Recent updates to YarcData’s software for its Urika analytics appliance reflect the fact that the enterprise is starting to understand the impact that semantic technology has on turning Big Data into actual insights.
The latest update includes integration with more enterprise data discovery tools, including the visualization and business intelligence tools Centrifuge Visual Network Analytics and TIBCO Spotfire, as well as those based on SPARQL and RDF, JDBC, JSON, and Apache Jena. The goal is to streamline the process of getting data in and then being able to provide connectivity to the tools analysts use every day.
As customers see the value of using the appliance to gain business insight, they want to be able to more tightly integrate this technology into wider enterprise workflows and infrastructures, says Ramesh Menon, YarcData vice president, solutions. “Not only do you want data from all different enterprise sources to flow into the appliance easily, but the value of results is enhanced tremendously if the insights and the ability to use those insights are more broadly distributed inside the enterprise,” he says. “Instead of having one analyst write queries on the appliance, 200 analysts can use the appliance without necessarily knowing a lot about the underlying, or semantic, technology. They are able to use the front end or discovery tools they use on daily basis, not have to leave that interface, and still get the benefit of the Ureka appliance.”
YarcData, Menon says, also is seeing more integration with other Big Data technologies, like Hadoop, with customers interested in being able to get data from a Hadoop infrastructure, do discovery analytics, then provide the output to discovery front end tools, and also to pump that insight back into the Hadoop infrastructure, he notes.
The software update also hits the speed button, with the ability for Urika to handle key operations on aggregate functions up to 400 times faster. “Being able to have more complex queries run in shorter times means we can support analysts’ speed of thought in queries into the database,” Menon says. “The ability to put more information or triples into a given memory space is significant, too, meaning customers can get more out of a given-sized appliance than they would have done previously without upgrading any aspect of hardware.”
Menon also says that users can expect YarcData to move in the direction of providing advanced graph and analytics algorithms directly in the Ureka appliance as SPARQL query extensions. “That makes it much easier for customers to just write a SPARQL query to get advanced analytics,” he says. That’s driven by work from a number of use cases the company already is undertaking with customers around advanced algorithms aimed at doing groupings at will. Businesses, for instance, are engaging in this to find communities of related or like customers, where the definition of similarity can change depending on mixes of attributes. “In traditional data warehouse architectures it could take two to three months to change a grouping because the schemas have to change. But semantics helps because it is a schema-free representation – there’s no need to go in and change a bunch of links and keys and modify schemas to reflect new groupings,” he says. “You can due it at will on the fly because the data structure allows it and the appliance does it near realtime because we have the arch with shared memory and massively multithreaded processors to do this.”
Another big use case hits at the life science sector, around data discoveries that lead to being able to re-purpose existing medications for conditions beyond those which they were originally created to address. “The ability to shorten the time to market can be dramatically changed – from six to eight years to eighteen months – if you can find an existing drug and re-purpose it for another disease or ailment,” he says. In fact, the winners of the YarcData Graph Analytics Challenge this year worked in that vein, to identify existing drugs that could be used to address certain types of cancer. “That just goes to show that literally, with a few weeks of analysis you can find insights from data without presupposing anything,” he says.