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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.
Keywords:
ETL, ELT, Large Language Models, Generative AI
Cite Article:
"From ETL to ELT to LLMs: Redefining Data Engineering in the Generative AI Era", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2455-2631, Vol.10, Issue 12, page no.b135-b139, December-2025, Available :http://www.ijrti.org/papers/IJRTI2512114.pdf
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2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator