Shiny vs Jupyter comparison

• Pros: While still being relatively new for python, Shiny for R has been around for a long time, so that a lot of experiences on that could be put into Shiny for python. Sharing similar concepts should also help R users to migrate. The core of shiny is a reactive programming engine, trying to reduce the required computations as much as possible.
• Cons: Shiny uses Bootstrap as its framework for layout and styling - this means you must have understood the concepts of Bootstrap in order to change the UI.
• Use (Shiny) if: You don't have time for web development but want to expose your python code as fast as possible.
• Alternatives: Dash. Panel.

• Pros: The most used and well known notebook tool in the python ecosystem. Every python developer will likely know about and how to use Jupyter. Many projects use a jupyter notebook for demoing. Jupyter is omnipresent in the python ecosystem. There are dozens of integrations with other programming languages as well great charting integration. Prototyping with Jupyter is as fast as it gets. Anything is at the choice of the developer, there is no limitation on charting libraries or SDKs being used. As anything can be used, it is also easy to use machine learning libraries and presentation tools on top of your data.
• Cons: Not as eye-catching as other solutions. The stored data is not really easy to digest in versioning systems, which makes merging changes hard when several people work on the same notebook. Notebooks can contain sensitive information, which requires the author to ensure to read secrets from another location. It's also harder to create a web application out of a jupyter notebook than with other dashboarding tools.
• Use (Jupyter) if: you need a tool that everyone knows in the python space, Jupyter is a good idea. Same for presentations and demos that many people in your teams should understand easily, as the entry barrier is low.
• Alternatives: Take a look at Google Colab, Apache Zeppelin, Spyder, Voila or Panel.

Firebolt integration examples:
https://github.com/spinscale/firebolt-jupyter-demo
‍https://github.com/firebolt-db/firebolt-python-sdk/blob/main/examples/management.ipynb
https://github.com/firebolt-db/firebolt-python-sdk/blob/main/examples/management.ipynb