You can read 100 articles about DAGs.
You can quote what “Directed Acyclic Graph” means.
But none of it matters until you write one.
Today’s goal?
You’ll build your first working DAG — start to finish — that does something useful and gets you closer to job-ready.
If you can complete this, you’ll be able to walk into any interview and say:
“Yes, I’ve built production-style pipelines in Airflow.”
Let’s do this.
Why This Matters
Anyone can Google "What is Airflow?"
You’re here to build with it — and speak about it like an engineer who’s done it in prod.
Today’s drop isn’t theory.
You’ll create your first working DAG — a mini pipeline that does something useful, reliable, and interview-worthy.
You’ll leave this newsletter with:
A runnable DAG
A deployable GitHub project
A STAR-based story you can use in interviews
And clarity about what makes DAGs production-grade
What You’re Building
The business use case is real:
A team receives a messy CSV of order data every few hours. Right now it’s being cleaned manually. You’ve been tasked with automating this.
Here’s what your DAG will do:
Extract: Read CSV
Transform: Drop nulls, clean types
Load: Write to Snowflake
Retry: If anything breaks
Alert: Slack notifications
Schedule: Every 6 hours
Your Airflow Starter Pack
Step 1: Install Airflow (If not done yet)
pip install apache-airflow
airflow db init
airflow webserver --port 8080
airflow schedulerOpen Airflow UI at localhost:8080 and create your user.