CBoard

Abstract

1. Introduction

CBoard is not only a analysis platform that supports interactive multi-dimensional report design and data analysis for user, but also a BI product development platform for developer. Powered by Shanhai Chuguo Information and Technology Co., Ltd.

Light weight architecture, common technology stack, self designed multi-dimension engine, clear optimization rule, small application running on your big data stack!

2. Objective

An easy to use, self-service open BI reporting and BI dashboard platform.

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

3. Key Features / Modules

• Simple and beautiful interface and layout
• Interactive, drag-and-drop OLAP classisc report development experience
• One dataset, multiple report widgets. Maximize reuse query result
• Cube level data refresh / realtime chart for quick query
• Role Based Access Control
• More than 20 chart types and dashboard insight or email report
• Multiple types data source connection
• Support JDBC data connection (Almost all kinds database that has implemented JDBC protocal)
• Support Native Elasticsearch connection for version 1.x, 2.x, 5.x
• Support Native Kylin connection for version 1.6

4. Technology Stack

5. System Requirements

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

• Node.js (LTS) and npm
• A modern web browser
• VS Code or any code editor
• Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/TuiQiao/CBoard.git
cd CBoard

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 TuiQiao and published on GitHub under the Apache License 2.0. Please follow the license terms and credit the original author when you use or modify this code.