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Data Engineering with DuckDB & MotherDuck
Rating: 4.6 out of 5(40 ratings)
573 students

Data Engineering with DuckDB & MotherDuck

Learn how to build data workflows that run on your laptop, in the cloud, or across both using DuckDB and MotherDuck.
Created byAndreas Kretz
Last updated 10/2025
English

What you'll learn

  • Set up DuckDB locally and use it for fast analytical queries
  • Connect to MotherDuck and query cloud data with the same engine
  • Compare local vs. cloud execution using EXPLAIN and EXPLAIN ANALYZE
  • Build a Python-based ELT workflow with DuckDB
  • Export data to CSV and Parquet, and re-query directly from files
  • Combine local and cloud data in a single “dual execution” query
  • Visualize real-world data in Python (NYC elevator complaints project)
  • Use DuckLake to manage Parquet data with schema evolution and transactions

Course content

7 sections • 19 lectures • 1h 23m total length

  • Welcome & Course Overview1:28

    Get an overview of what you’ll learn in this course and how DuckDB and MotherDuck fit into modern data engineering workflows.

  • What Are DuckDB and MotherDuck?2:23

    Understand what makes DuckDB unique as a lightweight analytical database and how MotherDuck extends it to the cloud for collaboration and scale.

  • Setup & Installation5:28

    Step-by-step instructions for installing DuckDB locally, setting up the DuckDB UI, and connecting your environment to MotherDuck.

Requirements

  • Basic SQL and Python knowledge
  • No prior DuckDB or cloud setup required

Description

In this hands-on course, you’ll start by exploring DuckDB locally: querying CSV and Parquet files, building persistent databases, and analyzing data right from your terminal or the built-in DuckDB UI. You’ll then connect to MotherDuck, the cloud platform built around DuckDB, and learn how to scale analytics, share data, and collaborate without switching tools.

You’ll build hands-on ELT workflows using the DuckDB CLI, Python, and MotherDuck. From analyzing local CSV files to running cloud-scale data pipelines. You’ll see how hybrid execution works, compare local versus cloud compute, and learn to move effortlessly between environments while maintaining a single, simple toolset.

Along the way, you’ll work on a real-world project analyzing NYC 311 elevator service requests, combining local and cloud datasets to generate insights, visualize hotspots, and identify business opportunities.

Finally, you’ll explore DuckLake, DuckDB’s new integrated lakehouse format, which adds schema evolution, snapshots, and transactions to your Parquet data. You’ll understand where Duck Lake fits into the modern data stack and how it connects to cloud storage like S3.

By the end of this course, you’ll have a complete setup, reusable SQL and Python scripts, and the confidence to use DuckDB and MotherDuck together for modern, scalable data engineering workflows.

Who this course is for:

  • Data Engineers, Data Scientists, and Analysts exploring modern, lightweight data stacks
  • Anyone who wants to understand how DuckDB and MotherDuck fit into real data platforms

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