There’s no denying it: Graph databases are hot. According to DB-Engines.com, graph databases have outgrown every other type of database in popularity since 2013, and not by a small margin either. It’s clear that developers, data scientists, and IT pros are just beginning to explore the potential of graph databases to solve new classes of big data analytic and transaction challenges.
Here are four reasons why graph databases are surging in popularity now:
1. It’s All About Relationships
As organizations accumulate large stockpiles of data, it’s only natural to want to know what’s in it. There are many ways to query data, but one of the most interesting approaches involves seeing how various pieces of data are connected, and what relationships exist among the data. That’s one of the capabilities that graph databases excel at.
Graph databases are useful for storing data that refers to things that are naturally connected in the real world, such as groups of people on social networks, devices on a wide area network, vehicles on a road network, or even chemical structures in families of organic compounds. Storing individual pieces of data as “nodes” in a graph, which are connected to each other via “edges,” enables organizations to quickly gain new views on data in ways that would be very difficult to do using relational data structures.
2. Speedy Performance
One of the main reasons developers are choosing graph database is performance. For certain types of big data problems–particularly those that involve analyzing the relationships among millions or billions of entities–a graph database will outperform nearly every other type of out-of-the-box database in existence.
For certain types of data, like data from social networks, devices on the IoT, and other data that’s inherently of a highly connected nature—it’s tough to beat graph. That’s why many big companies—some in the business say all big companies–are investing in graph technologies.
3. Semantics Matter
You may have heard the phrase “Semantic Web” and wondered what it meant. As it turns out, it’s all about the organization of information across the Net, and making it more people-friendly, as opposed to the chaotic mishmash that has basically defined the Internet for much of its existence.
“The two nouns, buyer and book, are related together by the act of purchasing (a verb),” Bloor writes in the ebook, which was sponsored by Cray. “Note that both ‘nouns’ and ‘verbs’ can have attributes. Again, when data volumes get large, it is preferable to hold data of this kind in a graphical structure because it is semantically meaningful to do so.”
4. Graphs Make a Difference
The science and math behind graphs have been around for hundreds of years, but graph databases have only been around for about 10 years, with the biggest impact coming during the last two or three. While graph databases occupy just a slim percentage of the overall database market, the early returns on the technology are promising.
“Think of it like a huge knowledgebase about diseases and proteins and genes and everything having to do about human physiology,” Franz CEO Jans Aasman told Datanami a year ago. “We can take all these things in, and without even modifying them, load it into our system.”
BITNINE GLOBAL INC., THE COMPANY SPECIALIZING IN GRAPH DATABASE
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