The answer is in the term "data warehousing" -- http://en.wikipedia.org/wiki/Data_warehouse -- which has implication that you're going to be doing data mining on vast amounts of data, often historical data like logs or transaction histories.

Google has systems like this for analyzing its request logs. Think of how many HTTP requests hit Google's front-end servers per second or hour or day. Each one has a few dozen pieces of data associated with it -- URL, client IP, headers, etc. Suppose I want to make a bar chart of how many requests came from France containing a certain header, each day for the last year. The system can do this query quickly if the requests are already bucketed by time interval, organized by column, compressed, and stored so that exactly the information needed can be brought into RAM quickly.

It is a little funny, when you step back, that "storing," "archiving," and "warehousing" are different things and Amazon has services for each. Try explaining the difference between S3, RDS, EBS, Glacier, and Redshift to a layperson.

RDS has a maximum capacity of a terabyte, you'd need to shard to go beyond that.

This can scale up much more than a single RDS database since it spreads the data across multiple machines, but it's not exactly a replacement for MySQL database. It's also possible that this doesn't make use of EBS, which could make it perform more predictably and protect it from failure when EBS fails.