Gobi

Abstract

1. Introduction

A lightweight, API-first Business Intelligence (BI) engine built with Go - designed for modern applications that need embedded analytics, automated reporting, and real-time data visualization. The ultimate Go-native BI solution for developers.

2. Objective

🚀 Modern Go-Native Business Intelligence (BI) Engine – API-first, lightweight BI for analytics, charting, and automated reporting with 3D visualization.

This project demonstrates how Go can be applied to a real-world Data Science problem.

4. Technology Stack

• Backend: Go 1.21+ with Gin framework
• Database: SQLite (dev) / MySQL/PostgreSQL (prod)
• Authentication: JWT + API Keys with bcrypt hashing
• Charts: Custom 3D rendering with WebGL support
• Scheduling: Cron-based with timezone support
• Notifications: Webhook system with HMAC signatures
• Architecture: Clean Architecture with Repository Pattern
• Caching: Intelligent caching with go-cache

5. System Requirements

General requirements for this technology stack — check the README for exact versions.

• Go 1.20 or later
• Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/sy-vendor/Gobi.git
cd Gobi

# Clone and setup everything
git clone https://github.com/sy-vendor/gobi.git
cd gobi
make setup

# Start development server
make dev

# Server starts on http://localhost:8080
# Default admin: admin/admin123

# Build and run with Docker
docker build -t gobi .
docker run -p 8080:8080 gobi

# Or use docker-compose
docker-compose up -d

Full setup instructions are in the project README.

7. Future Enhancements

Suggested extensions you can add to make this your own project.

• Turn the analysis into an interactive dashboard
• Automate data refresh with a scheduled job
• Add a predictive model on top of the analysis

8. Viva / Review Questions

Common questions examiners ask for projects in this domain.

1. What is the source of the dataset and how was missing data handled?
2. Which exploratory analysis steps revealed the most useful insight?
3. Why were these particular charts chosen to present the data?
4. Which statistical or ML technique supports the conclusions?
5. How could the analysis be automated or refreshed with new data?

9. Source Code & License

This project is developed by sy-vendor and published on GitHub under the MIT License. Please follow the license terms and credit the original author when you use or modify this code.