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Data Science and AI
Data Pipelines with Apache Airflow
Schedule, run and debug workflows as code
A short classroom course in scheduling data work reliably. The modules follow the core concepts of the Apache Airflow 3.4 documentation: architecture, Dags and Dag runs, tasks and operators, sensors, TaskFlow, XComs, variables and params, executors, backfill, and debugging Dags. This course prepares for no exam.
8Modules
IntermediateLevel
6 weeksDuration
COURSE THUMBNAIL
What you will be able to do
Explain the parts of an Airflow deployment
Write a Dag that runs tasks in the right order on a schedule
Wait for external events with sensors
Pass data between tasks safely
Re-run past periods with backfill and debug a failing Dag
Course outline
01
Airflow architecture overview
02
Dags and Dag runs
03
Tasks and operators
04
Sensors
05
TaskFlow
06
Passing data: XComs, variables and params
07
Executors and backfill
08
Debugging Airflow Dags; project pipeline
Tools you will use
Apache AirflowPythonDockerPostgreSQL
What you need
Comfortable programming in Python
Basic SQL and Linux command-line skills
