Guide to NoSQL databases: How they can help users meet big data needs
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When people think about big data technologies, Hadoop and NoSQL databases are usually the first things that come to mind. But in many cases, big data environments are supported by a mix of data management platforms -- and Hadoop and NoSQL systems aren't the predominant ones. For example, a survey conducted last year by Enterprise Management Associates Inc. (EMA) and 9sight Consulting found that NoSQL and Hadoop ranked sixth and eighth, respectively, on a list of eight technology platforms being used as a part of big data projects. More traditional technologies -- such as analytical databases, operational data stores and enterprise data warehouses -- were deployed more broadly than the surmised big-data duo, according to the survey of 259 IT and business professionals.
EMA and 9sight released a report about the survey last November, using the phrase "Operationalizing the Buzz" as part of the report's title. In a video interview with SearchDataManagement at the 2014 TDWI World Conference and BI Executive Summit in Las Vegas, EMA analyst John Myers discussed some of the key big data trends that the survey pointed to. Myers said a similar survey in 2012 "validated the buzzwords about big data: what it was, what it wasn't." By comparison, he added, the 2013 survey found an increasing number of organizations that were moving forward and bringing big data tools and applications "into their operational workflows and processes."
Hadoop and NoSQL software are clearly part of the picture, Myers said -- but they aren't synonymous with big data. Only 16% of the survey respondents said they were using Hadoop; for NoSQL, it was 22%. To power their big data programs, many of the companies represented in the survey are creating what EMA calls a hybrid data ecosystem, with a blend of old and new technologies. "So it's not one platform to rule them all, so to speak," he said, "but rather, how do you coordinate between a series of data management platforms to meet these challenges?"
One of the big challenges, he noted, is meeting the need for speed in big data analytics applications. According to Myers, data scientists, business analysts and other end users are looking for fast responses to analytical queries. One reason: Some of the prominent applications turned up by the survey were risk management and asset optimization -- "things that are core in an operational business." Such uses don't necessarily involve continuous real-time analytics, Myers said. But when queries are run, he added, "you need to be able to hit the button and get that speed-of-response back."
Watch the four-minute video to see all of what Myers had to say about the survey results and big data management and analytics trends.