Because Airflow is 100% code, knowing the basics of Python is all it takes to get started writing DAGs. However, writing DAGs that are efficient, secure, and scalable requires some Airflow-specific finesse. Whether you’re writing traditional ELT/ETL pipelines or complex ML workflows, we’re here to help you learn how to make Airflow work best for your use case.
In this webinar, we cover DAG writing best practices applicable to data engineers and data scientists on topics like DAG design, dynamic DAGs, and testing.
The code shown in this webinar can be found in this repo.
