I use SPARQL a lot although not Fuseki. I really like the flexibility it gives in the schema (flexible not less schema). As well as that one query language can be used on radically different implementations i.e. I don't need to change my datamodel or queries to try different storage models.
Although we also use BerkleyDB/je+lucene for indexing as well as a number of existing relational databases. Yet, considering the youth of the SPARQL eco system (1.1 of the standard is only out since the beginning of this year) there is some fantastic performance possible for both hard and easy queries. I think it will be a bit like Java, not pretty, but fast enough and extremely robust in the long run. With a similar marketing pitch "Query Once Store Anywhere".
I also evaluated MongoDB, and I understand the value of a document store. I just don't think that MongoDB is a good document store, imho it just a slow /dev/null.
The title of this article should be honestly changed as it does not do mongodb justice, there are a lot of uses for it but relational is not one. Regarding the TV example, this is a classic relational solution and i enjoyed this exact example in a pycon tut,SQL for Python Developers - http://www.youtube.com/watch?feature=player_embedded&v=Thd8y...
I see a lot of people thinking MVP ---> schemaless to save time -----> mongodb but you will always need a schema unless you are just dumping a list of stuff. I would like to say that another cool solution is an RDF data store, i have been using Fuseki with SPARQL.