Pipelines: The Core of Data Processing in Gig Economy Platforms Junjie Chen This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6018424/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 The gig economy is characterized by rapid fluctuations in demand and a diverse array of data generated from various sources. Timely and efficient data processing is critical for platforms operating in this landscape, as they require real-time analytics to inform decision-making and enhance service offerings. In this paper, we introduce a comprehensive framework designed to develop efficient and scalable data pipelines tailored for gig economy platforms. Our framework focuses on systematically managing data processing tasks and offers a modular architecture that integrates multiple data sources seamlessly. It incorporates both stream and batch processing paradigms to optimize data flow and reduce latency. By utilizing microservices architecture, the framework enables independent component deployment, providing greater resilience and adaptability. Testing with extensive benchmarks on real-world datasets demonstrates improvements in processing speeds and resource efficiency in comparison to traditional methods, ultimately empowering gig economy platforms to handle large volumes of data effectively and respond adeptly to changing market dynamics. Computational Neuroscience Gig economy platforms Microservices architecture Full Text Additional Declarations The authors declare no competing interests. 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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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-6018424","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":415012229,"identity":"7c422154-f4a8-4645-98c5-e5c6cca6f3bb","order_by":0,"name":"Junjie Chen","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3klEQVRIiWNgGAWjYBACxgb+Bwc+/rNhYGBnYGBmMACJJRDSwsP4cAZbGkg9kVoYGHiYjTnYDkO1MBChhbn97DFpBp7z8vzMPKabCwrsGPjZcwzwO6wnL026QOK24cxmHrPbMwySGSR73hDQMoPBTHqGwe0Eg8NALTwGQO/cIGQLSAtPwjmYlnoGe8JaeIyNeQ4cgGk5zGAgQdAvaYkPZzYkA/3CVgb0y3EeiTPPCvBqMWw/fODAxwY7eX725m23C/5Uy/G3J2/Ar6UBTYAHr3IQkCeoYhSMglEwCkYBAHmUQiinPEuQAAAAAElFTkSuQmCC","orcid":"","institution":"Columbia University","correspondingAuthor":true,"prefix":"","firstName":"Junjie","middleName":"","lastName":"Chen","suffix":""}],"badges":[],"createdAt":"2025-02-13 00:19:43","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-6018424/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6018424/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":76253188,"identity":"e5758a3f-c97a-4be9-a7a5-1a0a729a04ff","added_by":"auto","created_at":"2025-02-14 04:08:47","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":431848,"visible":true,"origin":"","legend":"","description":"","filename":"EfficientandScalableDataPipelinesTheCoreofDataProcessinginGigEconomyPlatforms2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6018424/v1_covered_2740b65c-3f8b-48e4-9998-297e5d852b17.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eEfficient and Scalable Data Pipelines: The Core of Data Processing in Gig Economy Platforms\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Columbia University","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":"Gig economy platforms, Microservices architecture","lastPublishedDoi":"10.21203/rs.3.rs-6018424/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6018424/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe gig economy is characterized by rapid fluctuations in demand and a diverse array of data generated from various sources. 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