Autonomous ETL Workflow Generation Using Semantic Embeddings and  Large Language Model Planning Agents

Annapurneswar Putrevu

Autonomous ETL Workflow Generation Using Semantic Embeddings and Large Language Model Planning Agents

Authors

Annapurneswar Putrevu

Keywords:

Automated ETL Pipeline Generation, Large Language Models, Semantic Embeddings, Retrieval-Augmented Generation, Data Engineering Automation

Abstract

Contemporary organizational infrastructures depend extensively on heterogeneous, large-scale dataecosystems, necessitating intelligent automation of Extract-Transform-Load workflows. Thisinvestigation introduces an innovative AI-assisted framework for autonomous generation of ETL pipelines from natural-language enterprise specifications

References

Jahangir Khan, "Automating ETL Pipelines Using Artificial Intelligence: Transforming Legacy Data Integration Systems into Intelligent Data Workflows," ResearchGate Preprint, June 2025. Available: https://www.researchgate.net/publication/394105841_Automating_ETL_Pipelines_Using_Artificial_Intel

ligence_Transforming_Legacy_Data_Integration_Systems_into_Intelligent_Data_Workflows

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Published

2025-12-30

How to Cite

Annapurneswar Putrevu. (2025). Autonomous ETL Workflow Generation Using Semantic Embeddings and Large Language Model Planning Agents. Journal of Computational Analysis and Applications (JoCAAA), 34(12), 786–794. Retrieved from https://www.eudoxuspress.com/index.php/pub/article/view/4581