#AIRFLOW PYTHON EXAMPLE UPDATE#

Operators are wrappers that cover the task. Op = DummyOperator (task_id = 'task' ) Operators

The DAG itself doesn’t care about what is happening inside the tasks it is merely concerned with how to execute them - the order to run them in, how many times to retry them, if they have timeouts, and so on.

It will also say how often to run the DAG - maybe “every 5 minutes starting tomorrow”, or “every day since January 1st, 2020”.

Example DAG above defines four Tasks - A, B, C, and D - and dictates the order in which they have to run, and which tasks depend on what others.DAG (Directed Acyclic Graph)Ī DAG (Directed Acyclic Graph) is the core concept of Airflow, collecting Tasks together, organized with dependencies and relationships to say how they should run. Worker: The process or sub-process executing the task. Metastore: Database where metadata is stored.Įxecutor: Class that defines how tasks should work. It is the most important component of Airflow. Scheduler: The daemon that schedules the workflows. Web Server: A Flask server that serves the UI. You can write your plugins and integrate them easily.

#AIRFLOW PYTHON EXAMPLE UPDATE#

Problematic tasks can be restarted etc.Įxtensible: No need to wait for Airflow update when a new tool comes out. Errors that occur in data pipelines and where they occur can be easily observed. Scalable: As many tasks as desired can be easily run in parallel. As a result, Airflow provides tremendous dynamics when creating our tasks. Benefits of Apache Airflowĭynamic: What can be done with Python also can be done with Airflow.

Such transactions can be managed in an advanced way by using Airflow. Roughly, as in the example above, taking the data from a source and saving it to the target after certain operations are called ETL (Extract Transform Load). If you have a lot of data pipelines like this, it will eventually become overwhelming.What if an error occurs in any of these stages? There may be an error in the API from which you are pulling the data, there may be an error while processing the data, or there may be an error while saving to the DB.Imagine you have a data pipeline like the one above. Airflow is an orchestration tool that ensures that tasks are running at the right time, in the correct order, and in the right way.

Airflow python example