Excerpt of article contributed by Emil Eifrem in Fourth Source

While the formulae for love may vary based on the dating site, they are all underpinned by data. This is where graph databases make a difference, with digital leaders like Google and LinkedIn owing their breakthrough to having adopted this revolutionary approach to data very early on.

Graph databases differ from relational databases – as the vast majority of everyday business databases are called – as they specialise in identifying the relationships between multiple data points. Thus Google was able to exploit the connections in every Web document and build a connected dataset to rank search result, getting back substantially better search results: a fundamental factor in its meteoric rise.

LinkedIn, meanwhile, digitises real-life relationship networks, common business contacts and friends-of-friends in a slick way that has given it total domination of the business social market space. Those relationships can be spotted and followed by the code much more quickly than in a relational database because those ‘relationships’ (in the graph database world, ‘joins’) don’t need to be created in order to run a query. This means much improved query time, supporting speedier transactions. The graph database can also query and display a huge volume of connections between people, preferences, personal profile criteria, and so on.

And although not dealing with two human connections, managers in a very different industry to online dating – retail – should be able to see the potential in matching up prospective customers with their ‘ideal’ products or services.

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