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.

Apache Cassandra, originally developed at Facebook and later released as an open source project, is an example of a wide column store NoSQL database system. Finally, we have the graph database which ...

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

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 ...

Instead, there are four popular types of NoSQL database offerings: document stores, column stores, key/value pairs, and graph databases. A document store manages and stores data at the document level.

Extending the success of NoSQL databases and big data solutions, architects are now realizing that a new type of approach to working with data, be it a graph database or a graphing engine, can help ...

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.

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

A graph database is a single-purpose, specialized platform for building and manipulating graphs. Another often used word for a graph database is graph analytics, which refers to the process of ...

Like other NoSQL databases, a graph database is schema-less. Thus, in terms of performance and flexibility, graph databases hew closer to document databases or key-value stores than they do ...