From Computer Weekly Developer Network, excerpt of interview with Emil Eifrem, CEO of Neo Technology.

Put simply, data no longer works as part of a one-size-fits-all strategy. With the arrival of big data we are no longer talking in Mb or Gb and we’re certainly not talking about structured information.

Businesses are collecting vast streams of data about anything and everything, often without much thought on how it will be managed, analysed or even stored. Trying to push these huge, irregularly-shaped data sets into the traditional SQL model is painful.

Not Only SQL

Hence we have the ‘Not Only SQL’ movement (also known as NoSQL).

Within NoSQL we have real choice over how data is structured, each model offering various strengths and weaknesses.

Ed — exactly ! .. as recently explained on Forbes: “NoSQL is argued to be shaping our future because, as a database type, it depends on data structures that can (for certain use cases) operate faster than traditional relational databases. The NoSQL data structure taxonomy is defined by key-value stores, documents or graph databases. In other words, the database design can be structured around what can be a more custom-aligned DNA for the use case in hand.”

Eifrem continues…

Graph databases are part of this movement. Focusing on the relationships between data-points, rather than on the values themselves, graphs are perfect for those big, messy and connected data sets. This is something that SQL databases simply can’t do – at least without spending significant effort creating complicated join tables.

With the graph you can ask complex and abstract questions that look beyond the first data connection.

They can uncover patterns that are difficult to detect using traditional representations such as tables. It may be a social graph; it may be going from point A to point B; or it may be product recommendations, where you want to know what else was bought by the people who bought similar things to you.

Importantly, understanding the connections between data, and the meaning of these links, doesn’t need new data. You can pull new insights existing data, simply by reframing the problem and looking at it in a graph.

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