Since the release of dynamic task mapping in Airflow 2.3, many of the concepts in this webinar have been changed and improved upon. Please check out our newer Dynamic Tasks in Airflow webinar for the latest dynamic dag best practices, including how dynamic tasks can accomplish many of the same use cases more efficiently.
The simplest way of creating an Airflow DAG is to write it as a static Python file. However, sometimes manually writing DAGs isn't practical.
Maybe you have hundreds or thousands of DAGs that do similar things, with just a parameter changing between them. Or maybe you need a set of DAGs to load tables, but don't want to manually update DAGs every time those tables change. In these cases, and others, it can make more sense to dynamically generate DAGs. Because everything in Airflow is code, you can dynamically generate DAGs using Python alone.
In this webinar, we'll talk about when you might want to dynamically generate your DAGs, show a couple of methods for doing so, and discuss problems that can arise when implementing dynamic generation at scale.
In this webinar we cover:
- How Airflow identifies a DAG
- Use cases for dynamically generating DAGs
- Commonly used methods for dynamic generation
- Pitfalls and common issues with dynamic generation
Want to become an airflow expert? Check out our guide: www.astronomer.io/guides
#learnwithastronomer #dags #dynamicdags
5 июл 2024
