Tuesday, January 7, 2014

Getting Started with Neo4j 2.0

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With the recent release of Neo4j 2.0 and it’s a great time to get familiar with graphs and graph databases. Neo4j is quite different from relational databases, and it’s also quite different from most of the other NoSQL databases. The reason for its differences is that it addresses more complex challenges with interconnected (joined-up) data. This makes Neo4j ideal for high fidelity modelling and high-performance querying of rich, real-world domains.

In this article we’ll work through some retail recommendations problems with Neo4j and see how we can use graphs to store and query complex interconnected data for fun and profit.

What’s Neo4j?

Aside from being strangely named software, Neo4j is an enterprise-grade, open source, ACID transactional, graph database. First deployed in 2003, Neo4j is by far the leader in the graph database world with a vibrant community, active development, and huge numbers of successful commercial and open source deployments.

As a graph database, data model is radically different from relational databases, and although it is considered to be part of the NoSQL space, its data model is also radically different from denormalised aggregate stores. In Neo4j, data is stored in nodes that hold key-value pairs. In turn, those nodes are linked through relationships. There can be many relationships between nodes, and each of them is named and directed (always with a start and end node). Although these are simple tools to understand, they can be used to create very expressive data models where the semantic glue between entities (nodes) is a first-class citizen like the entities themselves.

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