NoSQL Benchmarks NoSQL use cases NoSQL Videos NoSQL Hybrid Solutions NoSQL Presentations Big Data Hadoop MapReduce Pig Hive Flume Oozie Sqoop HDFS ZooKeeper Cascading Cascalog BigTable Cassandra HBase Hypertable Couchbase CouchDB MongoDB OrientDB RavenDB Jackrabbit Terrastore Amazon DynamoDB Redis Riak Project Voldemort Tokyo Cabinet Kyoto Cabinet memcached Amazon SimpleDB Datomic MemcacheDB M/DB GT.M Amazon Dynamo Dynomite Mnesia Yahoo! PNUTS/Sherpa Neo4j InfoGrid Sones GraphDB InfiniteGraph AllegroGraph MarkLogic Clustrix CouchDB Case Studies MongoDB Case Studies NoSQL at Adobe NoSQL at Facebook NoSQL at Twitter



Paper: The Graph Traversal Pattern

A paper on graph databases and their applicability by Marko A. Rodriguez (@twarko) and Peter Neubauer (@peterneubauer):

Graphs are a flexible modeling construct that can be used to model a domain and the indices that partition that domain into an efficient, searchable space. When the relations between the objects of the domain are seen as vertex partitions, then a graph is simply an index that relates vertices to vertices by edges. The way in which these vertices relate to each other determines which graph traversals are most efficient to execute and which problems can be solved by the graph data structure. Graph databases and the graph traversal pattern do not require a global analysis of data. For many problems, only local subsets of the graph need to be traversed to yield a solution. By structuring the graph in such a way as to minimize traversal steps, limit the use of external indices, and reduce the number of set-based operations, modelers gain great efficiency that is difficult to accomplish with other data management solutions.

This paper together with the graph theory book and the operations on graph databases series should make you realize some of the advantages of the existing graph databases.