We actually developed this feature (we call it recipe) 2 weeks ago and we're working on the documentation at the moment. Since we focus on product data, the first recipe is written for Segment Warehouse.
When users connect to their Segment warehouse, we automatically install this recipe, they can fork our Segment recipe to build their models on top of our base recipe. Thanks to DBT, we support some advanced features such as incremental materialization and since our focus is on product data, we have embedded features such as funnel & retention & segmentation.
Our product is not feature-complete compared to Looker but we're implementing features & working on stability for the last few months. One of our team members is working on automatic LookML converter to Rakam Recipe so that we can have more coverage of LookML. We will definitely focus more on Looker use-cases after this acquisition. :) Would love to talk if anyone is interested! (email is in my bio)
That's correct but we're not an ETL engine. We use DBT for the data transformation and you basically define what the measures/dimensions/filters are in this template so that your non-technical people run ad-hoc queries without any need to write SQL queries.
