The Only Guide You’ll Need for Testing Your Data Pipelines With Confidence
By the end, you’ll walk away with a working pipeline test suite, custom GX checkpoints, and a README you’d be proud to ship.
You are reading Level-Up Data Engineering — a special monthly minicourse series designed to turn mid-level data engineers into senior. Subscribe for a paid plan to boost your career growth by turning skills into habits.
Greetings, Data Engineer,
Pipelines rarely scream when they fail. Most days, they just whisper quietly into the wrong table.
It happens more often than anyone admits. Because in data work, testing is often skipped or bolted on after the fact.
If you can catch bad data before it spreads, you become the kind of engineer teams rely on — the one who protects the numbers that power real decisions.
Testing isn’t extra work. It’s what separates amateurs from engineers who ship with confidence.
In this month’s mini-course, I’ll show you how to validate your pipelines step-by-step.
You and I will build a comprehensive testing strategy for the entire ELT process:
We’ll start testing our Python integrations.
Then we’ll test our dbt models.
And finally, we’ll automate everything for maximum confidence.
By the end, you’ll know how to test, trust, and scale your data with real-world discipline.
How to Work With This Mini-Course
Just reading the article alone would take you over 20 minutes. So:
Bookmark this guide and set a reminder to revisit it weekly.
Skim the entire article once to understand the big picture.
Each week, complete the exercises before applying them to your own projects.
Share your progress on LinkedIn to reinforce learning and expand your network.
Take your time. Don’t rush to implement everything at once. Master each step before moving to the next.
Also, you will need about an hour to read the whole thing and write the code at once. It’s much easier to spend 15 minutes per week!