A graph database is a dynamic database management system uniquely structured to manage complex and interconnected data.

The gist of what's going on is that we have a war for graph database domination, and query language is a key battle to be won. SQL did not arrive at universal adoption overnight.

A new generation of graph databases has taken hold, and a generation of query languages has arrived alongside them. The assorted graph database query languages include the likes of Gremlin, Cypher ...

It does, however, have its own Diffbot Query Language (DQL). DQL looks pretty simple, if you are familiar with query languages. But, then again, if you are familiar with query languages, why would ...

Here’s a look at a few of them particular. Key Benefits of a Graph Database . Better, Faster Queries and Analytics: Graph databases offer superior performance for querying related data, big or small.

Graph databases excel for apps that explore many-to-many relationships, such as recommendation systems. Let’s look at an example Jeff Carpenter is a technical evangelist at DataStax. There has ...

Newsela uses Dgraph, a “graph database,” to speed the delivery of content while making it easier for the company’s developers to create new features.

It’s also given space for competitors like TigerGraph, which an upstart developer of a fully distributed property graph database, to make some hay at Neo4j’s expense. That meant that Webber needed to ...

It’s open source, uses an SQL-like query language, follows the Property Graph Data Model and supports indexes, UNIQUE constraints and full ACID rules. Let’s walk through the installation of ...

The graph database was originally designed to store networks — that is, the connections between several elements such as people, places they might visit, or the things they might use.