Different NoSQL platforms solve different problems. Graph databases can build and query relationships between entities with significant speed. Mongo has been mentioned a few times because it's easy to iterate development with a schema-less database, but it's also fantastic when dealing with class based structures with sparse attribute populations (e.g. a CMDB). Cassandra's notion of an always available database is a key foundational element in high-scale devops environments. Scylla offers incredibly fast transactions (they advertise 1M transactions per second per server). There are XML databases that store and query documents in ways that are easy for developers to translate (i.e. xpath). There are databases like Axibase that are built for time series data. BayesDB is easy to query for statistical inference.
It's very easy to fall into the trap of "everything is relational" or "I could do that with Postgres/Oracle/etc". There are a lot of problems that have good RDBMS centric solutions, but you don't have to look too far to find end users or developers who are unhappy with the RDBMS solutions that they work with on a daily basis.
It's very easy to fall into the trap of "everything is relational" or "I could do that with Postgres/Oracle/etc". There are a lot of problems that have good RDBMS centric solutions, but you don't have to look too far to find end users or developers who are unhappy with the RDBMS solutions that they work with on a daily basis.