Data Engineering with Generative AI Automating Pipelines and Transformations Using LLMS like GPT

Author(s)	Krishna Prasanth Brahmaji Kanagarla
Country	United States
Abstract	The study describes about Generative AI, with large language models like GPT, will affect the current working structure of data engineering. The ability to automate several issues in ETL, unstructured data management, and intelligent transformations put them up against traditional data pipelining. This research indicates that LLMs are bound to make data processing efficient and also change the face of data engineering in industries. It discusses the advantages and challenges brought about by LLMs in data engineering related to scalability, interpretability, and data privacy issues. Future aspects move in increasing domain-specific adaptability, integrating it with other technologies, and ethical concerns.
Keywords	Generative AI, Extract, Transform, Load (ETL), Large language model (LLM), Data Pipeline, SQL, Unstructured Data, Data Engineering, GPT
Field	Engineering
Published In	Volume 16, Issue 1, January-June 2025
Published On	2025-01-22
Cite This	Data Engineering with Generative AI Automating Pipelines and Transformations Using LLMS like GPT - Krishna Prasanth Brahmaji Kanagarla - IJAIDR Volume 16, Issue 1, January-June 2025.

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