[cm]

It's accurate to say that we're more focused on the extraction and loading parts of ETL. In our experience, almost all useful data analysis requires data to be transformed at multiple stages - for example, once to cleanse and normalize the data, and again to aggregate it. Our goal is to leave the data in the rawest form possible without losing accuracy or precision, so that further transformation can occur, likely using SQL. We recognize this isn't perfect for every use case, but our customers love it for getting rapid access to data that would otherwise be locked away in SaaS applications and transactional databases.