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arXiv · 2504.10950

Unveiling Challenges for LLMs in Enterprise Data Engineering

Jan-Micha Bodensohn·Ulf Brackmann·Liane Vogel·Anupam Sanghi·Carsten Binnig
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Abstract

Large Language Models (LLMs) promise to automate data engineering on tabular data, offering enterprises a valuable opportunity to cut the high costs of manual data handling. But the enterprise domain comes with unique challenges that existing LLM-based approaches for data engineering often overlook, such as large table sizes, more complex tasks, and the need for internal knowledge. To bridge these gaps, we identify key enterprise-specific challenges related to data, tasks, and background knowledge and extensively evaluate how they affect data engineering with LLMs. Our analysis reveals that LLMs face substantial limitations in real-world enterprise scenarios, with accuracy declining sharply. Our findings contribute to a systematic understanding of LLMs for enterprise data engineering to support their adoption in industry.

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Jan-Micha Bodensohn, Ulf Brackmann, Liane Vogel, Anupam Sanghi, Carsten Binnig. 2025-04-15. Unveiling Challenges for LLMs in Enterprise Data Engineering. https://doi.org/10.14778/3773749.3773758

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