It's a goddamn madhouse.
Forgive me if I come across as combative, but I don't understand generic appeals to things like a language being rigorous. Rigorous to what end? What problem is it solving where that is required? If you know something specific in this domain where that level of rigor is needed, why not share what it is?
There are a lot of problems in the analytics space (and a lot of opportunity for new practices, tools, and businesses), but I would argue that at the end of the day the primary issue is whether or not data producers choose to model data such that it is legible outside of the system that produced it much more than it is about any particular language or methodology.
Having well modeled data that matches the business domain is a massive (2-10x) productivity boost for most business analysis.
And what value does it add?
A vast majority of companies are working with < 1TB of data that sits neatly in a single cloud database. Python and tools like dbt are fantastic for a huge class of problems without compromising workflow velocity, and pushing transformations into SQL removes most Python-bound performance constraints.
Changing singer.io to require Thrift or protobuf schemas isn't going to add the value you think it is. How data is shuffled between systems is considerably less important and time consuming than figuring out how to put that data to work.
It would only help once you start shuttling around terabytes.
It’s a good tool (I use it), but the concerns GP is raising are very much its weaknesses.
It uses Java for its connectors, and looks great but has issues importing a massive dataset into S3 as there is a chunk limit of 10k, and each chunk size is 5mb :).