Let’s talk about graph databases. How do you like graph databases? Do you use them often? What do you use your graph databases for?
What’s that? You don’t know what a graph database is? No worries! In the hodge-podge of high-tech word salad that’s flying around the Internet these days, the term doesn’t exactly stand out very often, so hey, no harm no foul.
But now you have the chance to close shut that particular little knowledge gap as we take a look at graph databases, what they are, and what they are good for.
Otherwise known as a graph-oriented database, it’s a type of NoSQL database that employs graph theory in order to store, map, and query relationships. A graph database consists of a series of nodes and edges. Nodes represent people, businesses, accounts, or organizations, while edges represent a connection, a relationship, between a pair of nodes.
Every node in the database is defined by a unique identifier (an alphanumeric or numeric string), a collection of incoming and/or outgoing edges, and a set of properties in the form of a key/value pair. An edge also has its own unique identifier and set of properties, plus a starting and/or ending place node or nodes.
Graph databases optimize highly related data, and in many cases offer greater speed and flexibility in managing said data. As the article “Graph Databases: Not Your Father’s Big Data” points out, Facebook employs graph databases, hardly surprising, considering the sheer amount of information that passes through the site.
If you want another inspiration to help create an accurate mental image, consider an assembled Tinkertoy model. For that matter, a flow-chart on a white-board works as well.
Apparently, some well-known brand names out there think that there’s something to this whole concept of graph databases. In addition to the aforementioned Facebook, LinkedIn is working on a proprietary graph database, Twitter relies on a graph database, E-Bay, Wal-Mart, and Glassdoor employ them, and a few of the biggest online dating sites in the world have switched to a graph database system.
Graph databases not only store information about individuals, it also stores the relationships between them. As a result, users can frame more exacting questions, and get the results faster. What does this benefit? Try portfolio management, fraud detection, product line management, real-time analytics, and a whole lot more.
In essence, any company that works with large data sets and wants query results as rapidly as possible can benefit from a graph database.
According to this piece in Forbes, graph databases are finding homes in far more businesses than originally expected, supplanting the traditional relational databases. The article cites the reasons for this widespread acceptance as the increasing interconnectedness of the world, the desire for greater speeds, and the tendency for change to occur quickly.
If you find yourself being constrained by the limitations of a MySQL or Oracle database, you may want to consider a change. The graph database solution is not perfect, but by all accounts, the advantages appear to outweigh the drawbacks.
Stephen Jeske is an occasional contributor to HowDoyou.com and enjoys covering issues surrounding technology and Big Data
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