Head to the Docker website and install Docker Desktop for your operating system. Airflow runs in docker containers and installs everything needed, such as a web server and a local database. Docker is extremely lightweight and powerful. Docker is a container management system that allows you to run virtual environments on local computers and in the cloud. If you don't have Homebrew installed, you can install it by visiting the Homebrew website and running the command they provide. Homebrew will be the easiest way for you to install the Astronomer CLI. In this post, I'll walk through the basics of Airflow and how to get started with Astronomer. You can even leverage Airflow for Feature Engineering, where you apply data transformations in your Data Warehouse, creating new views of data. A common use case for Airflow is taking data from one source, transforming it over several steps, and loading it into a data warehouse. Astronomer is a managed Airflow service that allows you to orchestrate workflows in a cloud environment. The power of writing a DAG with Python means that you can leverage the powerful suite of Python libraries available to do nearly anything you want. One of the biggest advantages to Airflow, and why it is so popular, is that you write your configuration in Python in the form of what is referred to as a DAG ( Directed Acyclic Graph). Apache Airflow is an open-source workflow management platform that helps you build Data Engineering Pipelines. In the near future, Astronomer will have an option for KEDA autoscaling on Celery, combining a lot of the great features between Kubernetes executor and Celery executor.We'll start here with Airflow. It’s also great for environments with long running tasks and users pushing code when jobs are running (since there is no grace period concept). The Kubernetes executor is great for dags that have really different requirements between tasks (e.g, the first task may be a sensor that only requires a few resources, but the downstream tasks have to run on your GPU node pool with a higher CPU request). In summary, the Celery executor is a great fit for any environment where the tasks are “similar” and you can find a configuration for the worker that fits all sizes, or for any tasks that need to run quickly (since the workers are “always on”).
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