Send us a text

What is the modern data stack—and how do tools like Spark, dbt, and Airflow actually work together? In this lesson, I’ll break it down step by step with real-world insights.

In Lesson 2 of the Data Engineering 101 series, we take a deep dive into the Modern Data Stack—what it is, how the components work together, and why data modeling is still the backbone of every great pipeline.

💡 You’ll learn how tools like Spark, dbt, Pandas, and Airflow function like machines in a factory—transforming raw logs and cloud-stored data into meaningful business insight.

We’ll also talk about:

Databases vs. data warehouses

Cloud storage as digital basements

Why orchestration matters

When Excel is actually OK

And how to start mapping your own data stack

📦 This episode includes visuals, analogies, and action steps you can apply right now—whether you're building from scratch or modernizing legacy systems.

📚 Watch the full Data Engineering 101 Playlist:
👉 https://www.youtube.com/playlist?list=PLewT1HTMY0WZ4tpoBw-w_CcewwrEDNmsU

🚨 Don’t miss Lesson 3: SQL Fundamentals (coming next week!)

—
Looking to get more technical practice? Join Codecademy while supporting the channel! 
If you are a high school or college student you get 35% off with this link:
https://www.pntrs.com/t/2-591852-361892-213588
Not a student, but want a special discount, get 15% off the normal prices with this special link from Gambill Data: https://www.pntrac.com/t/2-523372-361892-213588

🔔 Subscribe for more real-world data engineering strategies, tools, and career advice.
➕ Follow me on LinkedIn: https://www.linkedin.com/in/databasemanagement/
💻 Check out consulting & mentoring resources:  https://www.gambilldataengineering.com
—
Chapters: 
00:00 Intro – Why the Cloud Changed Everything  
00:39 What We’ll Cover in This Lesson  
00:58 What Is the Modern Data Stack?  
01:34 Databases vs. Data Warehouses  
02:18 Cloud Storage = Digital Basements  
03:02 Data Processing Tools (Spark, dbt, Pandas)  
03:24 Airflow: The Data Factory Supervisor  
05:10 Cloud Platforms: Azure, AWS, GCP   
06:03 Modeling Is Like Organizing a Library  
07:06 Relational, Dimensional, and NoSQL Models  
09:14 When Excel Is OK!  
10:10 Key Takeaways  
11:20 Coming Up Next: SQL Fundamentals  

#DataEngineering #ModernDataStack #ApacheSpark #dbt #Airflow #DataModeling #ETL #CloudData #SQL #Azure #AWS #GCP

Support the show

Chris Gambill is a data engineering consultant and educator with 25+ years of experience helping organizations modernize their data stacks. As founder of Gambill Data, he specializes in data strategy, cloud migration, and building resilient analytics platforms for mid-market and enterprise clients. He’s passionate about making real-world data engineering accessible.

Connect with Chris on LinkedIn or learn more at gambilldata.com.