Leveraging Generative AI for Data Engineering Workflows

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.

Authors

Publication Details

Journal
Journal of Computer Science and Technology Studies
Published
2025-05-01
DOI
https://doi.org/10.32996/jcsts.2025.7.3.14
Citations
3
Primary Topic
Scientific Computing and Data Management
Type
article
Field-Weighted Citation Impact
12.51
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article

Leveraging Generative AI for Data Engineering Workflows

Quang Hai Khuat
3 citations
Journal of Computer Science and Technology Studies
Scientific Computing and Data Management
12.51
article

Leveraging Generative AI for Data Engineering Workflows

Quang Hai Khuat
article en
3 citations

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.

Journal of Computer Science and Technology StudiesVol. 7(3)
Openalex Percentile: Top 2%
Scientific Computing and Data Management
12.51
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