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Yin Yin Chan

Getting Started: Workflow Setup with Github Codespaces, Visual Studio Code, and Docker

by Yin Yin Chan 🤓 June 2026

TL;DR

This is a simple workspace setup to simplify the data engineering workflow. Initializing from codespaces to tap into their preconfigured development environments. Launching the codespace into a desktop Visual Studio Code editor for productivity. Containerization with Docker to make our programs portable.

Getting Started

Github Codespaces

  1. Create a new Github repository and initialize with Readme and python .gitignore.
  2. Clone the repository into a Codespace
    • Click on “<> Code”
    • Click on “Create codespace on main”
    • This should launch the browser-based workspace

We use Github Codespaces to remove setup friction and initialize the project from a standardized cloud setup. Codespaces comes with a preconfigured development environment without us having to configure it on our local desktop. The workflow here will launch a codespace, but we will use a desktop IDE (VS Code).

Visual Studio Code

The next set of instructions assumes you have VS Code already installed on your local machine.

  1. From the browser-based codespace,
    • Click on the hamburger menu icon
    • Click on “Open VS Code Desktop”
  2. Open the terminal:
    • Go to “Terminal”
    • Click “New Terminal”

We can run this codespace on the desktop using Visual Studio Code to avoid lag (as opposed to running it in-browser) and for a more productive workflow.

Docker, Python, uv

We use Docker to make our program portable.

  1. Docker is pre-installed by default, but let’s check. And here’s a Docker CLI cheatsheet
    docker --version
    
  2. Install python onto your codespace and check the python version
    apt update && apt install python3
    python3 -V
    
  3. I prefer to use uv as the virtual environment for package management. I don’t want to install packages globally on my system.
    pip install uv
    

© 2026 Yin Yin Chan. Designed with heart. Built with code.