---
title: "Dumky de Wilde - MotherDuck Blog"
description: "I spent over 10 years as a consultant setting up data pipelines, data models, and cloud infrastructure for clients ranging from government to fintech to retail and energy before moving to MotherDuck to help people and their AI agents make the most of MotherDuck through documentation, examples and other content. I am the co-author of The Fundamentals of Analytics Engineering and love writing about anything and everything data related."
canonical: "https://motherduck.com/authors/dumky-de-wilde/"
---

# Dumky de Wilde

Dumky de Wilde's photo

Developer & Agent Experience Engineer

I spent over 10 years as a consultant setting up data pipelines, data models, and cloud infrastructure for clients ranging from government to fintech to retail and energy before moving to MotherDuck to help people and their AI agents make the most of MotherDuck through documentation, examples and other content. I am the co-author of The Fundamentals of Analytics Engineering and love writing about anything and everything data related.

## 3 BLOG POSTS AND VIDEOS

[2026/04/30 - Dumky de Wilde](/blog/ai-agent-analytics-with-vercel-motherduck/)

### [AI Agent Analytics with Vercel & MotherDuck](/blog/ai-agent-analytics-with-vercel-motherduck)

Track AI agent traffic that bypasses your web analytics. Stream Vercel Log Drains into MotherDuck to see how ChatGPT, Claude, and other agents browse your site.

[2026/04/21 - Dumky de Wilde](/blog/motherduck-on-cloudflare-workers/)

### [MotherDuck on Cloudflare Workers](/blog/motherduck-on-cloudflare-workers)

Build a real-time voting app on Cloudflare Workers that queries MotherDuck through the Postgres endpoint, using Durable Objects for live state and the pg package for analytical workloads.

[2026/02/02 - Dumky de Wilde](/blog/microbatch-dbt-duckdb/)

### [Microbatch: how to supercharge dbt-duckdb with the right incremental model](/blog/microbatch-dbt-duckdb)

Microbatch support in dbt-duckdb lets you process large tables in time-based batches — making incremental models recoverable, backfillable, and parallelizable without rebuilding from scratch.

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