This is where a good analytical (as opposed to operational) data model comes into play. Something less normalized that is intuitive and efficient to use.
You can also store every event as a row and pull it out through Hadoop to process (as another commenter mentioned) but you're going to get huge performance bottlenecks just in extracting the data from the database, serializing it, transferring to another server for processing, and then deserializing it. Not to mention that MapReduce is batch-oriented so it's not going to be real-time.
If you want to know what users who did X and later did Y, couldn't you select all of the users who did X in a subquery and then find out how many of those user_id's match user_id's of people who did Y, where the timestamp on Y is between the X's timestamp and X's timestamp + some_predetermined_amount_of_time?
I am out of my depth, but isn't there some database best practice for tracking user session start and stop times?
Edit: I wrote this comment before I saw a different comment of yours above, which I think answers my question.