There’s a reason that Neo Technologies, purveyor of graph-database Neo4j, has added some of the most well-known and established companies (Hewlett-Packard, eBay, Cisco) to its list of licensed, paying customers since 2011.
It’s closely linked to the expanding community of Neo4j users, which recently closed out 2013 with 50,000 “new instance” activations per month, a rate that is three times higher than that for 2012.
It has a good deal to do with the horizontal nature of graph databases, which are showing up in a striking number of vertical industries and expediting processes that are difficult and time-consuming for relational technologies.
Quite simply, graph databases are gaining in popularity and, thanks to the recent release of Neo4j 2.0, usability as well:
“Graph databases have traditionally been used only for social media,” said Neo Technologies CEO Emil Eifrem, who founded the company in 2000. “It turns out that this ability to process connections between data elements is a completely horizontal concern. That’s something that you need in every single industry out there.”
More Than Social Media
Although Neo4j and graph databases can accelerate numerous database processes from Business Intelligence to content management, there are presently three core horizontal areas (excluding social media, in which the relationships between people/users is readily identified and modified via graphs) in which all industries benefit from its capabilities: Master Data Management (MDM): Whether focused on customers, products, or multiple domains, MDM solutions greatly benefit from graph databases which readily map supply chains and relationships between domains to speed up data access and relevance. Software/Network and Data Center Management: Determining potential effects of network failures and various software and hardware components has never been easier than with graph databases, which can map (even visually, in the case of Neo4j 2.0) the relationship between components and greatly accelerate what is a tedious process with relational databases. Geographic Data Management: Again, the mapping potential for graph databases can expeditiously delineate routes and relationships between various points of location. Graph databases can also greatly enhance CRM, fraud detection, recommendations, resource optimizations, and other facets of the enterprise database functions. The fact that they are employable for uses other than social media should not downplay this particular application, since the technology for graph databases and NoSQL is greatly responsible for the explosion of Big Data and the accurate gauging of sentiment data via the Internet.Use Cases
According to DB Engines, a site dedicated to technology analysis, in recent months the popularity of graph databases has pushed Neo Technologies to the forefront of the NoSQL movement and exceeds that of relational SQL technologies as well. The January 2014 popularity of Neo4j, which is based partly on aggregates of website mentions, technical discussions and job offers referring to a system, exceeds the scores of all other relational graph bases combined: “The problems that Mongo solves are really important, but they’re not the problems that Neo4j solves,” Eifrem said. “The problems that we solve you can’t solve with Mongo and vice versa. The same is true for Cassandra and other databases.” After acquiring the London-based delivery service Shutl, Ebay was suddenly confronted with the task of routing, in real time, a network of carriers to deliver products to customers within 90 minutes of placing an order for its newfound Ebay Now imprint. Scaling requirements included those for consumer-to-consumer delivery and the calculation of simultaneous, multiple routes. The company replaced its legacy MySQL solution with Neo4j, which significantly enhances the speed at which routing is conducted, due to the fact that there is substantially less code required with the latter. The result is more effective code quality and a greatly reduced time to market. Within the healthcare industry, Neo4j operates as a platform which integrates various data sources, issues queries, and serves as a customer interface for San Francisco-based Zephyr Health, which offers an analytics app for life science patients to facilitate customer engagement, and research and development. Neo4j provides the means by which customers can query and issue feedback for any number of topics, a process which requires mining Cloud data and maintaining ID relevance at high speeds and extreme scalability.
Keywords: Cloud • Dataversity • eBay • HP • MySQL • Shutl • Zephyr Health