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
- Create a new Github repository and initialize with Readme and python .gitignore.
- 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.
- From the browser-based codespace,
- Click on the hamburger menu icon
- Click on “Open VS Code Desktop”
- 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.
- Docker is pre-installed by default, but let’s check.
And here’s a Docker CLI cheatsheet
docker --version - Install python onto your codespace and check the python version
apt update && apt install python3 python3 -V - I prefer to use
uvas the virtual environment for package management. I don’t want to install packages globally on my system.pip install uv
