Graph databases are hot. There’s no denying it. 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 five reasons why graph databases are surging in popularity now:
1. It is 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.
In some cases, a graph database can run queries that would be prohibitively expensive, or even impossible, to run on other databases. For example, a graph database that’s powering a recommendation engine can efficiently compare the properties of entities that it stores—perhaps recording the musical tastes of people who are represented as entities in a graph–and return the results within seconds, whereas it could take minutes or hours for the equivalent operation to be executed on a relational database using a table-join operation.
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.
Semantics are near and dear to what graph databases do, particularly those that adhere to the World Wide Web Consortium’s (W3C’s) Resource Description Framework (RDF) specification, such as Franz‘s AllegroGraph, GraphDB, Cray‘s Graph Engine, MarkLogic’s multi-modal database, and Cambridge Semantics Anzo Graph Query Engine. (Property graph databases, such as those from Neo4j, OrientDB, TitanDB–which Datastax is building into its enterprise Cassandra offering, which Amazon Web Services offers hooked into its DynamoDB NoSQL-as-a-service offering, and which IBM, our partnership recently offered as a standalone service on the Bluemix cloud–are the other main type of graph databases seen in the market.
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.
For example, in the field of precision medicine, Franz is working with our partnership and Hadoop distributor Cloudera and chip giant Intel to build a big data analytic platform at Montefiore Health System in New York City. The idea is to enable Montefiore to build a semantic data lake (SDL) that lets medical professionals analyze and find patterns among large amounts of data from a variety of systems, such as patient histories, genetic test results, drug interaction databases, and even external demographic data stores.
5. Ask Tough Questions
The way graph databases work melds very nicely with the type of questions that people want to ask these days, particularly when the source data sets are of a large and highly connected nature. Of course, giving answers to tough questions is ultimately the goal of many tools in the big data analytics spectrum. But graph databases are particularly well positioned to give us an advantage.
Graph databases are already helping organizations in retail, financial services, healthcare, and security fields, and with the growth of new data from the IoT, the use cases for graph are expected to soar.
[Reference] Datanami.com (https://www.datanami.com/2016/08/19/5-factors-driving-graph-database-explosion/)
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