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. It’s no surprise that one of the most requested topics in this year’s Airflow survey was more Airflow best practices content. We hear you, and we’ve got you covered with this hands-on workshop on DAG writing best practices.
Whether you’re writing traditional ELT/ETL pipelines or complex ML workflows, you’ll learn how to make Airflow work best for your use case. We’ll teach you best practices including things like:
- How to design your DAGs for easy readability and maintenance
- How to make your DAGs dynamic in an efficient and scalable way
- How to avoid common pitfalls that can cause performance issues
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