Jupyter vs Panel comparison

• 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

• Pros: Panel allows you to turn jupyter notebooks into dashboards without much additional work. Due to the omnipresence of notebooks this is an easy way to get started fast with a shared dashboard. Also there is built-in support for the many common graph libraries and widgets. You can integrate your panel app into web application frameworks like Django or FastAPI and also use built-in OAuth integration. Panel also supports caching of results on the server side to speed up rendering dashboards.
• Cons: The learning curve of Panel is slightly higher than other dashboarding tools, because of  the sheer choice of widgets and libraries that are supported and need to be learned as well. The focus of Panel is more on python and less on HTML/CSS.
• Use (Panel) if: you want to be flexible about existing widgets and libraries that are supported out of the box and want to make a Jupyter dashboard available for everyone as a web application.
• Alternatives: Dash. Jupyter.