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Paper Title
From ETL to ELT to LLMs: Redefining Data Engineering in the Generative AI Era
Authors
Jayanth Veeramachaneni
Keywords
ETL, ELT, Large Language Models, Generative AI
Abstract
Generative AI is radically changing the career of data engineering, particularly through the application of generative AI, and more specifically, Large Language Models (LLMs). The following review outlines the transformation that is to be conducted in the field to convert the traditional extraction, transform and load (ETL) systems into dynamic extract, load, transform (ELT) systems and eventually to the intelligent, LLM-driven data pipelines. The given paper is dedicated to the critical analysis of the pipeline generation process, semantic transformation, and errors that are minimized and generated automatically and with the help of LLMs. A review of recent publications and current trends in the industry—mentioning the overlap of LLMs with new data architecture such as the data lakehouse, the current popularity of semantic ETL, the introduction of the term 'LLMOps', and democratizing access to data using natural language interfaces—is contained in the paper. The generative paradigm will minimize the overhead and the technical complexity to a bare minimum, and it is the paradigm change that could help organizations to come up with smarter data systems that are more dynamic and user-friendly. The review also presupposes the detailed study of the process of transforming the ideals of data engineering at the age of generative AI when the LLMs are utilized.
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IJRTI — journal style
"From ETL to ELT to LLMs: Redefining Data Engineering in the Generative AI Era", IJRTI - International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2456-3315, Vol.10, Issue 12, page no.b135-b139, December-2025, Available :https://ijrti.org/papers/IJRTI2512114.pdf
APA — 7th edition
Veeramachaneni, J. (2025). From ETL to ELT to LLMs: Redefining Data Engineering in the Generative AI Era. International Journal for Research Trends and Innovation, 10(12), b135-b139. https://doi.org/10.56975/ijrti.v10i12.208596
MLA — 9th edition
Veeramachaneni, Jayanth. "From ETL to ELT to LLMs: Redefining Data Engineering in the Generative AI Era." International Journal for Research Trends and Innovation, vol. 10, no. 12, 2025, pp. b135-b139, https://doi.org/10.56975/ijrti.v10i12.208596.
Chicago — 17th, bibliography
Veeramachaneni, Jayanth. "From ETL to ELT to LLMs: Redefining Data Engineering in the Generative AI Era." International Journal for Research Trends and Innovation 10, no. 12 (2025): b135-b139. https://doi.org/10.56975/ijrti.v10i12.208596.
Harvard — author–date
Veeramachaneni, J. (2025) 'From ETL to ELT to LLMs: Redefining Data Engineering in the Generative AI Era', International Journal for Research Trends and Innovation, 10(12), pp. b135-b139. Available at: https://doi.org/10.56975/ijrti.v10i12.208596
IEEE — numbered reference
J. Veeramachaneni, "From ETL to ELT to LLMs: Redefining Data Engineering in the Generative AI Era," IJRTI, vol. 10, no. 12, pp. b135-b139, Dec. 2025, doi: 10.56975/ijrti.v10i12.208596.
Vancouver — biomedical
Veeramachaneni J. From ETL to ELT to LLMs: Redefining Data Engineering in the Generative AI Era. IJRTI. 2025 Dec;10(12):b135-b139. doi: 10.56975/ijrti.v10i12.208596
AMA — 11th edition
Veeramachaneni J. From ETL to ELT to LLMs: Redefining Data Engineering in the Generative AI Era. IJRTI. 2025;10(12):b135-b139. doi:10.56975/ijrti.v10i12.208596
BibTeX — Zotero, Mendeley, LaTeX
@article{IJRTI2512114,
author = {Jayanth Veeramachaneni},
title = {From ETL to ELT to LLMs: Redefining Data Engineering in the Generative AI Era},
journal = {International Journal for Research Trends and Innovation},
volume = {10},
number = {12},
pages = {b135-b139},
year = {2025},
month = {December},
issn = {2456-3315},
doi = {10.56975/ijrti.v10i12.208596},
url = {https://doi.org/10.56975/ijrti.v10i12.208596}
}
RIS — EndNote, RefWorks
TY - JOUR
AU - Veeramachaneni, Jayanth
TI - From ETL to ELT to LLMs: Redefining Data Engineering in the Generative AI Era
T2 - International Journal for Research Trends and Innovation
JA - IJRTI
VL - 10
IS - 12
PY - 2025
SN - 2456-3315
DO - 10.56975/ijrti.v10i12.208596
UR - https://doi.org/10.56975/ijrti.v10i12.208596
SP - b135-b139
ER -
Issue
Volume 10 Issue 12,
December-2025
Pages : b135-b139
Other Publication Details
Paper Reg. ID
IJRTI_208596
Published Paper ID
IJRTI2512114
Downloads
205,639
Research Area
Engineering
Country
-, -, India
Published
December 2025
Published Paper PDF
https://ijrti.org/papers/IJRTI2512114.pdf
Published Paper URL
https://ijrti.org/viewpaperforall.php?paper=IJRTI2512114
CrossRef DOI
https://doi.org/10.56975/ijrti.v10i12.208596
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International Journal for Research Trends and Innovation
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2456-3315
ISSN
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Declarations
Funding
No external funding was received for this study.
Conflict of Interest
The authors declare that they have no conflict of interest.
Acknowledgements
The authors would like to thank the reviewers and the editorial board of International Journal for Research Trends and Innovation for their careful reading and constructive comments, and all colleagues who supported the preparation of this manuscript.
