etl-markup-toolkit VS sparkmagic

I have a side project (https://github.com/leozqin/etl-markup-toolkit, if you're interested) that takes transformations as yaml files and outputs step-level logs about each step of the transformation. I've always felt that both artifacts could made searchable using an ELK stack or something... Do you have similar artifacts? Or perhaps there's a way to turn SQL into a structured or semi-structured form to aid in searchability

My current side project (https://github.com/leozqin/etl-markup-toolkit) is a low code way to express transformations as configuration and run it on pyspark. It also supports abstraction so you can call business logic like a function and has step-level reporting you can load into a metadata table. Usual disclaimers about OSS apply, although I'm happy to answer questions and take contributions.

Not sure if it meets your exact requirements, but I maintain an open source project that enables spark transformations as configuration, and part of that capability is reporting, including logging of columns in vs columns out, row counts, etc... It's very early stage but perhaps could be useful - https://github.com/leozqin/etl-markup-toolkit

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