For the last 30 years or more, the relational database has been the answer to most data analysis needs. In many ways it has been excellent for storing and exploring data. But it’s weak in two particular areas: complex hierarchical data and processing graphical data structures. And as the volume of complex and graphical data has grown, the shortcomings of relational databases have become more pronounced.

As data has changed, new database varieties designed to handle these “inconvenient” data structures have emerged. Of them, graph databases were specifically built to process data in a graphical manner and enable queries that traverse networks of data. These databases are complementary to relational or document databases and many have been engineered to scale out and process very large volumes of data.

We’re happy to offer a report from the graph experts at The Bloor Group, an independent research firm. In this white paper, you’ll learn:    

You’ll also get an introduction to Cray’s answer to graph analytics – the Cray® Urika®-GX platform and the Cray Graph Engine – as well as a close look at key use cases from the cybersecurity and health sciences industries.

What’s behind the need for an alternative to relational databases

Fundamental differences between relational, document and graph databases

Which business sectors are adopting graph analytics applications

The basics of how graph databases work

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