From what I've read, I wouldn't pick MongoDB my first choice for a large data warehouse. Maybe 10gen can change that.
I believe CouchDB is a better choice for very large data sets because of its design.
+ CouchDB uses a Map Reduce design that I believe would scale better over very large data sets. + CouchDB always stores data in a consistent state on disk. You can literally pull the plug on the server at any time and the data will never be inconsistent.
MongoDB is geared for performance and is a great bridge between a relational database and a high-performance No-SQL database. But I don't recall that it's strength is handling large datasets.
Mongo is designed from the ground up to deal with large datasets. Take a look at their sharding architecture.
Arbitrarily large data is the exclusive domain of hadoop/hypertable/cassandra AFAIK atm.
Where CouchDB really falls flat is for write-heavy applications. The default configuration in CouchDB is to not reindex a view until it has been read. When a read occurs, any new data in a view that was added since the last read must be re-indexed by executing the map/reduce functions on that data. If you're writing frequently to CouchDB but not reading a lot (as in a data warehouse) the first query you run is going to be extremely slow, since it will need to run map/reduce on a lot of new data. CouchDB doesn't distribute work to multiple nodes like Hadoop, and I've found even simple reduce functions to slow down re-indexing by a factor of 10. I think CouchDB has settings now to update the index on commit, or you could always run a cron job to regularly query the view and force a reindex, but it's still going to be slow.
BigCouch (https://cloudant.com/#!/solutions/bigcouch) might be a potential choice for data warehousing, since it advertises full compatibility with the CouchDB API but offers distributed map/reduce like Hadoop/Hive/etc. I haven't used it though.
We are currently optimizing the views for cluster access, but the design goal is to offer at least the query performance CouchDB offers on small datasets, even on very large clusters.
More info: http://blog.couchbase.com/couchbase-server-2-0-tour-and-demo
the theory behind the thing is great. in reality, its buggy and not fun to work with.