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Graph databases address one of the great macroscopic business trends of today: leveraging
complex and dynamic relationships in highly connected data to generate insight
and competitive advantage. Whether we want to understand relationships between
customers, elements in a telephone or data center network, entertainment producers
and consumers, or genes and proteins, the ability to understand and analyze vast graphs
of highly connected data will be key in determining which companies outperform their
competitors over the coming decade.
For data of any significant size or value, graph databases are the best way to represent
and query connected data. Connected data is data whose interpretation and value requires
us first to understand the ways in which its constituent elements are related. More
often than not, to generate this understanding, we need to name and qualify the connections
between things.
Although large corporates realized this some time ago and began creating their own
proprietary graph processing technologies, we’re now in an era where that technology
has rapidly become democratized. Today, general-purpose graph databases are a reality,
enabling mainstream users to experience the benefits of connected data without having
to invest in building their own graph infrastructure.
What’s remarkable about this renaissance of graph data and graph thinking is that graph
theory itself is not new. Graph theory was pioneered by Euler in the 18th century, and
has been actively researched and improved by mathematicians, sociologists, anthropologists,
and others ever since. However, it is only in the past few years that graph
theory and graph thinking have been applied to information management. In that time,
graph databases have helped solve important problems in the areas of social networking,
master data management, geospatial, recommendations, and more. This increased focus
on graph databases is driven by twin forces: by the massive commercial success of
companies such as Facebook, Google, and Twitter, all of whom have centered their
business models around their own proprietary graph technologies; and by the introduction
of general-purpose graph databases into the technology landscape.
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