By Paul Krill | InfoWorld

Interest in graph databases will continue to grow, given its ability to analyze data delivered in a non-relational format, such as social networking data

Graph databases, which store and query connected information, are starting to emerge as a way of dealing with data delivered in a non-relational format, such as social networking data.

With a graph database, the focus is on the connections between data. “You’re telling the database in advance that things are connected and how, and representing those relationships physically,” as opposed to storing them in tables and relating them through indexes, said Philip Rathle, senior director of products at graph database vendor Neo Technology, at the recent NoSQL Now conference in San Jose, Calif.

But there is still plenty of room for old-fashioned relational databases. A graph database is for special uses. “I definitely wouldn’t recommend a graph database if you have very tabular and well-structured data. Use a relational database for it,” said Emil Eifrem, Neo’s CEO. “But if you have data that is messy, that is complex, that is connected, then a graph database is vastly superior.” Eifrem cited applications such as a social network or fraud detection, where data is connected, as systems that could benefit from a graph database.

Usage of graph databases is increasing, but the technology still represents a “minority sport,” according to analyst Philip Howard of Bloor Research. “Graph databases are critical when the degree of separation [ie, I know x who knows y who is related to z who used to live in the same house as w etc.] between entities becomes too great to handle using conventional technology. Oracle or DB2, for example, can reasonably handle up to three degrees of separation but not the six or seven degrees you need in, say, telco fraud,” Howard said.

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