Introduction to Airflow in Python
Learn how to implement and schedule data engineering workflows.
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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
Intro to Airflow
FreeIn this chapter, you’ll gain a complete introduction to the components of Apache Airflow and learn how and why you should use them.
Introduction to Airflow50 xpRunning a task in Airflow50 xpExamining Airflow commands50 xpAirflow DAGs50 xpDefining a simple DAG100 xpWorking with DAGs and the Airflow shell50 xpTroubleshooting DAG creation100 xpAirflow web interface50 xpStarting the Airflow webserver50 xpNavigating the Airflow UI50 xpExamining DAGs with the Airflow UI50 xp - 2
Implementing Airflow DAGs
What’s up DAG? Now it’s time to learn the basics of implementing Airflow DAGs. Through hands-on activities, you’ll learn how to set up and deploy operators, tasks, and scheduling.
Airflow operators50 xpDefining a BashOperator task100 xpMultiple BashOperators100 xpAirflow tasks50 xpDefine order of BashOperators100 xpDetermining the order of tasks100 xpTroubleshooting DAG dependencies50 xpAdditional operators50 xpUsing the PythonOperator100 xpMore PythonOperators100 xpEmailOperator and dependencies100 xpAirflow scheduling50 xpSchedule a DAG via Python100 xpDeciphering Airflow schedules100 xpTroubleshooting DAG runs50 xp - 3
Maintaining and monitoring Airflow workflows
In this chapter, you’ll learn how to save yourself time using Airflow components such as sensors and executors while monitoring and troubleshooting Airflow workflows.
Airflow sensors50 xpSensors vs operators100 xpSensory deprivation50 xpAirflow executors50 xpDetermining the executor50 xpExecutor implications100 xpDebugging and troubleshooting in Airflow50 xpDAGs in the bag50 xpMissing DAG100 xpSLAs and reporting in Airflow50 xpDefining an SLA100 xpDefining a task SLA100 xpGenerate and email a report100 xpAdding status emails100 xp - 4
Building production pipelines in Airflow
Put it all together. In this final chapter, you’ll apply everything you've learned to build a production-quality workflow in Airflow.
Working with templates50 xpCreating a templated BashOperator100 xpTemplates with multiple arguments100 xpMore templates50 xpUsing lists with templates100 xpUnderstanding parameter options50 xpSending templated emails100 xpBranching50 xpDefine a BranchPythonOperator100 xpBranch troubleshooting50 xpCreating a production pipeline50 xpCreating a production pipeline #1100 xpCreating a production pipeline #2100 xpAdding the final changes to your pipeline100 xpCongratulations!50 xp
In the following tracks
Data EngineerCollaborators
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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