Optimize Workflows with Apache Airflow Operators

This course will teach you to create efficient DAGs using branching, file sensors, PythonOperators, and DummyOperators, and to create custom operators for logging, enabling you to handle complex data processing with ease.

Apache Airflow offers a wide variety of operators to help you optimize your workflows. In this course, Optimize Workflows with Apache Airflow Operators, you’ll gain the ability to streamline your data pipelines and enhance workflow efficiency using various Airflow operators. First, you’ll explore how to create a DAG that performs conditional data processing using a branching operator and checks for file availability with a FileSensor. Next, you’ll discover how to perform data validation with a PythonOperator, group tasks using a DummyOperator, and utilize Airflow variables for dynamic processing. Finally, you’ll learn how to generate both aggregated and filtered reports, write them out to an output folder, and then create your own custom operator to clean data that you will use in your workflow. When you’re finished with this course, you’ll have the experience working with a variety of Airflow operators needed to optimize and maintain complex data workflows efficiently.

• Course Overview 1min
• Create and Optimize Workflows with Operators 21mins