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Stream processing pipeline template with a simple extensible visualization

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Hackathon Quix visualization template

This template includes a real-time data processing pipeline with the following services:

  • demo-data-source: A car telemetry data source for demonstration purposes.
  • starter-visualization: A simple template that reads from a Kafka topic and sends data to a web page. This page can be customized via ChatGPT to create various visualizations.
%%{ init: { 'flowchart': { 'curve': 'monotoneX' } } }%%
graph LR;
demo-data-source[fa:fa-rocket demo-data-source &#8205] --> f1-data{{ fa:fa-arrow-right-arrow-left f1-data &#8205}}:::topic;
f1-data{{ fa:fa-arrow-right-arrow-left f1-data &#8205}}:::topic --> starter-visualization[fa:fa-rocket starter-visualization &#8205];


classDef default font-size:110%;
classDef topic font-size:80%;
classDef topic fill:#3E89B3;
classDef topic stroke:#3E89B3;
classDef topic color:white;
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How the starter-visualization works

This application receives data from a Kafka topic and sends it to a WebSocket connection without any transformation. The same application serves an HTML webpage located at ./templates/index.html, which reads from the WebSocket and creates a real-time visualization.

How to use it

  1. Run the visualization application: Ensure your data source is running.
  2. View initial visualization: The time-series data will be displayed on the screen.
  3. Customize the visualization:
    • Copy the content of ./templates/index.html and paste it into ChatGPT, describing how you want your visualization to be customized.
    • Use the modified code provided by ChatGPT to replace the content in ./templates/index.html.
    • Refresh the webpage to see the updated visualization.
  4. Iterate: Repeat this process until you achieve the desired visualization.

Example

  • Initial visualization: This is the default visualization provided by the template.

    Initial visualization

  • ChatGPT conversation: View Conversation

  • Improved visualization: After a few iterations with ChatGPT, the visualization was enhanced. However, there's potential to achieve even more with additional creativity and imagination.

    Improved visualization

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Stream processing pipeline template with a simple extensible visualization

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  • Python 64.4%
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