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    Juniper, Guavas Partner on Big Data Analytics Platform

    Written by

    Nathan Eddy
    Published May 21, 2013
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      Networking and data intelligence specialist Juniper Networks and Guavus, a provider of big data analytics solutions, unveiled the Junos Network Analytics suite, a family of next-generation big data analytics and network intelligence solutions that includes the BizReflex and NetReflex products.

      BizReflex and NetReflex were developed with Guavus, leveraging an analyze-first architecture that delivers insights from Internet protocol (IP) and Multiprotocol Label Switching (MPLS) network traffic patterns to better understand network behavior. Junos Network Analytics stock keeping units (SKUs) are available now and subscription-based pricing will be on Juniper’s price list in June, a company release noted.

      BizReflex is a network analytics engine and dashboard for business decision makers that allow them to gather intelligence on how customers, peers and prospects interact with the network. The tool extracts and analyzes information from edge and core routers to allow operators to segment enterprise customers according to their respective value and price services accordingly.

      NetReflex provides network architects and operations personnel with detailed traffic trends and analysis for IP and MPLS networks. The solution is designed to give operators more insight into traffic patterns on the network, with the aim of helping network service providers to reduce costs and improve the efficiency of their network.

      “Our analytics solutions have been built from the ground up to unlock the value of network-generated data by dramatically increasing the speed and scale at which business insights can be delivered and better businesses decisions can be made,” Anukool Lakhina, founder and CEO of Guavus, said in a statement. “We are pleased to be working with Juniper Networks to deliver a network analytics solution that allows customers to optimize their IP/MPLS assets for more efficient network operations, reduce costs and increase revenue.”

      Junos Network Analytics extracts network flow data and routing tables from Juniper Networks routing platforms or other third-party vendor routing platforms, and leverages an engine and user-friendly dashboard to provide business intelligence for communications service providers.

      “Junos Network Analytics harnesses the power of big data to bring near real-time information to service providers that will scale to their needs and provide a complete business insight solution for IP and MPLS networks,” Robert Krohn, senior vice president of Juniper’s edge software business unit, said in a statement. “Collaborating with Guavus allows Juniper Networks to provide customers with dynamic solutions that complement Juniper’s existing portfolio.”

      A report earlier this month from IT research firm Gartner indicated that while big data’s ability to analyze unstructured data in large volumes and from disparate sources leads to innovative opportunities, to get maximum value from the information, chief intelligence officers (CIOs) must realize that innovation needs to go well beyond the technology used to manage big data.

      The report noted that in most cases, there has been very little precedence for the ways big data can add value to an enterprise and it was never possible to run these kinds of analyses or access these new types of data. Therefore, any business will need time to trust new data sources and new analytics and enterprises should start small with pilots that allow full transparency on the data, the analytics and the resulting insight.

      Nathan Eddy
      Nathan Eddy
      A graduate of Northwestern University's Medill School of Journalism, Nathan was perviously the editor of gaming industry newsletter FierceGameBiz and has written for various consumer and tech publications including Popular Mechanics, Popular Science, CRN, and The Times of London. Currently based in Berlin, he released his first documentary film, The Absent Column, in 2013.

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