Apache Airflow Drag-and-Drop Plugin 🚀

The Apache Airflow Drag-and-Drop Plugin enhances the Apache Airflow UI by allowing users to create and manage workflows using a drag-and-drop interface.

This plugin simplifies the process of creating DAGs (Directed Acyclic Graphs) by providing an intuitive visual interface. 🎉

Features ✨

✅ Drag-and-Drop Interface – Easily create and modify DAGs visually.

✅ Predefined Templates – Use templates for common workflows.

✅ Custom Operators – Extend the palette with custom operators.

✅ Real-Time Validation – Validate workflows before deployment.

✅ Export DAG Code – Save workflows as .py file.

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Installation 🛠️

🔹 For Non-Dockerized Airflow Setup

1️⃣ Install the Plugin via pip:

pip install apache-airflow-dragdrop-plugin

2️⃣ Restart Airflow Services:

airflow webserver --reload
airflow scheduler --daemon

3️⃣ Access the Plugin:

Open the Airflow UI and navigate to the "Drag-and-Drop" tab.

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🐳 For Dockerized Airflow Setup

Option 1️⃣: Add the Plugin to requirements.txt

1. Add the following line to your requirements.txt file:

   apache-airflow-dragdrop-plugin
   

2. Rebuild your Docker image:

   docker-compose build -t your-image-name
   

3. Restart your Docker containers:

   docker-compose up -d
   

Option 2️⃣: Install the Plugin Directly Inside the Running Container

1. Install the plugin inside the running Airflow container:

   docker exec -it <container_id> pip install apache-airflow-dragdrop-plugin
   

2. Restart the Airflow webserver and scheduler inside the container:

   docker exec -it <container_id> airflow webserver --reload
   docker exec -it <container_id> airflow scheduler --daemon
   

3. If needed, restart the container:

   docker restart <container_id>
   

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Usage 🖥️

🚀 Creating a New Workflow

1️⃣ Open the Drag-and-Drop Interface : Navigate to the "Drag-and-Drop" tab in the Airflow UI.

2️⃣ Add Nodes : Drag operators onto the canvas and connect them.

3️⃣ Configure Nodes : Click on each node to set its properties.

4️⃣ Validate and Save : Ensure your workflow is error-free and save it as a DAG.

Contributing

We welcome contributions! Follow these steps:

1️⃣ Fork the repository

2️⃣ Create a new branch for your feature or bugfix.

3️⃣ Submit a pull request with a detailed description.

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License

This project is licensed under the Apache License 2.0. See the LICENSE file for details.

Support

If you encounter any issues or have questions, please open an issue on our GitHub repository.

Acknowledgments

Special thanks to the Apache Airflow community for their support and inspiration.

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Connect with Us

akshay.thakare031@gmail.com

https://www.linkedin.com/in/akshaythakare3