🛠️ SQL for Data Engineering - Full Course

Data Nerds! This repo contains all the projects needed to follow along with my free course: SQL for Data Engineering Support the course and get practice problems, course certificate, and more!

Team Members 👥

🙋🏼‍♂️ Course Leader: Luke Barousse
🎬 Course Producer: Kelly Adams
💻 Content Developer: Rikki Singh
📺 Video Editor: Brannon Linder

Table of Contents

Projects

• Project #1: Exploratory Data Analysis — Job market analytics with SQL
• Project #2: Data Warehouse & Mart Build — Production ETL pipeline from raw CSVs to star schema and data marts
• Project #3: Flat to Warehouse Build — Transform flat job posting data into a normalized star schema (bonus project, not covered in video)

How to Run SQL

Option 1: Cloud (Easiest to get started) — MotherDuck

1. Sign up for MotherDuck (free): lukeb.co/motherduck
2. Use MotherDuck Studio in your browser — no installation required
3. Attach the course database by running this command:

ATTACH 'md:_share/data_jobs/87603155-cdc7-4c80-85ad-3a6b0d760d93'

4. For running project files: Not recommended — the cloud UI works like a Jupyter notebook (run queries cell-by-cell; no direct file execution). Best for ad hoc analysis. To run full project files, use Option 2 below.

Option 2: Run Locally with DuckDB

1. Install DuckDB from the official MotherDuck install page — follow the instructions for your OS to install the correct version that supports MotherDuck.

2. Launch DuckDB in your terminal:

   duckdb

3. Attach and connect to the course database — either specify the database when launching DuckDB (duckdb "md:data_jobs"), or in your session run ATTACH then USE to switch to the attached database:

   ATTACH 'md:_share/data_jobs/87603155-cdc7-4c80-85ad-3a6b0d760d93';
   USE data_jobs;

4. For running project files: Follow the instructions outlined in each project section.

Found a Typo? Want to Contribute?

• If you find an error in this repo, please feel free to make a pull request by:
  • Forking the repo
  • Making any changes
  • Submitting a pull request

Special Thanks 🙌

A special thanks to SerpApi, whose generous credits made it possible to gather the job postings data used in this course.

If you're interested in using SerpApi for your own data projects, sign up here and mention Luke sent you to get an additional 20% off paid plans!