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Data Warehouses & LLMs
Your data warehouse holds the answers — LLMs can help you ask better questions. This practical guide shows data professionals how to build text-to-SQL pipelines, enrich warehouse data with AI, and bring semantic search to their existing tables.
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About the Book
Data Warehouses & LLMs is a hands-on guide for data professionals who want to bring large language models into their everyday analytics work. Starting from the fundamentals of data warehousing, the book walks step by step through building text-to-SQL pipelines, validating and self-healing generated queries, enriching warehouse data with LLM-powered classification, and building semantic search and RAG systems on top of your existing tables. Along the way, you'll write real Python and SQL code, learn prompting patterns that hold up in production, and see how AI-augmented analysts can move faster without sacrificing accuracy. Whether you're a data engineer, analyst, or someone straddling both roles, this book gives you a practical path from traditional warehousing to AI-augmented data systems.
Author
About the Author
krushal
Krushal Gohil is an Assistant Professor at Drs. Kiran & Pallavi Patel Global University (KPGU) in Vadodara, Gujarat, India, where he teaches and researches at the intersection of data systems and applied AI. His work focuses on helping data professionals bridge traditional data warehousing with modern large language model techniques, including text-to-SQL, data enrichment, and semantic search. Data Warehouses & LLMs is his first book, drawing on his classroom and research experience to make these techniques practical for working data engineers and analysts.
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