tunel VS sparkmagic

This weekend I've started to make tiny progress on my library "tunel," which means as it sounds - an elegant derivation of "tunneL' so you can imagine we are doing lots of tricks with ssh! It currently offers (and this will be extended) developer interfaces for HPC! -> https://github.com/vsoch/tunel. So far I have a few apps ready to go for standard slurm and/or singularity via sockets, and I should be adding an HTCondor launcher next. After that I plan to develop "app templates" so anyone can easily make an app that will easily run via HPC. If anyone knows of an existing tool for some visualization / monitoring or other that they would like me to investigate as an app, please let me know!

Please file an issue at https://github.com/jupyter-incubator/sparkmagic

I maintain several open source projects, most notably:

Sparkmagic (https://github.com/jupyter-incubator/sparkmagic)

Sparkmagic provides jupyter magics and kernels for working with remote Spark clusters. It's used by thousands of developers and companies like Pinterest, Amazon, more!

I've been maintaining for the past few years and would love help!

KSOPS (https://github.com/viaduct-ai/kustomize-sops)

KSOPS, or kustomize-SOPS, is a kustomize KRM exec plugin for SOPS encrypted resources. KSOPS can be used to decrypt any Kubernetes resource, but is most commonly used to decrypt encrypted Kubernetes Secrets and ConfigMaps. As a kustomize plugin, KSOPS allows you to manage, build, and apply encrypted manifests the same way you manage the rest of your Kubernetes manifests.

KSOPS is the most popular kustomize plugin and I'd love help maintaining and improving it from out GitOps fanatics.

Things get a bit more complicated on interactive sessions. We've created Sparkmagic compatible REST API so that Sparkmagic kernel could communicate with Lighter the same way as it does with Apache Livy. When a user creates an interactive session Lighter server submits a custom PySpark application which contains an infinite loop which constantly checks for new commands to be executed. Each Sparkmagic command is saved on Java collection, retrieved by the PySpark application through Py4J Gateway and executed.

Jupyter would be even better if it supported the seamless combination of Python and SQL code cells.

My notebook code typically involves a data prep stage with querying a SQL database, then downloading into Python for more complex analysis, ML modelling, integration with external data sources, etc. So the notebook has a Python kernel with SQL usually as embedded """-quoted strings.

Does anyone have a solution to treating selected code cells as SQL - with SQL highlighting and tooltips - exposed as string variables to the Python code?

Sparkmagic [1] does part of this for Python/SQL/Spark interoperability, but as far as I recall, doesn't support SQL syntax highlighting.

[1] https://github.com/jupyter-incubator/sparkmagic