Graph databases, technically NoSQL databases, are perfect for social networking analytics where relationships bind multiple entities across a messy pattern that’s hard to break into structured traditional RDBMS or other database forms.
Structure of graph databases Graph databases work on graph theory – breaking information into nodes, edges and key-value properties. Nodes represent entities such as people, businesses, accounts, or other tangible forms. Properties describe the node in multiple ways that would be relevant to the database accessing it. Edges describe the relationship between nodes and contain the most information. Nodes connect to properties or other nodes to form complex patterns of interrelationships. For example, A is B’s spouse who is C’s brother whose wife D lives at X located in Y. This level of separation is best handled by graph databases.
This database model supports faster retrieval of associative data sets as it relies on a schema-less, bottom up model that’s ideal for capturing ad-hoc and rapidly changing data.
Market demand for graph databases
With more enterprises moving towards social enterprise applications, graph databases are turning into strategic assets that can improve target audience identification, sales, CRM and every other business function that ties into the network. Moreover, database graphs are very scalable and offer high performance in querying data from heavily interconnected data stores. These qualities help improve predictive analysis, process management, and decision making via business intelligence.
Emil Eifrem, CEO of Neo Technology talks about the relevance and increasing adoption of graph databases in an article: According to former Forrester analyst, James Kobielus, the market for graph databases will boom in 2012 as companies everywhere adopt them for social media analytics. Social graph analysis, although not a brand-new field, will become one of the most prestigious specialties in the data science arena.
Limitations of graph databases
According to analyst Philip Howard of Bloor Research, graph database is a ‘minority sport’ despite increasing adoption. One reason is that traditional RDBMS still works well for manyenterprise applications that have their data stored in massive clusters and schemas. Graph databases are only helpful for applications that generate data associated across six or seven degrees of separation.
Secondly, graph databases cannot be implemented across a low-cost cluster but have to run on a single machine for optimum performance. Another reason he cited against graph databases is that you have to either write your own queries in Java or some other language, or use SPARQL, Cypher or other languages specifically designed for graph databases. Either way, you need programming skills. Visualization tools are available for graph databases but they’re not greatly usable.
The leader of RDBMS, Oracle, recognizing the rising demand for graph databases has renamed its product Oracle Spatial to Oracle Spatial and Graph to highlight its graph capabilities. IBM too has announced the DB2 10.1 NoSQL Graph data store, an RDF graph capability. Other prominent players in the graph database market are Neo4J (an open-source/commercial (GPLv3 community edition, AGPLv3 advanced and enterprise edition) graph database), GiraffeDB (powerful graph database system for the .NET framework 4.0), DEX, Bigdata and more.
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