# Other directories and files for your project │ ├── docker-compose.yml # Docker Compose file for defining your services │ ├── Dockerfile # Dockerfile for building your custom Airflow image │ ├── dags/ # Directory to store your Airflow DAGs ├── airflow/ # Directory for all Airflow-related files Here's a suggested structure for your project: my_project/ It's a good practice to keep all Airflow-related files in a dedicated directory to maintain a clean and organized project structure. If it's not installed, you can follow the official Docker Compose installation guide. You can check if Docker Compose is installed and see its version by typing: $ docker-compose -version It's typically included with the Docker installation on Windows and Mac, but may need to be installed separately on some Linux distributions. Choose the version that's appropriate for your operating system.Īfter installation, you can check if Docker is installed correctly by opening a terminal window and typing: $ docker -version Install Docker Composeĭocker Compose is a tool that allows us to define and manage multi-container Docker applications, which is what our Airflow setup will be. You can download Docker from the official Docker website. To check if Python is installed and see its version, open a terminal window and type: $ python -version Install Dockerĭocker allows us to containerize our Airflow setup. As of writing, Airflow requires Python 3.6 or above. You can download it from the official Python website. Install PythonĪpache Airflow is written in Python, so you'll need Python installed on your machine. For Airflow, you can install it using pip, Python's package installer. For Python and Docker, you can follow the official installation guide for your specific OS. Installing Python, Docker and Airflow is straightforward. To get started, you'll need to have the following software installed on your machine: Second, Docker makes it easy to version, distribute, and replicate your Airflow setup, which can be particularly useful in a team setting or when moving from development to production. First, Docker provides an isolated and consistent environment for your Airflow setup, reducing the chances of encountering issues due to differences in dependencies, libraries, or even OS. Running Airflow locally with Docker is a great idea for several reasons. Docker, on the other hand, is a platform that enables developers to package applications into containers-standardized executable components that combine application source code with the OS libraries and dependencies required to run that code in any environment. Apache Airflow is an open-source platform that allows you to programmatically author, schedule, and monitor workflows. Apache Airflow and Docker are two powerful tools that have revolutionized the way we handle data and software deployment.
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