![]() ![]() ![]() Maybe you wanted to schedule a DAG at different times on different days, or daily except for holidays, or just daily at uneven intervals - all impossible with Airflow 1.10.X. One example: In Airflow 1.10.X, scheduling DAGs often involved struggling with counterintuitive start_date and execution_date attributes. Airflow 2.0, introduced in late 2020 and since updated with additional major features by a number of follow-on releases, has improved how enterprises handle orchestration in a material way. The Airflow community (including several top committers on our team at Astronomer) is constantly addressing issues and adding new features. Upgrade to Airflow’s latest versionįirst and foremost: upgrade to the current version to fully benefit from Apache Airflow. Airflow is an infinitely scalable tool that helps data engineers, scientists, and analysts write and deploy expressive data pipelines. In today’s digital economy, the ability to use data more efficiently can generate a significant competitive edge, which is why users turn to Apache Airflow in the first place. Steven Hillion, VP of Data & Machine Learning at Astronomer In the end, orchestration is about using data to drive actions, to create real business value. But it goes beyond simple data management. Why Airflow?ĭata orchestration provides the answer to making your data more useful and available. This article sets out ten best practices that will help all Apache Airflow users ensure their data pipelines run smoothly and efficiently. Whether you’re just beginning your Apache Airflow journey or you’re an experienced user looking to maximize data orchestration efficiency, we’ve got you covered. Streamline your data pipeline workflow and unleash your productivity, without the hassle of managing Airflow. ![]()
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