Since Meltano is now aimed at more technical users/engineers, does anyone think it could make sense for the explore page to eventually have an actual SQL editor rather than dynamically generating the SQL based on the columns/aggregations you've clicked on? Also, this raises the question of whether the current system of having to build LookML-esque model files and topic/table files is still necessary. At least being able to automatically infer these based on the Singer schema would save some time when building new extractors

@al_whatmough That's a good question! The way I see it, we are explicitly targeting more technical users (data engineers) with our ELT functionality, but that targeting does not necessarily extend to our Explore/Analyze functionality, which is explicitly out of focus for the moment. Once it comes into focus again, I expect the target audience for that part of the Meltano puzzle to still be less technical users looking for insights, since Meltano's end-to-end vision aims to serve an entire data team (or really: a company's entire data strategy), and while the people at the start of the data lifecycle (where ELT takes place) are highly technical, those at the end usually are not, or less so. For those users, being able to build reports and dashboards on top of model files and topic/table files written by their more technical team members would still be essential. That means that I don't think we should be changing Meltano's Explore/Analyze functionality now to be more catered to technical users, because that's not who that part of the app is meant for. If you just want to run SQL queries against your loaded and transformed data, there are plenty of other tools that can help you with that, and building generic SQL exploration tooling into Meltano seems like it would be a distraction, since it helps the technical data engineer, but wouldn't actually help the data team or its less technical business end-users. What do you think about that?