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From Academia to Industry: Bridging Data Engineering Challenges

Data Engineering Podcast

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Navigating Data Management: Business vs. Academia

This chapter explores the differing approaches to data management in business and research contexts, focusing on structured data engineering for business needs and the challenges faced in academia due to a lack of standardized data sets. It emphasizes the transformative role of large language models (LLMs) in enhancing metadata creation and improving data usability, while addressing the complexities of integrating these models into real-world applications. The discussion also highlights the importance of human involvement in AI-enhanced data pipelines and the ongoing challenges in selecting suitable technology stacks for effective data management.

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