Airflow DAGs are Python code (which can pretty much do anything you want) and Airflow has hundreds configuration options (which can dramatically change Airflow behavior). Those two facts contribute to endless combinations that can run the same workloads, but only a precious few are efficient. The rest will result in failed tasks and excessive compute usage, costing time and money.
This talk will demonstrate how small changes can yield big dividends, and reveals some code improvements and Airflow configurations that can reduce costs and maximize performance.
