In the world of managing data & ETL workflows, Apache Airflow is a crucial tool for automating tasks. However, getting the most out of it requires more than just a basic setup. In this article, we take a deep dive into the details of Airflow performance improvement, focusing on how it’s set up and configured. Through deep dive analysis, we uncover the details that can greatly improve how DAG tasks are carried out and how the Airflow scheduler performs. By optimizing & tuning the airflow environment adjustments to Airflow Environmental Configuration settings & DAG level changes, we unleash the full power of Airflow, making workflows more productive and reliable. Join us as we uncover the secrets to orchestrating tasks with accuracy and next-level achievements.
Airflow provides the custom ability to tune the performance of instance. We can modify environment-level settings or DAG-level changes to tune performance across all of the DAGs in your Airflow environment. In this session, we are going deep into airflow performance optimization through performance tuning & configuration along with Top Level Python code removal
