Introduction to Apache Airflow in Python

Course Overview

This DataCamp course is for people whose data jobs currently run on hand-written scripts and cron entries. It shows how to move that work into Apache Airflow, which adds scheduling, error handling, and reporting. The central idea is the Dag, a directed acyclic graph that maps out tasks and the order they must run in.

The material has been updated for Airflow 3.1.6, so the syntax matches the current major version. That matters because many Airflow tutorials online still show older patterns. By the end you will have written Dags, scheduled them, watched them run in the web interface, and put together a small sales ETL pipeline with branching and a manual approval step.

What You Will Learn

• Reading and writing Dags, and running them from the Airflow shell
• Navigating the Airflow web interface and diagnosing Dags that fail to load
• Turning Python functions into tasks with decorators, including @task.bash
• Setting task order and defining schedules in Python
• Passing small pieces of data between tasks with XCom
• Waiting on outside conditions with sensors, such as a FileSensor
• Sending failure notifications through callbacks and an SmtpNotifier
• Using Jinja templates and Airflow Variables to avoid hard-coded values
• Controlling flow with trigger rules, retries, child Dag triggers, and @task.branch
• Pausing a pipeline for human sign-off using the HITL operator

Course Structure

1. Intro to Airflow: the main components, a first Dag, shell commands, and a tour of the UI.
2. Building Dags in Airflow: operators, task decorators, scheduling, XCom, and sensors.
3. Maintaining and monitoring Airflow workflows: callbacks, notifications, logs, Jinja, Variables, and troubleshooting.
4. Controlling Dag logic: trigger rules, retries, branching, human approval, and the closing sales ETL pipeline.

Who Is This Course For?

It suits Python users who already write functions comfortably and have used a terminal. They want a more dependable way to run recurring data jobs than cron. Data engineers and analysts who maintain scheduled scripts will get the most from it.

Look elsewhere if you are new to Python or the command line, because the listed prerequisites are real. Also look elsewhere if your main need is installing and administering an Airflow cluster. The outline centers on writing and operating Dags, not on infrastructure.

Format & Time Commitment

The course is video lessons paired with exercises: 16 videos and 57 exercises, adding up to roughly four hours. Most exercises are short and worth 50 to 100 XP each. You could finish it over a weekend or in a handful of evening sessions. It is also one of the courses in DataCamp's Data Engineer in Python track.

Pros and Cons

Pros

• The course targets Airflow 3.1.6, so you learn current syntax rather than legacy habits.
• Exercises appear in every chapter, so you write Dags instead of only watching.
• The final project strings together scheduling, branching, and approval into one realistic pipeline.
• Alerting, logging, and troubleshooting get their own lessons.
• The topic sequence runs from basics to production-style logic, with no big jumps.

Cons

• Four hours is short. You will get the foundations, not deep coverage of topics like scaling, deployment, or running Airflow on a managed service.
• The prerequisites are firm. Learners without intermediate Python and basic shell skills will struggle early.
• The Statement of Accomplishment records completion. It is not a proctored exam, and employers may weigh it lightly. The page lists a separate Airflow Fundamentals certification, which is a different credential.
• The exercises are guided, so you may still need your own side project to feel confident setting up Airflow from scratch.

FAQ

Do I need to know Airflow already? No. The course starts with the components and a first Dag. You do need intermediate Python and an introduction to the shell.

Which Airflow version does it use? Version 3.1.6, so the examples reflect the current release line.

What do I get at the end? A Statement of Accomplishment that you can add to a LinkedIn profile or résumé. It shows you completed the course, nothing more.

Is this the only way to learn Airflow on DataCamp? No. The course is part of the Data Engineer in Python track, which also covers wider data engineering topics. You can take it on its own if Airflow is all you need.

Want to see the full lesson list and try the first exercises? The course page on DataCamp has everything.