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Introduction to Airflow in Python

Learn how to implement and schedule data engineering workflows.

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4 Hours16 Videos55 Exercises18,987 Learners4050 XPData Engineer Track

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Course Description

Delivering data on a schedule can be a manual process. You write scripts, add complex cron tasks, and try various ways to meet an ever-changing set of requirements—and it’s even trickier to manage everything when working with teammates. Airflow can remove this headache by adding scheduling, error handling, and reporting to your workflows. In this course, you’ll master the basics of Airflow and learn how to implement complex data engineering pipelines in production. You'll also learn how to use Directed Acyclic Graphs (DAGs), automate data engineering workflows, and implement data engineering tasks in an easy and repeatable fashion—helping you to maintain your sanity.

  1. 1

    Intro to Airflow


    In this chapter, you’ll gain a complete introduction to the components of Apache Airflow and learn how and why you should use them.

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    Introduction to Airflow
    50 xp
    Running a task in Airflow
    50 xp
    Examining Airflow commands
    50 xp
    Airflow DAGs
    50 xp
    Defining a simple DAG
    100 xp
    Working with DAGs and the Airflow shell
    50 xp
    Troubleshooting DAG creation
    100 xp
    Airflow web interface
    50 xp
    Starting the Airflow webserver
    50 xp
    Navigating the Airflow UI
    50 xp
    Examining DAGs with the Airflow UI
    50 xp

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Data Engineer


hadrien-d4e73b49-bc29-46b7-a485-2f598f38e3b9Hadrien Lacroixlis-sulmontLis Sulmont
Mike Metzger Headshot

Mike Metzger

Data Engineer Consultant @ Flexible Creations

Mike is a consultant focusing on data engineering and analysis using SQL, Python, and Apache Spark among other technologies. He has a 20+ year history of working with various technologies in the data, networking, and security space.
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