Panel vs Dash | Python Tools Comparison

Panel vs Dash - TLDR

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.

Pros: Dash allows a very customizable experience by using React under the hood. Dash easily supports interactive visualizations. There is a huge and active community willing to help out each other.
Cons: Dash is focused around its own visualization library, and may require some extended CSS and HTML knowledge for customization. As Dash also has an Enterprise version, some functionality is reserved for Enterprise only.
Use (Dash) if: You need a high level of customization to adhere to your use-case and may need enterprise features in the future.
Alternatives: Streamlit and Shiny

You can use Firebolt with both Strealit and Dash in order to build your applications.

Include the Python SDK for Firebolt to your dependencies and analyze your data stored in Firebolt right away!