# Apache Airflow For Newcomers

DevFeed: [Apache Airflow For Newcomers](<https://devfeed.tech/articles/apache-airflow-for-newcomers-32556.md>)

Original publisher: [Read original article](<https://airflow.apache.org/blog/apache-airflow-for-newcomers/>)

Author: Apache Airflow

Published: 2020-08-17T00:00:00Z

Content type: tutorial

Language: en

Sources: [Apache Airflow Blog](<https://devfeed.tech/sources/apache-airflow-blog.md>)

Topics: [airflow](<https://devfeed.tech/topics/airflow.md>), [Development](<https://devfeed.tech/topics/development.md>)

Tags: [airflow](<https://devfeed.tech/tags/airflow.md>), [apache-airflow](<https://devfeed.tech/tags/apache-airflow.md>), [community](<https://devfeed.tech/tags/community.md>), [dependencies](<https://devfeed.tech/tags/dependencies.md>), [python](<https://devfeed.tech/tags/python.md>), [scheduler](<https://devfeed.tech/tags/scheduler.md>), [scheduling](<https://devfeed.tech/tags/scheduling.md>), [tasks](<https://devfeed.tech/tags/tasks.md>), [workflows](<https://devfeed.tech/tags/workflows.md>)

## AI overview

This tutorial introduces Apache Airflow for newcomers, explaining how to author workflows with Python scripts, represent them as DAGs, define task dependencies, and use the scheduler to execute tasks when dependencies are met.

## Source excerpt

Apache Airflow is a platform to programmatically author, schedule, and monitor workflows. A workflow is a sequence of tasks that processes a set of data. You can think of workflow as the path that describes how tasks go from being undone to done. Scheduling, on the other hand, is the process of planning, controlling, and optimizing when a particular task should be done. Authoring Workflow in Apache Airflow. Airflow makes it easy to author workflows using python scripts. A Directed Acyclic Graph (DAG) represents a workflow in Airflow. It is a collection of tasks in a way that shows each task's relationships and dependencies. You can have as many DAGs as you want, and Airflow will execute them according to the task's relationships and dependencies. If task B depends on the successful execution of another task A, it means Airflow will run task A and only run task B after task A. This dependency is very easy to express in Airflow. For example, the above scenario is expressed as task_A >> task_B Also equivalent to task_A.set_downstream(task_B) That helps Airflow to know that it needs to execute task A before task B. Tasks can have far more complex relationships to each other than expressed above and Airflow figures out how and when to execute the tasks following their relationships and dependencies. Before we discuss the architecture of Airflow that makes scheduling, executing, and monitoring of workflow an easy thing, let us discuss the Breeze environment. Breeze Environment The breeze environment is the development environment for Airflow where you can run tests, build images, build documentations and so many other things. There are excellent documentation and video on Breeze environment. Please check them out. You enter the Breeze environment by running the ./breeze script. You can run all the commands mentioned here in the Breeze environment. Scheduler The scheduler is the component that monitors DAGs and triggers those tasks whose dependencies have been met. It watc