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The Rise of Graph Databases

Written by: David Fralinger

We’re living in a time of immense change. In fact, some have described the modern era as the “Fourth Industrial Revolution.” There’s no doubt that information technology has connected us more than ever before, and we are just beginning to understand the full economic and cultural impacts of the digital era.

In reality, thinking about the digital revolution as a fourth industrial revolution is somewhat misleading. While previous industrial revolutions have been heralded by advancements in manufacturing, the current economy has seen us shift toward producing products that do not exist in the physical world. New software, apps, online content and digital environments are now more profitable than most physical products.

One of the most exciting aspects of this brave new economy is the rise of Big Data. Data is quickly becoming a valuable commodity, no different than access to an oil well or natural resource. And just like a natural resource, data has to be properly mined, refined and analyzed before it can be used effectively. The rise of big data has led to an increased interest in fields related to data science.

One trend in the realm of data science is a reliance on graph databases. Graph databases represent data as a combination of “nodes” and “edges.” Nodes represent items (people, businesses, etc.) and edges represent the relationships between nodes. Each node can be assigned certain “properties.” Taken together, the nodes, edges and properties present information graphically, as seen below.

The Rise of Graph DatabasesSource: https://en.wikipedia.org/wiki/Graph_database#/media/File:GraphDatabase_PropertyGraph.png

Graph databases are a powerful tool for interpreting relational data. One obvious application is the analysis of social media data. In this example, each node would represent a user, properties would describe the user (age, gender, etc) and the edges would stand for relationships like “friends with” or “follows.”

It’s interesting to think about how graph databases may be used and interpreted by artificial intelligence, because in many ways graph databases mirror the human brain. Although we are still learning about how the brain stores and processes information, we already understand that our brains are made up of neurons connected by intricate networks of axons and dendrite. At least in structure, this highly resembles the nodes and edges of a graph database. The future of artificial intelligence may involve “teaching” a computer program to make these connections on its own, without human input.

Data analysis is rapidly emerging as an important sector in our new economy. Understanding the basics of how data is stored and analyzed is crucial for today’s business leaders. As the fields of data science race forward, a strong comprehension of current trends will only become more important.

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