I have written Julia scripts and accessed different Julia libraries without issue in Jupyter. I have not noticed any limitations on its use or any performance degradation. I imagine some aspects of Julia that are very screen dependent (such as using the Julia webstack to build a website) may be hampered by conflicting uses of the same concept.

I have repeatedly seen updates being run when I am attempting to run a Julia script, as shown in the following screenshot. I am not sure why they decided to always update the underlying tool rather than use what is in play and have the user specify whether to update libraries:

I have also noticed that once a Julia Notebook is opened, even though I have closed the page, it will still display Running on the home page. I don't recall seeing this behavior with the other script languages that are available.

Another issue has been trying to use a secured package in my script, for example, plotly. It appears to be a clean process to get credentials, but using the prescribed methods for passing your credentials to plotly does not work under Windows. I am hesitant to provide examples that do not work in both environments.

Further interactions with Windows are also limited, for example, attempting to access environment variables by calls to standard C libraries that are not normally present on a Windows installation.

I have another issue with Julia itself, regardless of running under Jupyter or not. When using a package, it will complain about features that are used in the package that have been deprecated or improved. As a user of the package, I have no control over this behavior, so it does not help me in my work.

Lastly, running some of these scripts takes several minutes. The scripts used are small. It seems to take a long time for the Julia kernel to start.