IDEAS home Printed from https://ideas.repec.org/a/alk/jcsts0/v7y2025i3id9310.html

Leveraging Generative AI for Data Engineering Workflows

Author

Listed:
  • Quang Hai Khuat

    (University of Rennes 1, France)

Abstract

Generative AI represents a powerful new layer of automation for data engineering. When leveraged responsibly, it can improve efficiency, reduce errors, and even enable non-experts to contribute to data workflows, all while allowing expert data engineers to tackle more ambitious challenges. We are witnessing the early stages of this transformation. By staying informed of the latest tools, adopting best practices for AI usage, and continuously refining the human-AI partnership, data engineering teams can ride this wave to build more intelligent, adaptive, and robust data pipelines than ever before. The future data platform may very well be a co-creation of human engineers and AI, each complementing the other’s strengths – and the organizations that embrace this symbiosis will be positioned at the forefront of the data-driven era.

Suggested Citation

  • Quang Hai Khuat, 2025. "Leveraging Generative AI for Data Engineering Workflows," Journal of Computer Science and Technology Studies, Al-Kindi Center for Research and Development, vol. 7(3), pages 120-140, April.
    • Handle: RePEc:alk:jcsts0:v:7:y:2025:i:3:id:9310
    DOI: 10.32996/jcsts.2025.7.3.14
    as

    Download full text from publisher

    File URL: https://al-kindipublisher.com/index.php/jcsts/article/view/9310
    File Function: Abstract page
    Download Restriction: no
    File URL: https://al-kindipublisher.com/index.php/jcsts/article/download/9310/7970
    File Function: Full text
    Download Restriction: no
    File URL: https://libkey.io/10.32996/jcsts.2025.7.3.14?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:3:id:9310. 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.