From Zero AI Knowledge to Building
Enterprise Text-to-SQL Solutions
I began noticing a pattern that appeared again and again: business users struggling to access insights trapped behind SQL queries they didn't know how to write. The same challenge surfaced across different data platforms and enterprise environments, and it became clear that solving this problem required a deeper understanding of how Text-to-SQL systems actually work in production.
Through my experience helping customers build Text-to-SQL solutions, one truth became crystal clear:
There is no one-size-fits-all approach.
Each enterprise has unique schemas, business logic, and user requirements that demand flexible, accurate solutions tailored to their specific use cases. The challenge isn't just building a Text-to-SQL system—it's building one that adapts to complex, real-world enterprise data architectures.
With 14+ years of experience in data engineering and business intelligence, I immersed myself in learning everything about LLMs from the ground up. Nights and weekends disappeared into understanding transformer architectures, prompt engineering, and the nuances of how language models interpret natural language and translate it into precise SQL. But reading papers and tutorials wasn't enough—I needed to test these systems against the messy, interconnected schemas that define enterprise data warehouses.
Studying transformer architectures, prompt engineering, and language model fundamentals from scratch
Creating 20+ prototypes to test against real enterprise schemas and complex table relationships
Identifying patterns that separate theoretical Text-to-SQL from practical, production-ready solutions
Deploying enterprise-grade Text-to-SQL systems handling complex queries and real-world data challenges
This experience taught me something valuable: the knowledge gap between basic Text-to-SQL demos and enterprise-grade systems is enormous. That's why I created the Text-to-SQL Handbook—but with a different philosophy than traditional technical documentation.
The goal isn't to provide abstract explanations of modern solutions.
Instead, every concept links directly to GitHub repositories where you can clone, test, and tweak the code based on your specific requirements.
Retention from lectures
Retention from practice
Learn by Building
If you're building Text-to-SQL systems, wrestling with complex schema challenges, or exploring how LLMs can democratize data access in your organization, I'd love to hear from you.
Share emerging challenges in Text-to-SQL
Work together on innovative solutions
Advance natural language querying