From the course: Apache Airflow Essential Training

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DAG using TaskFlow

DAG using TaskFlow - Apache Airflow Tutorial

From the course: Apache Airflow Essential Training

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DAG using TaskFlow

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- [Narrator] Now let's see how we can rewrite this same simple DAG using TaskFlow operators. The TaskFlow API was introduced in version 2.0 to make it simple for you to write your DAGs using Python code. So if most of your DAGs use plain Python code, you can eliminate a lot of the boilerplate involved with Python operators by using the TaskFlow API. The TaskFlow API is a new way to define workflows using a more Pythonic and intuitive syntax and it aims to simplify the process of creating complex workflows by providing a higher-level abstraction compared to the traditional DAG-based approach. If you notice on lines 11, 19, and 23, we make use of the @dag and @task decorators to identify the DAG and tasks in our workflow. On line 17, I've defined a function called dag_with_taskflow_api that I have decorated using the @dag decorator. The @dag decorator has parameters which span line 11 through 16. These contain the same…

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