Note the (airflow) before your cursor, it indicates the name of the current virtualenv that you have activated. Let’s activate this environment and install Airflow on it. This should have created a directory named airflow in your working directory. In order to use Python3, we use the -p argument if your system’s default Python version is 3, then you don’t need to specify it. The above command will create a virtual environment named airflow, which we have specified explicitly. The first thing we will do is create a virtual environment with Python 3 in which we will install and run Airflow. Please do not use Python 2 anymore as it has reached its end of life. The only thing you need to be installed on your machine for this is Python 3 and the Python package virtualenv. Kubernetes setup using Helm, for running KubernetesExecutor.Setup using Docker, in which we run CeleryExecutorusing Redis as a queue.In this setup, we run SequentialExecutor, which is ideal for testing DAGs on a local development machine. Basic setup using a virtualenv and pip.We pick one executor per approach, to explain: In each approach, one can use one of three types of executors. In this blog, we explain three different ways to set it up. There are multiple ways to set up and run Apache Airflow on one’s laptop. Now that we know about Airflow’s different components and how they interact, let’s start with setting up Airflow on our workstation so that we can locally test and run the pipelines that we build.
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How to Install Apache Airflow to Run Different Executors
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