airflow taskflow api

Airflow Taskflow API: The Guide

Airflow Taskflow is a new way of writing DAGs at ease. As you will see, you need to write fewer lines than before to obtain the same DAG. That helps to make DAGs easier to build, read, and maintain. The Taskflow API has three main aspects: XCOM Args, Decorator, and

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ShortCircuitOperator in Apache Airflow: The guide

The ShortCircuitOperator in Apache Airflow is simple but powerful. It allows skipping tasks based on the result of a condition. There are many reasons why you may want to stop running tasks. Let’s see how to use the ShortCircuitOperator and what you should be aware of. By the way, if

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Dynamic Task Mapping in Apache Airflow

Dynamic Task Mapping is a new feature of Apache Airflow 2.3 that puts your DAGs to a new level. Now, you can create tasks dynamically without knowing in advance how many tasks you need. This feature is for you if you want to process various files, evaluate multiple machine learning

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dag dependencies

DAG Dependencies in Apache Airflow: The Ultimate Guide

DAG Dependencies in Apache Airflow might be one of the most popular topics. I received countless questions about DAG dependencies, is it possible? How? What are the best practices? and the list goes on. It’s funny because it comes naturally to wonder how to do that even when we are

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dynamic dags airflow

Dynamic DAGs in Apache Airflow: The Ultimate Guide

Airflow dynamic DAGs can save you a ton of time. As you know, Apache Airflow is written in Python, and DAGs are created via Python scripts. That makes it very flexible and powerful (even complex sometimes). By leveraging Python, you can create DAGs dynamically based on variables, connections, a typical

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Airflow TaskGroups: All you need to know!

Airflow TaskGroups have been introduced to make your DAG visually cleaner and easier to read. They are meant to replace SubDAGs which was the historic way of grouping your tasks. The problem with SubDAGs is that they are much more than that. They bring a lot of complexity as you

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