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

Peter Neubauer introduces Graph databases and how they compare to RDBMS' and where they stand in the NOSQL-movement, followed by examples of using a graph database in Java with Neo4j.

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.

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.

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 ...

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

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 ...

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 ...

The modern sense of NoSQL, which dates from 2009, refers to databases that are not built on relational tables, unlike SQL databases. Often, NoSQL databases boast better design flexibility ...

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