People too often forget about graph databases when talking about NoSQL solutions. Graph databases offer an interesting and elegant alternative to relational dat

People too often forget about graph databases when talking about NoSQL solutions. Graph databases offer an interesting and elegant alternative to relational databases and I could definitely see a startup decide to use this kind of technology. As far as I know, most graph databases support transactions and offer great scalability. Such databases are also schema-less and can be queried with Gremlin, a powerful graph traversal language (see www.tinkerpop.com). With respect to scalability and transactions, Titan (http://thinkaurelius.com/ http://thinkaurelius.com/) looks very promising: it supports various backends for storage (Cassandra, HBase, etc.) and indexing (currently Elastic Search and Lucene). Graph analytics can be done via Faunus (http://thinkaurelius.github.io/faunus/ http://thinkaurelius.github.io/faunus/), backed by Hadoop. There are other vendors out there (Neo4J, OrientDB, etc.) which offer interesting solutions worth looking at - I'm just a bit less familiar with them. The major downside I see with graph databases is that most of them are fairly recent and their ecosystem is tiny (though growing). Should a startup venture on such young technologies, or stick to mature and battle-tested solutions (ie. relational databases)? Could startups use this kind of graph "NoSQL" databases? I don't see why not. If your startup is some kind of social network, graph databases are certainly an option worth considering. If I were to create a startup, I'd hardly use a document database like MongoDB but I will really consider using a graph database. In the end, it's all about having the right tool in hand, and knowing how to assert what is "right" for you.