IDEAS home Printed from https://ideas.repec.org/a/alk/jcsts0/v7y2025i5id9801.html

AI-Powered Data Load Automation from SAP HANA to Cloud Platforms with Instant Error-Handling Techniques

Author

Listed:
  • Venkateswaran Petchiappan

    (NEA Consulting, Inc., USA)

Abstract

This article presents an AI-powered framework for automating data loads from SAP HANA to various cloud platforms while implementing advanced error-handling techniques. The architecture combines SAP HANA's native capabilities with custom AI-driven components to create self-healing data pipelines that minimize manual intervention and maintain data integrity. Key components include intelligent extraction mechanisms using SAP's integration tools, machine learning models for error prediction and analysis, comprehensive error handling through Dead-Letter Queue implementation and auto-retry logic, and performance optimization through dynamic resource allocation and scheduling. The solution addresses common challenges in enterprise data integration by providing real-time detection and resolution of data pipeline issues, dramatically improving reliability while reducing operational overhead. By integrating predictive capabilities with automated remediation, organizations can achieve resilient data flows that adapt to changing conditions and recover automatically from most failure scenarios.

Suggested Citation

  • Venkateswaran Petchiappan, 2025. "AI-Powered Data Load Automation from SAP HANA to Cloud Platforms with Instant Error-Handling Techniques," Journal of Computer Science and Technology Studies, Al-Kindi Center for Research and Development, vol. 7(5), pages 272-282, May.
    • Handle: RePEc:alk:jcsts0:v:7:y:2025:i:5:id:9801
    DOI: 10.32996/jcsts.2025.7.5.34
    as

    Download full text from publisher

    File URL: https://al-kindipublisher.com/index.php/jcsts/article/view/9801
    File Function: Abstract page
    Download Restriction: no
    File URL: https://al-kindipublisher.com/index.php/jcsts/article/download/9801/8468
    File Function: Full text
    Download Restriction: no
    File URL: https://libkey.io/10.32996/jcsts.2025.7.5.34?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:alk:jcsts0:v:7:y:2025:i:5:id:9801. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Al-Kindi Center for Research and Development (email available below). General contact details of provider: https://al-kindipublisher.com/index.php/jcsts .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.