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CarData Explorer

CarData Explorer is a premium, high-performance web application designed for browsing and searching through a vast database of over 78,000 car specifications. Extracted from the CarQuery API, the application features an optimized data architecture to ensure lightning-fast performance and a seamless user experience.

CarData Explorer Preview


Key Features

  • Tree Browser: Explore the car hierarchy structurally by Make > Model > Trim.
  • Data Table View: A professional-grade table for high-density data viewing with sticky headers and zebra striping.
  • Card Grid View: A modern, visual way to explore car models.
  • Advanced Filtering: Filter by keyword and release year range.
  • Dual Theme Support: High-contrast Dark and Light modes with persistent user preferences.
  • Premium UI: Built with a minimalist, outline-only aesthetic using Lucide Icons and the Inter typeface.
  • Fully Responsive: Optimized for browsing on desktops, tablets, and mobile devices.

Optimized Data Architecture

To maintain high performance while handling large datasets, this project uses a multi-tier data strategy:

  1. Global Index (web_data/index.json): A compact 3.8MB index used for instant searching, filtering, and the initial Tree/Grid views.
  2. Detail Fragments: Deep technical specifications are lazy-loaded from separate JSON files per manufacturer in web_data/details/ only when a user requests "View Specs."
  3. Raw Data Support: For heavy processing, the project includes raw datasets in the raw_data/ directory:
    • make_model_trim_compact.json (3.7MB)
    • make_model_trim_detailed.json (132MB+)
  4. Static Speed: Designed to be hosted on any static hosting platform (like GitHub Pages) without needing a database server.

Technology Stack

Local Development

Since the application uses fetch() to load JSON data, it must be run through a local server due to browser security (CORS) policies.

  1. Clone the repository:

    git clone https://github.com/imamhossain94/cardata-explorer.git
    cd cardata-explorer
  2. Run a local server:

    • Using Node.js: npx serve .
    • Using Python: python -m http.server 8000

Acknowledgments


Developed by Imam Hossain

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