What is a Graph?

A graph is composed of two elements: a node and a relationship.


Each node represents an entity (a person, place, thing, category or other piece of data), and each relationship represents how two nodes are associated. This general-purpose structure allows you to model all kinds of scenarios – from a system of roads, to a network of devices, to a population’s medical history or anything else defined by relationships.

What is a Graph Database?

A graph database is an online database management system with Create, Read, Update and Delete (CRUD) operations working on a graph data model.


Unlike other databases, relationships take first priority in graph databases. This means your application doesn’t have to infer data connections using things like foreign keys or out-of-band processing, such as MapReduce.

The data model for a graph database is also significantly simpler and more expressive than those of relational or other NoSQL databases.

Graph databases are built for use with transactional (OLTP) systems and are engineered with transactional integrity and operational availability in mind.

There are two important properties of graph database technologies:
 

icon-graph-storage Graph Storage

Some graph databases use native graph storage that is specifically designed to store and manage graphs, while others use relational or object-oriented databases instead. Non-native storage is often much more latent.

icon-graph-engine Graph Processing Engine

Native graph processing (a.k.a. “index-free adjacency”) is the most efficient means of processing graph data since connected nodes physically “point” to each other in the database. Non-native graph processing uses other means to process CRUD operations.




Graph databases that use native graph storage and native graph processing perform and scale better than their non-native counterparts. 
Learn More about Graph Databases →




Why does My Enterprise Need a Graph Database?


Today’s CIOs and CTOs don’t just need to manage larger volumes of data – they need to generate insight from their existing data. In this case, the relationships between data points matter more than the individual points themselves.

In order to leverage data relationships, organizations need a database technology that stores relationship information as a first-class entity. That technology is a graph database.

Ironically, legacy relational database management systems (RDBMS) are poor at handling data relationships. Their rigid schemas make it difficult to add different connections or adapt to new business requirements.

Not only do graph databases effectively store data relationships; they’re also flexible when expanding a data model or conforming to changing business needs.