Welcome to Data Engineering 101.
If you're looking for a real-world guide to building a data engineering career — not just another "theoretical overview" — you're in the right place.
Over my 25+ years in data, I've seen what works... and what silently wrecks projects.
This series walks you through foundational concepts step-by-step — with field-tested strategies, scar stories, checklists, and tools you can actually use.
🔹 Each lesson is short, sharp, and battle-tested.
🔹 Each module builds toward real-world projects, not just passing exams.
🔹 And every concept is tied to business value — not just technical jargon.
Because moving data is easy.
Building trust is hard.
By the end of these lessons, you'll be able to design real-world data pipelines that prioritize trust, not just movement.
Let's get started.
Data Engineering 101 Lesson 4: Medallion Architecture Deep Dive
Imagine your data platform as a supply chain. Raw ingredients (source data) arrive at your “factory,” but unless you have quality checks at every workstation, you risk shipping defective products (dashboards and KPIs) to your customers (executives, analysts, and stakeholders).
Data Engineering 101 Lesson 2: Mastering the Modern Data Stack
The dynamic landscape of data management has witnessed a paradigm shift with the advent of the modern data stack. In this exploration, we will dissect the components that make this stack a transformative force in the realm of data engineering. This narrative begins in 2005…
Data Engineering 101 Lesson 1: ETL vs ELT
Bad ETL and ELT decisions don't just slow analytics.
They quietly detonate budgets, compliance, and trust.
Data engineering isn’t about moving bytes.
It’s about protecting your company’s reputation, wallet, and future.