Introduction to Apache Airflow in Python

You should be comfortable writing Python functions and have basic familiarity with the command line. The course uses Bash, Python operators, and touches on tools like PostgreSQL and Celery, so general programming experience helps.

Data engineers and Python developers who need to schedule, automate, and monitor data pipelines in production. It is especially useful for anyone currently managing workflows with cron jobs or ad hoc scripts who wants a more reliable and repeatable approach.

A DAG is a Directed Acyclic Graph — a map of tasks and the dependencies between them. Airflow uses DAGs to define the order in which tasks run, ensure nothing executes out of sequence, and make the entire pipeline visible and auditable.

You will be able to schedule and run Bash commands, Python scripts, and database operations, wait for external conditions using sensors, add branching logic for if-then workflows, and trigger pipelines manually or on a cron schedule.

An operator defines what a task does, such as running a Bash command or calling a Python function. A sensor is a special operator that waits for a condition to be met before proceeding. An executor is the underlying system that actually runs the tasks, such as the LocalExecutor or CeleryExecutor.

The course has four chapters. Chapter 1 introduces Airflow and its components. Chapter 2 covers building DAGs with operators and scheduling. Chapter 3 focuses on sensors, executors, debugging, and SLA monitoring. Chapter 4 covers templating, triggers, branching, and building a complete production pipeline.