Dataline

Abstract

1. Introduction

Introducing DataLine, the simplest and fastest way to analyze and visualize your data!Generate and export charts, tables, reports in seconds with DataLine - Your AI-driven data analysis and visualization tool

We're looking for maintainers! If you're interested, message me on Discord

Technical or non-technical people who want to explore data, fast.

2. Objective

Chat with your data - AI data analysis and visualization on CSV, Postgres, MySQL, Snowflake, SQLite...

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

3. Key Features / Modules

• Broad DB support: Postgres, MySQL, Snowflake, Excel, CSV, SQLite, and more
• Generating and executing SQL from natural language
• Ability to modify SQL results, save them, and re-run
• Better support for explorative questions
• Querying data files like CSV, Excel, SQLite, sas7bdat (more connection types)
• Charting via natural language
• Modifying chart queries and re-rendering/refreshing charts
• Dashboards and triggers
• Knowledge base and 'trainable' examples (flavor of RAG)
• More advanced charting options (bubble, stacks, etc.)

4. Technology Stack

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/RamiAwar/dataline.git
cd dataline

docker run -p 7377:7377 -v dataline:/home/.dataline --name dataline -e ALLOWED_ORIGINS="http://123.123.12.34:7377,https://123.123.12.34:7377" ramiawar/dataline:latest

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