This instance will use the CeleryExecutor in Docker for running the scheduled jobs. Steps explained here apply to Ubuntu 18.04 or above. We are using Ubuntu 18.04 for running our test Airflow 2.2.3 instance. Install Docker service and Docker Compose on your host operating system. The prerequisite for running Airflow 2.2.3 as Docker container is the latest version of Docker service and Docker Compose utility. Getting started with Airflow 2.2.3 with Docker UI/UX interface of Airflow can easily be used to visualize complex data pipelines, it’s dependencies and the job progress, logs, and status. This software is one of the most robust platforms used by the data engineering team for scheduling complex jobs. We will also develop a simple job (DAG) that executes tasks in parallel.Īirflow is growing fast among organizations for orchestrating workflows or complex data pipelines using Python programming language. In this blog we are going to use Docker and Docker Compose for running Apache Airflow 2.2.3 on our system. Other important features of Airflow 2.0 are horizontal scalability, zero recovery time, easier maintenance, and Full REST API. Now Airflow is a leading workflow orchestration and job scheduling engine in the open-source world.Īpache Airflow 2.0 was released on December 17th, 2020 with major features including a refactored and highly available scheduler along with over 30 UI/UX improvements. In January 2019, Apache Airflow joined the top-level project list. This project was moved on to the Apache Software Foundation as a part of the Incubator Program in March 2016. Apache Airflow was developed by Airbnb’s Beauchemin in 2014 as an open-source enterprise job scheduling engine in Python programming language and was designed on the principle of "configuration as code". ^ "Introducing Amazon Managed Workflows for Apache Airflow (MWAA)". ![]()
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