AI-Driven Zero-Downtime Data Validation Framework for Multi-Terabyte Cloud Migrations

AI-Driven Zero-Downtime Data Validation Framework for Multi-Terabyte Cloud Migrations | 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 AI-Driven Zero-Downtime Data Validation Framework for Multi-Terabyte Cloud Migrations Venkat Alamuri This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9455603/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 current data validation systems are mostly reactive, static, and resource-heavy which may lead to interruptions of pipelines and will not be able to detect data corruption in real-time settings. This paper presents SYNAPSE-X, an AI-based, zero-downtime system to perform predictive, adaptive, and self-healing data validation in intelligent data pipelines to overcome these shortcomings. The framework presents some of the main innovations, such as Validation DNA, a single representation of data quality features; the Spatio-Temporal Validation Graph (STVG) of context-aware dependency modeling and trust propagation; an Adaptive Validation Controller (AVC++) based on reinforcement learning, to make dynamic decisions; and the Self-Healing Reconstruction Engine (SHRE) to recover data autonomously. Experimental testing shows that there is a substantial performance improvement, such as better validation accuracy, less computational cost, virtually zero downtime, and recovery in the face of corrupted data situations, than in traditional validation methods. The key contributions of this work are: (i) a new predictive validation architecture, (ii) combined graph-based validation system, (iii) adaptive optimization process based on RL, and (iv) autonomous self-healing data validation system. Artificial Intelligence and Machine Learning Computer Architecture and Engineering Predictive Data Validation Self-Healing Data Pipelines Reinforcement Learning Graph-Based Validation Data Quality Assurance Autonomous Data Systems Spatio-Temporal Graphs Intelligent Data Engineering 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. 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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-9455603","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":625359954,"identity":"93d718d4-e937-4fcc-89ec-580ce437d802","order_by":0,"name":"Venkat Alamuri","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/ElEQVRIiWNgGAWjYHACZjDJByYNbIAEY+MBwloSGBjYIFrSQFoaSNHCcBhM4tVicLz3sMHPHwzybOyn06QrCs7brW0/DLSlxiYap5Yz55ITexIYDNt4crdJnjG4nbztTCJQy7G03AYcWiRn5Bgf4ElgYGxjAGppAGoxOwDUwthwGLeW+W+MD/5JYLBv438L0nIu2ez8Q/xa+CV4jJOBtiS2SYBtOWBndoOALfw8OcbGMmkSyW0SbzdbNhgkJ5jdANqSgMcvbOxnjCXf2NjY9vPnbrzZ8MfO3ux8+sMHH2pscGqBAgk4KxGsMgG/clRgT4riUTAKRsEoGBkAAOSkW9N+oNlrAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0009-0000-7460-047X","institution":"NO","correspondingAuthor":true,"prefix":"","firstName":"Venkat","middleName":"","lastName":"Alamuri","suffix":""}],"badges":[],"createdAt":"2026-04-18 08:58:10","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":true,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":true},"doi":"10.21203/rs.3.rs-9455603/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9455603/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107412298,"identity":"1f7b121c-ec5a-4f3d-a0df-dd0acd4eac5c","added_by":"auto","created_at":"2026-04-21 09:13:55","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":889153,"visible":true,"origin":"","legend":"","description":"","filename":"AIDrivenZeroDowntimeDataValidationFrameworkforMultiTerabyteCloudMigrations.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9455603/v1_covered_3bf5c8fd-22e3-441f-9a99-378c0d010c62.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eAI-Driven Zero-Downtime Data Validation Framework for Multi-Terabyte Cloud Migrations\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"others","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":"Predictive Data Validation, Self-Healing Data Pipelines, Reinforcement Learning, Graph-Based Validation, Data Quality Assurance, Autonomous Data Systems, Spatio-Temporal Graphs, Intelligent Data Engineering","lastPublishedDoi":"10.21203/rs.3.rs-9455603/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9455603/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe current data validation systems are mostly reactive, static, and resource-heavy which may lead to interruptions of pipelines and will not be able to detect data corruption in real-time settings. 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