Naive question: What does analyzing big data sets get you that sampling doesn't?
It really depends on what you want to do. If you want to compute a statistic over a large amount of data or build a predictive model, some sort of sampling may create virtually identical results with orders of magnitude less time/memory. In modeling, sampling away certain classes may actually be essential for algorithmic stability.
Sometimes sampling is wrong. If you have to create statistics over your entire dataset (let's say, number of transactions aggregated by user), sampling may not be worth the loss of precision, and it won't really save you much time either. In general, you never want to sample away data in a way that the confidence in the statistic you are calculating becomes low.
Now, whether or not the inferential premises of statistics hold up on website data (and population data) that typically is neither random nor representative, that's another story.
I definitely think Clojure has a future in big data - the repl, immutability and the strong links to the Java ecosystem make it an obvious choice for developing interactive (in so far as this is possible) big data applications.
Also, it's fun to code, which should never be discounted as a reason for a language to succeed.
Suppose you are interested in user behavior. You want sample(group_by(user_ident, all_user_events)), not group_by(user_ident, sample(all_user_events)). This involves running a group_by on the full data set.
In short, not all problems are big data problems.