NoSQL Essentials – Part 5 – Graph databases


So far I’ve covered key-value, document and column-family databases. All that’s left now are graph databases. However surprisingly, they’re nothing like what I’ve covered so far. They’re used in completely different scenarios to other NoSQL stores and are also structured like no other database out there.

When thinking about graph databases you can almost forget about everything you know about NoSQL. You can forget about tables, columns, rows, aggregates. Instead you need to know about entities aka nodes, edges aka relationships and properties. Common graph databases include Neo4J, Infinite Graph, OrientDB, FlockDB. In this article I am going to be focusing on the most popular of the bunch, Neo4J.

Graph database are commonly used in the social scene. Their main goal is to map huge amounts of data and to find patterns in data. For example this could be to find people you may know based on who your friends know or to finds bands you might like, based on who your friends like.

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NoSQL Essentials – Part 1 – Overview

NoSQL Databases

NoSQL was first used in the 1990’s as the name of a file based database, which used SQL, however this is not what we class as NoSQL today. NoSQL got coined again in 2009 by a new wave of databases, aiming to improve on the short comings of relational databases. In the last 2 years, NoSQL has started gaining huge interest throughout the compsci community. Here I hope to teach the core elements of NoSQL, as I know them.

Before I make a fool of myself, I would like to point out, I am far from being experienced with NoSQL databases. The below is almost a scrapbook, for my thoughts on what NoSQL is, why it’s useful, how to use it and things to be aware of. The majority of my knowledge thus far, has come from reading NoSQL Distilled by Pramod J. Sadalage and Martin Fowler. It is a great book and if you find this article useful and interesting, I would seriously recommend the investment.

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