Welcome to our state-of-the-art data pipeline project leveraging Azure IoT Hub and Databricks to facilitate real-time machine learning recommendations. Designed meticulously to simulate an IoT equipment setup, this project is pivotal in reading data seamlessly and maneuvering them through the Bronze, Silver, and Gold layers for insightful analysis and recommendation model building.
- IoT Device Simulation: Simulate an IoT equipment setup to generate real-time data.
- Azure IoT Hub Integration: Integrated with Azure IoT Hub for reliable and secure bi-directional communications between IoT applications and the devices it manages.
- Databricks Implementation: Utilizing Databricks to read data and transition it through various processing layers - Bronze, Silver, and Gold.
- Real-Time ML Recommendations: Endowing the system with a real-time recommendation model through machine learning.
- Azure Account
- Databricks Account
- Basic knowledge of Azure IoT and Databricks
- Python 3.x
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Clone the repository: https://github.com/jaimeandresalas/iot-end-to-end-databricks.git
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Navigate to the project directory: cd iot-end-to-end-databricks
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(Any other installation steps)
(Provide detailed steps on how to use the application - include code snippets, screenshots, etc.)
For detailed documentation, please refer to the documentation folder.
We welcome contributions from the community. If you wish to contribute, please take a look at our contributing guidelines.
This project is licensed under the MIT License.
- Project Lead: Jaime Salas
- Project URL: https://github.com/jaimeandresalas/iot-end-to-end-databricks
(Any acknowledgements or credits you wish to give)