How LLMs Connect to Data Warehouses ?

Описание: 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.

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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