env-airflow

Docker-based development environment for Apache Airflow, optimized for handling spatial datasets.

Getting Started

Build the Docker Image

Build the Docker image using the following command:

docker build -f Dockerfile.airflow -t extended-airflow:3.0.0 .

Start the Container

After building the image, start the container with Docker Compose:

docker compose up

Access the Airflow UI

Once the container is running, access the Airflow UI by navigating to the appropriate URL in your browser (e.g., http://localhost:8080).

Develop DAGs

To develop DAGs, you can connect to the airflow-scheduler container. Using VS Code, follow these steps:

1. Install the Dev Containers extension.
2. Open the Command Palette (Ctrl+Shift+P or Cmd+Shift+P on macOS) and select Remote-Containers: Attach to Running Container.
3. Choose the airflow-scheduler container from the list.
4. Once connected, navigate to the /workdir.

Connect to the Database

With DBeaver use the following connection details:

• Host: localhost
• Port: 5432 (or the port configured in your docker-compose.yml)
• Database: airflow
• Username: airflow
• Password: airflow

Add check on Show all databases

Change Color in VS Code Appearance

To customize the color theme of the VS Code window, update the .vscode/settings.json file with the following configuration:

{
    "workbench.colorCustomizations": {
        "titleBar.activeBackground": "Color of your choice in hex"
    }
}

Have fun :)