Building a Custom Agentic Analytics System: Python, LangChain, and SQL Data Lakes

This article provides a comprehensive tutorial for building a custom agentic analytics system using Python, LangChain, and Dremio as the SQL data lake layer. It covers prerequisites, connecting to Dremio via Arrow Flight SQL, building a SQL agent with LangChain's create_sql_agent, configuring prompts for schema safety, and running investigations. The agent can explore schemas, write SQL, correct errors, and return structured results. It also discusses extending the agent with custom tools, evaluating output quality, and limitations. Targeted at developers and data engineers interested in automated SQL analytics and AI-driven data exploration.