EDA

Abstract

1. Introduction

Edalitics (previously Enterprise Data Analytics) is an open-source analytics and dashboarding platform created by Jortilles. Its purpose is to make data exploration and visualization extremely easy — even for non-technical users — while still offering powerful features for advanced analysts.

2. Objective

Edalitics is an open-source business intelligence platform that lets you build interactive dashboards, analyze data from multiple sources, and connect AI assistants to your business data in minutes.

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

3. Key Features / Modules

• No-code dashboard creation.
• Advanced SQL query mode for power users.
• Tree mode to explore logical data models.
• KPI definitions and automatic e-mail alerts.
• Public dashboards shareable via url.
• Row Level Security (RLS) for controlled data access.
• Clean, modern, responsive UI.

4. Technology Stack

• Node.js, TypeScript, Angular.
• MongoDB for metadata and configuration storage
• A lightweight, extensible backend + intuitive frontend

5. System Requirements

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

• Node.js (LTS) and npm / yarn / pnpm
• VS Code or any code editor
• Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/jortilles/EDA.git
cd EDA

cd EDA/eda/eda_api
npm install
npm start

cd EDA/eda/eda_app
npm install
npm start

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 jortilles and published on GitHub under the GNU Affero General Public License v3.0. Please follow the license terms and credit the original author when you use or modify this code.