Tellery

Abstract

1. Introduction

Tellery lets you build metrics using SQL and bring them to your team. Metrics are defined consistently and constantly updated, no longer scattered across tools, and recreated with no oversight.

2. Objective

Tellery lets you build metrics using SQL and bring them to your team. As easy as using a document. As powerful as a data modeling tool.

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

3. Key Features / Modules

• Let business users get answers in just a few clicks. No longer communicate in tables and columns.
• Build beautiful documents & dashboards visually with drag-and-drop ease
• An editor with rich text styling features, slash commands, and markdown support
• A modern SQL editor with multi-tabs and auto-complete
• Model your data with just SQL and reference like CTEs
• Review work and collaborate in real-time with multiplayer
• Import dbt models and sources and inspect their metadata while editing SQL

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/tellery/tellery.git
cd tellery

1. Running your own Tellery with Kubernetes

# Clone the Tellery repo
git clone https://github.com/tellery/tellery.git

# Change directories into the demo directory
cd tellery/deploy/compose

# View or edit config file (optional)
# vim .env

# Run docker compose
docker-compose up

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 tellery 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.