Data-hungry AI applications are fed complex information, and that's where graph databases and knowledge graphs play a crucial role.

Graph databases facilitate discovery and analysis closely connected facts. This post is one of a series that introduces the fundamentals of NOSQL databases, and their role in Big Data Analytics.

Emil Eifrem overviews the trends leading to NOSQL, and four emerging NOSQL solutions. He also explains the internals of a graph database and an example of using Neo4j – a graph DB - in production.

At the high end of the complexity spectrum for NoSQL database lies the graph database, which are highly specialized data stores used for storing linked data. Instead of storing data in rows/columns or ...

The evolving landscape of NoSQL databases and NoSQL database management systems (NoSQL DBMS) has everything to do with Big Data analytics.

Today Neo Technology, makers of the NoSQL graph database Neo4j announced that it has raised a $11 million Series B led by Sunstone Capital with participation from previous investors such as ...

Around the same time as scale-out NoSQL, graph databases emerged. Many things are not “relational” per se, or not based on set theory and relational algebra, but instead on parent-child or ...

NoSQL and NewSQL databases are popular solutions in the data management space. At VoltDB, we’re sometimes asked to clarify the difference between the two approaches. Here’s what you need to know if ...

In order to understand who a player’s friends are in online Bingo, casino and slot games, Gamesys stepped out of its DB2 comfort zone to implement a NoSQL-based graph database, Neo4j. When Big Data ...

A NoSQL database has flexible data requirements, making it a better fit for applications that will evolve over time than an SQL database.