So this is a good question - the answer is that you're comparing apples to oranges. This is all about in-situ data processing, which basically means that you can run reasonably efficient, optimized queries on just regular old files that you have lying around, without having to do anything special. This is as opposed to a situation where you've spent a ton of time, effort and expense actually ingesting the data into whatever data system you're running (in this case redshift). So it's usually said that these systems are good for ad-hoc queries, i.e. one-off queries on files and datasets that you want to explore more, but it doesn't make sense to invest the hours upon hours of waiting and storage / cpu resources to bring them into your database just to make a few queries.