Less than three weeks before Data Outpost. Get your ticket before they sell out.Join AI and Data builders Nov 4-5 in SF
44:21
Oct 15, 2026
Zoey Weaver interviews Kaitlyn Henry, MotherDuck's Chief of Staff, as she walks through two internal data apps: an AI token spend tracker built on a Flight and a weekly business review Dive. She explains how Guides, markdown files that define the company's key metrics, keep everyone's numbers consistent.
Stream
MotherDuck Features
AI ML and LLMs
43:57
Oct 8, 2026
Mehdi Ouazza uses MotherDuck's prompt_jev() to turn text columns into columns you can GROUP BY. He classifies 100,000 consumer complaints, then walks through five use cases on real data: AI agent logs, Hacker News sentiment, sales calls, and two kinds of job-ad analysis.
Stream
MotherDuck Features
AI ML and LLMs
51:58
Oct 1, 2026
Jacob Matson walks through a working data chat agent he built: MCP tools, an agentic loop, a tuned system prompt, hard-coded read-only guardrails, and telemetry. He demos it live against a real dataset, walks through the actual codebase, then shows the same backend re-platformed into a Slack bot.
Stream
MotherDuck Features
AI ML and LLMs
Data Pipelines
49:02
Sep 29, 2026
MotherDuck's new prompt_jev() function runs text classification directly from SQL using Jev, a decision-focused model from typesafe.ai. In a live demo, Jacob Matson, Dumky de Wilde, and Hamilton Ulmer showed it filtering, disambiguating, and re-ranking real data without leaving SQL. The number they kept coming back to: 100,000 rows classified at 89% accuracy in 40 seconds, compared to 32 minutes and $37.58 for a comparable LLM call.
Stream
MotherDuck Features
AI ML and LLMs
Data Pipelines
37:22
Sep 16, 2026
One CLI to query MotherDuck, publish Python pipelines, and host dashboards from the terminal. See when it beats the MCP server, and when it doesn't.
Stream
MotherDuck Features
AI, ML and LLMs
Data Pipelines
47:43
Sep 10, 2026
See a live cross-engine pipeline: Spark transcribes audio and writes to Iceberg, then MotherDuck attaches that catalog directly as a queryable database.
Stream
Data Pipelines
dbt
Ecosystem
58:27
Sep 3, 2026
Learn how DuckLake time travel, schema evolution, and the change feed work: query any snapshot, undo bad writes, and evolve tables without rewrites.
Stream
SQL
Ecosystem
60:29
Aug 26, 2026
Do AI agents need a semantic layer? Evals comparing plain-text context with a Malloy semantic layer show where each wins, and what it costs.
Stream
AI, ML and LLMs
BI & Visualization
SQL
63:17
Aug 18, 2026
Agentic data engineering in practice: AI agents build dlt ingestion into MotherDuck and a governed Lightdash semantic layer, live in an hour.
Stream
AI, ML and LLMs
Data Pipelines
Ecosystem
52:41
Aug 13, 2026
Guides store business context inside your warehouse so AI agents write correct SQL. See how warehouse-native context works in MotherDuck.
Stream
AI, ML and LLMs
MotherDuck Features
38:23
Mar 17, 2026
See how MotherDuck's new query_rw MCP tool lets AI agents write back to your data warehouse, creating tables, storing embeddings, and saving views.
Stream
AI, ML and LLMs
MotherDuck Features
SQL
Jan 27, 2026
Jacob and Jerel from MotherDuck showcase practical ways to optimize your data warehouse for AI-powered SQL generation. Through rigorous testing with the Bird benchmark, they demonstrate that text-to-SQL accuracy can jump from 30% to 74% by enriching your database with the right metadata.
AI, ML and LLMs
SQL
MotherDuck Features
Stream
Tutorial
Jan 21, 2026
Jacob and Alex from MotherDuck query data using the MotherDuck MCP. Watch as they analyze 180,000 rows of shipment data through conversational AI, uncovering late delivery patterns, profitability insights, and operational trends with no SQL required!
Stream
AI, ML and LLMs
MotherDuck Features
SQL
BI & Visualization
Tutorial
Oct 31, 2025
Big Data is dead. Learn to evaluate data warehouse performance via Time-to-Insight and real costs, ignoring misleading petabyte-scale vendor benchmarks.
Stream
Interview
0:52:01
Apr 20, 2023
Jordan Tigani of MotherDuck and Aditya Parameswaran of Ponder debate whether Big Data is Dead or not, inspired by Jordan’s blog post.
YouTube
Stream