#Airflow docker operator example full

Airflow Worker - picks up the tasks and actually performs the work.Airflow Scheduler - the “heart” of Airflow, that parses the DAGs, checks the scheduled intervals and passes the tasks over to the workers.If a job fails, we can only rerun the failed and the downstream tasks, instead of executing the complete workflow all over again.Īirflow is composed of three main components: Moreover, we can split a data pipeline into several smaller tasks.

This approach allows us to run independent tasks in parallel, saving time and money. ApacheĪirflow also has a web interface for monitoring and visualizing the status of workflows, as well as a flexible architecture that allows it to integrate with a wide range of third-party tools and services. A DAG typically consists of several tasks or actions that need to be executed in a specific order. It allows users to define and execute complex workflows as Directed Acyclic Graphs (DAGs) and provides a rich set of features for managing dependencies, parallelism, and task execution. Why Airflow?Īpache Airflow is an open-source platform used to programmatically author, schedule, and monitor workflows.

#Airflow docker operator example full

You can find the full codes for this post on my GitHub. In this post, we will create a lightweight, standalone, and easily deployed Apache Airflow development environment in just a few minutes.