frameless VS azure-kusto-spark

I don't see how Spark is any "less controversial" when the Spark Delay instance for cats-effect takes an entire SparkSession implicitly.

To add to this I lately have used Spark with frameless for compile time safety and it's an interesting library that works well with Spark.

Frameless is a nice way to grab some type safety back from Spark, and features opt-in Cats integration.

Btw, are you familiar with the Frameless project (https://github.com/typelevel/frameless)?

I've looked at Frameless a bit and actually have an open issue on that repo. A lot of my Spark programming involves adding columns and it seems like Frameless requires a new case class every time a column is added, so it didn't seem practical for my workflows.

Valid point! Have you seen the withColumnTupled API? It returns a typed tuple instead. This seems to satisfy your use case - the dataset preserves its type and doesn't require a new case class. This is kind of what you're suggesting but without case class generation. Though not sure whether attribute labels (names) are preserved in this case. It's also unclear whether this is good enough for wide tables.

I recommend using Frameless, which includes a Cats module. In general, I would encourage you to master “purely” functional programming first, because it’s foundational. Spark is a very specific technology, and probably not even the best in that class today—I would be very careful about trying to build a career around it.

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