DataVerse

DataVerse is a cutting-edge data analysis platform designed to handle large datasets with speed and efficiency. Built with modern technologies, it provides a seamless experience for data analysts and developers to explore, filter, and query data using both no-code and code-driven approaches.

Features

• Dataset Upload: Easily upload datasets in CSV, XLSX, and JSON formats.
• Notebook Interface: Create notebooks to organize and analyze data, supporting multiple views and operations in a single workspace.
• No-Code Filters: Apply powerful filters and transformations without writing a single line of code. Perfect for users with minimal coding experience.
• SQL Script Support: Leverage the full power of SQL to query and manipulate your data directly within DataVerse.
• Python Script Support: Use Python for advanced data transformations and analysis.
• Blazing Fast Performance: Process large datasets efficiently with Rust and DuckDB, ensuring quick and reliable data operations.
• Secure Data Handling: Data encryption ensures that your datasets remain secure and protected.

Technologies Used

• Backend: Rust, DuckDB
• Frontend: React, TypeScript
• Database: Postgres
• File Storage: Supabase
• Deployment: Vercel, Render

Getting Started

Prerequisites

• Rust: Ensure you have Rust installed. Install Rust
• Node.js: Required for the frontend. Install Node.js
• Postgres: Set up a PostgreSQL database.
• Supabase: For file storage and database management.

Installation

1. Clone the Repository:

   git clone https://github.com/yourusername/dataverse.git
   cd dataverse

2. Backend Setup:

• Navigate to the backend folder:

  cd backend

• Install dependencies:

  cargo build

• Run the backend server::

  cargo run

3. Frontend Setup:

• Navigate to the frontend folder:

  cd frontend

• Install dependencies:

  npm install

• Run the backend server::

  npm run dev

Usage

• Upload a Dataset: Use the interface to upload your dataset (CSV, XLSX, JSON).
• Create a Notebook: Organize your analysis within a notebook.
• Apply Filters: Utilize no-code filters for quick data transformations.
• Run SQL Queries: Query the dataset using SQL within the notebook.
• Analyze with Python: For complex analysis, switch to Python scripting.

License

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

Contact

For questions or support, please contact: Usman Ghani: usmanghani564.ug9@gmail.com