How LLMs Connect to Data Warehouses ? скачать в хорошем качестве

How LLMs Connect to Data Warehouses ?

LLMs are great at writing and reasoning — but terrible at data aggregation. So how do you get AI to actually answer real business questions like "which customers are at risk of churning?" or "what's our annualized revenue?" The answer: connect your LLM to an analytics database. Whether you're building internal analytics tools or customer-facing data products, this walkthrough shows you what's actually possible today. ☁️🦆 Start using DuckDB in the Cloud for FREE with MotherDuck : https://hubs.la/Q02QnFR40 🔗 Resources MotherDuck MCP Server → https://motherduck.com/docs/key-tasks... ➡️ Follow Us LinkedIn:   / motherduck   X/Twitter :   / motherduck   Blog: https://motherduck.com/blog/ 00:00 Why LLMs fail at data analytics 01:39 What LLMs are really bad at (aggregation & facts) 02:08 Business questions that need a data warehouse 03:15 Transactional vs analytical databases (row vs columnar) 03:51 How LLMs interact with data warehouses (tools vs MCP) 05:00 Text-to-SQL: harder than you think (BirdBench benchmark) 06:30 How to improve SQL generation accuracy 07:31 About the East Lake dataset & MotherDuck metrics 09:00 What changed with Claude Opus 4.5 11:00 Adding context: business definitions & semantic models 13:00 Budget semantic modeling: views & column comments 16:00 DuckDB as memory, compute & transformation layer 17:20 MotherDuck architecture: hyper-tenancy explained 19:30 Zero-copy clones & read-scaling ducklings 22:24 Live demo: connecting Claude to MotherDuck via MCP 26:10 Adding AI chat to a SaaS app in ~1 hour 28:51 Real results: sales rep & customer reactions 30:00 Next steps & links #DuckDB #MotherDuck #AI #DataWarehouse #MCP #TextToSQL #LLM #DataEngineering #modelcontextprotocol