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    Sumo Logic Melds Log Data with Structured Metrics

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    The Challenges of Gaining Useful Insight into Data

    Most IT administrators spend a fair amount of their time trying to troubleshoot a problem by correlating log data against all the structured data that’s been collected about the IT environment. In fact, making sense of that data usually takes multiple orders of time than fixing the problem once the actual issue is discovered.

    To remove that drudgery for IT administrators, Sumo Logic today announced that its namesake machine data analytics service can now natively ingest, index and analyze structured metrics data and unstructured log data together in real time. In addition, Sumo Logic revealed that its analytic service now supports time-series metrics to make correlating log data and specific metrics simpler easy, instant, contextual and comprehensive.

    The service was originally designed to collect and analyze massive amounts of unstructured machine data using machine learning algorithms running on the Amazon Web Services (AWS) cloud. Sumo Logic CEO Ramin Sayar says IT organizations can now have a single pane of glass through which all key performance indicators can be holistically tracked using a set of declarative query tools provided by Sumo Logic. Alternatively, IT organizations can make the analytics data generated by Sumo Logic available to other applications via a set of REST application programming interfaces (APIs).

    SumoLogic

    While most IT organizations appreciate the potential of log data, collecting and correlating all that information remains a challenge. For that reason, Sumo Logic has been making the case for storing and analyzing log data in the cloud. Now, Sayar says, that same data can be used within the context of all the structured data that IT organizations collect in a variety of IT management applications to provide more context.

    The degree to which having access to all the data improves the overall quality of IT management will naturally vary by organization. But the days of manually sifting through a log data haystack looking for a proverbial needle to figure out what might be going wrong with a particular application are most definitely coming to a close.

    Mike Vizard
    Mike Vizard
    Michael Vizard is a seasoned IT journalist, with nearly 30 years of experience writing and editing about enterprise IT issues. He is a contributor to publications including Programmableweb, IT Business Edge, CIOinsight and UBM Tech. He formerly was editorial director for Ziff-Davis Enterprise, where he launched the company’s custom content division, and has also served as editor in chief for CRN and InfoWorld. He also has held editorial positions at PC Week, Computerworld and Digital Review.

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