Briefer

Abstract

1. Introduction

Briefer has been acquired by Resend. Read our latest blog post to learn more about what happens next. ⭐

We can help you deploy Briefer for free. Book a time here.

Here's a brief demo (no pun intended) of Briefer. It may take a few seconds to load.

2. Objective

Dashboards and notebooks in a single place. Create powerful and flexible dashboards using code, or build beautiful Notion-like notebooks and share them with your team.

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

4. Technology Stack

• Fetch data from multiple sources and join them together without having to create a data pipeline beforehand
• Hit APIs to gather data or enrich your own
• Use machine learning to include predictive analytics in your dashboards

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/briefercloud/briefer.git
cd briefer

# Install Briefer
pip install briefer

# Run Briefer and access it on http://localhost:3000
briefer

# Run Briefer using Docker
docker run -d \
  -p 3000:3000 \
  -v briefer_psql_data:/var/lib/postgresql/data \
  -v briefer_jupyter_data:/home/jupyteruser \
  -v briefer_briefer_data:/home/briefer \
  briefercloud/briefer

# Run Briefer using Docker
docker run -d `
  -p 3000:3000 `
  -v briefer_psql_data:/var/lib/postgresql/data `
  -v briefer_jupyter_data:/home/jupyteruser `
  -v briefer_briefer_data:/home/briefer `
  briefercloud/briefer

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 briefercloud 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.