I teach Data Engineering and AI Engineering, and I'm the founder of the Learn Data Engineering Academy.

15+ years of experience, 230k+ YouTube subscribers, thousands of students trained.

The Cookbook is my free 80+ page introduction to data engineering, and honestly the thing most people find me through on GitHub. It's a collection of the concepts, tools, and links I wish someone had handed me when I started. Pull requests and issues with new ideas are always welcome.

Not everything is behind a paywall. My Free Labs are short, hands-on projects built around real tools from the modern data stack, each one with videos, code, and architecture diagrams you can follow step by step. Some of the labs currently up:

• 🆕 Databricks Data Quality Monitoring with Zapier: build a dashboard, email the right owner, and let Claude answer your data quality questions
• High-Performance Data Analysis with Exasol: run Python UDFs, ML models, and LLMs directly inside a massively parallel analytics database
• Spark Declarative Pipelines and Lakeflow Designer on Databricks: moving from notebooks to production-ready pipelines
• Building Advanced Pipelines with Kestra on GCP: orchestration with Kestra, Cloud Storage, BigQuery, and Pub/Sub
• DuckDB for Data Engineers: from local to cloud with MotherDuck
• Docker Fundamentals: containers from the ground up for data workloads
• Python for Data Engineers: the Python foundations you need for data engineering work

I run the Learn Data Engineering Academy, 30+ courses covering everything from data engineering fundamentals to full AI engineering projects. Thousands of students have gone through it, and it's not a "watch videos and hope for the best" kind of course. Every course comes with real projects on AWS, Azure, and GCP, using tools like Spark, Kafka, Airflow, dbt, and Databricks.

If you want the full picture of what's inside:

Questions about the Academy, a course, or a partnership? Reach me at [email protected], or find me on any of the links up top.