Data teams often spend significant time writing complex SQL queries for extraction and analysis, while business users struggle to derive insights without technical expertise. Native LLM integration within modern data platforms offers powerful solutions to democratize data access and analysis. However, implementing these capabilities requires understanding serverless architectures and practical integration patterns.

This session demonstrates how to leverage platform-native LLM capabilities alongside open-source tools like Streamlit to enable natural language interactions for data querying and insight generation. Attendees will gain insights into implementation patterns and lessons learned from using integrated tools, helping teams move beyond basic SQL queries to automated insight discovery and analysis.

Key Takeaways

• Using platform-native serverless functions to enable LLM-powered SQL generation and data analysis.
• Implementing prompt engineering techniques for accurate SQL translation and analytical insights.
• Building interactive data applications with Streamlit, seamlessly connecting with platform LLMs.

Target Audience

This session is ideal for data engineers, AI developers, software architects, and business analysts who are seeking to integrate LLM-powered solutions for data querying and insights. It is also beneficial for product managers and enterprise decision-makers aiming to enable natural language-driven analytics across teams.