Abstract
Malloy Composer is an open-source Data Science project. Malloy Composer is a simple application to build dashboards or run ad-hoc queries using an existing Malloy model. The Malloy Composer Demo is provided as a working example of an application built on top of Malloy. If you have any questions about getting it running, please reach out to us for help! It is built using TypeScript. The complete source code is publicly available on GitHub under the MIT License, making it a useful reference for students building a Data Science mini project or final-year project.
1. Introduction
The Malloy Composer Demo is provided as a working example of an application built on top of Malloy. If you have any questions about getting it running, please reach out to us for help! If you find bugs or have feature requests, you can submit them as issues in this repo. Learn how to use the composer
Malloy Composer can be run with a built in web server (Full Mode) and can access BigQuery, Postgres and DuckDB or in WASM Mode, running entirely in a web browser. In WASM Mode, Malloy Composer can only access csv and parquet files (via DuckDB).
The composer is only intended for demo purposes, and is not a finished or supported product.
2. Objective
Malloy Composer is a simple application to build dashboards or run ad-hoc queries using an existing Malloy model
This project demonstrates how TypeScript can be applied to a real-world Data Science problem.
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/malloydata/malloy-composer.git
cd malloy-composerFull 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.
- What is the source of the dataset and how was missing data handled?
- Which exploratory analysis steps revealed the most useful insight?
- Why were these particular charts chosen to present the data?
- Which statistical or ML technique supports the conclusions?
- How could the analysis be automated or refreshed with new data?
9. Source Code & License
This project is developed by malloydata and published on GitHub under the MIT License. Please follow the license terms and credit the original author when you use or modify this code.
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