Over time, most likely, graph databases will become as commonplace as relational databases are today. Allied to this is the rise of graph data query languages like Cypher, which was recently made ...

The foundation of graph databases actually predates the relational model. Early enterprise stalwarts such as IBM’s IMS employed hierarchical structures called B-trees that mimicked the ...

Graph technology is well on its way from a fringe domain to going mainstream. We take a look at the state of the union in graph, featuring Neo4j's latest release and insights as well as data and ...

Key-value, document-oriented, column family, graph, relational… Today we seem to have as many kinds of databases as there are kinds of data. While this may make choosing a database harder, it ...

While relational databases remained strong, the 2000s saw the emergence of XML databases, and NoSQL, the idea that databases didn't need to be structured in a purely tabular form, began to get hot.

You can think of a graph database as a set of interconnected circles (nodes) and each node represents a person, a product, a place or ‘thing’ that we want to build into our data universe.

Our early implementation of a graph database (back in 2000-2003) were non-native with a graph API atop a relational database. When our queries involved around three levels of depth or more they ...

Graph-relational database developer EdgeDB Inc. is gearing up for prime time after closing on a $15 million early-stage round of funding ahead of its official launch early next year.

Like other graph database makers, vesoft has its own variant of a graph query language, called nGQL, which is SQL-like with graph principles added to it. (There is not, as yet, a standard GQL as there ...

Graph databases offer a more efficient way to model relationships and networks than relational (SQL) databases or other kinds of NoSQL databases (document, wide column, and so on).