A graph database is a dynamic database management system uniquely structured to manage complex and interconnected data. Unlike traditional databases organized in rows and columns, graph databases ...

Graph databases have moved from a topic of academic study into the mainstream of information technology in the last few years. ... The 2000s saw the emergence of XML databases, ...

Graph database vs. relational database. In a traditional relational or SQL database, the data is organized into tables. Each table records data in a specific format with a fixed number of columns ...

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.

Graph databases have been around in one form or another since the early oughts, ... with other integrated database systems, such as JSON or XML stores, or with search engines.

Graph databases have matured into mainstream information technology and delivered value to organizations in a wide range of applications. Here's how you can expect them to evolve.

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.

All databases need a way to talk with their clients, and the query languages they speak define what the database can do. Good graph database query languages unlock the power of graph databases by ...

It's a misnomer, and as Jain pointed out, "GraphQL is as much a graph query language, as relational databases are about relationships." In other words, not very much, and this is by design.

Neo4j is the most popular graph database on the market these days. While graph databases are part of the NoSQL movement, they really solve different problems than, say, Couchbase or MongoDB.