You basically have two options in how to store RDF data in wide-column databases like HBase and Cassandra: the resource-centric approach and the statement-centric approach.
In the statement-oriented approach, each RDF statement corresponds to a row key (for instance, a UUID) and contains subject, predicate and object columns. In Cassandra, each of these would be supercolumns that would then contain subcolumns such as type and value, to differentiate between RDF literals, blank nodes and URIs. If you needed to support named graphs, each row could also have a context column that would contain a list of the named graphs that the statement was part of.
In view of the previous considerations, the resource-oriented approach is generally a better natural fit for storing RDF data in wide-column databases. In this approach, each RDF subject/resource corresponds to a row key, and each RDF predicate/property corresponds to a column or supercolumn. Keyspaces can be used to represent RDF repositories, and column families can be used to represent named graphs.
Leaving aside Cassandra or HBase or Riak or the over a dozen existing solutions, you can always build a triple store on MongoDB.
Original title and link: Storing RDF in Wide-Column Databases (Cassandra, HBase) (NoSQL databases © myNoSQL)