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    <p><strong>MongoDB</strong> </p> <p><em>Scalability:</em> Highly available and consistent but sucks at relations and many distributed writes. It's primary benefit is storing and indexing schemaless documents. Document size is capped at 4mb and indexing only makes sense for limited depth. See <a href="http://www.paperplanes.de/2010/2/25/notes_on_mongodb.html" rel="noreferrer">http://www.paperplanes.de/2010/2/25/notes_on_mongodb.html</a></p> <p><em>Best suited for:</em> Tree structures with limited depth</p> <p><em>Use Cases:</em> Diverse Type Hierarchies, Biological Systematics, Library Catalogs</p> <p><strong>Neo4j</strong></p> <p><em>Scalability:</em> Highly available but not distributed. Powerful traversal framework for high-speed traversals in the node space. Limited to graphs around several billion nodes/relationships. See <a href="http://highscalability.com/neo4j-graph-database-kicks-buttox" rel="noreferrer">http://highscalability.com/neo4j-graph-database-kicks-buttox</a></p> <p><em>Best suited for:</em> Deep graphs with unlimited depth and cyclical, weighted connections</p> <p><em>Use Cases:</em> Social Networks, Topological analysis, Semantic Web Data, Inferencing</p> <p><strong>HBase</strong></p> <p><em>Scalability:</em> Reliable, consistent storage in the petabytes and beyond. Supports very large numbers of objects with a limited set of sparse attributes. Works in tandem with Hadoop for large data processing jobs. <a href="http://www.ibm.com/developerworks/opensource/library/os-hbase/index.html" rel="noreferrer">http://www.ibm.com/developerworks/opensource/library/os-hbase/index.html</a></p> <p><em>Best suited for:</em> directed, acyclic graphs</p> <p><em>Use Cases:</em> Log analysis, Semantic Web Data, Machine Learning</p>
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