It's really the only reason to use Spark in the first place, because you're doing non-local level processing - when I was throwing 100TB at the problem it made more sense than most of the data science tasks I see it used for.
We normally still would only need to process say, the last 10 days of user data to get decent recommendations, but occasionally it would make sense for processes running over the entire dataset.
Also this isn't that large when you consider binary artifacts (say, healthcare imaging) being stored in a database, which pretty sure that's what a lot of electronic healthcare record systems do.
A random company I bumped into has a 40TB OLTP database to this effect.