But for your company's analytics, you want to build a suite of well defined, reliable and documented datasets that power you dashboards and your regular analytics.
Another way to say it is that with NoETL, you write one query to answer one complex question. With ETL (actually ELT), you define core datasets with which you can answer many questions.
2. In terms of adoption, products like Dataform are helpful from the day you start having data in your data warehouse and have a full time analyst. It's basically giving superpowers to your analysts.
Can you elaborate this?
NoETL targets semi-structured and schema-less data models prevalent in NoSQL/HTAP data stores, so if well defined relates to well defined schemas, it maybe doesn't apply here.
As far as reliability (aka reproducibility) and documentation is concerned, I guess it's more about building tools that promote sound engineering processes like version control, modules (aka reusable snippets), documentation (could be Jupyter notebooks, etc.) and these tools could be built for NoETL systems too.