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⇱ MapR Archives - Page 2 of 2 - Java Code Geeks


  • DevOps👁 Image
    Martijn KieboomJune 30th, 2016
    0 170

    Using MapR, Mesos, Marathon, Docker, and Apache Spark to Deploy and Run Your First Jobs and Containers

    This blog post describes steps for deploying Mesos, Marathon, Docker, and Spark on a MapR cluster, and running various jobs…

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  • Software Development👁 Image
    Sameer NoriJune 26th, 2016
    2 144

    Top 10 Questions about Apache Spark on the MapR Data Platform

    In the last few weeks, we’ve seen a lot of activity and momentum centered on Apache Spark. At the Spark…

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  • Software Development👁 Image
    Ellen FriedmanJune 23rd, 2016
    0 278

    How Apache Kafka and MapR Streams Handle Topic Partitions

    Streaming data can be used as a long-term auditable history when you choose a messaging system with persistence, but is…

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  • Software Development👁 Image
    Karen WhippleMay 24th, 2016
    0 167

    Apache Apex on MapR Converged Platform

    Editor’s Note: This guest post was authored by Charu Madan and Thomas Weise. In today’s world of immense competition and…

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  • Software Development👁 Image
    Mathieu DumoulinApril 29th, 2016
    0 148

    Distributed Deep Learning with Caffe Using a MapR Cluster

    We have experimented with CaffeOnSpark on a 5 node MapR 5.1 cluster running Spark 1.5.2 and will share our experience, difficulties,…

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  • Software Development👁 Image
    Carol McdonaldApril 25th, 2016
    0 178

    Fast, Scalable, Streaming Applications with MapR Streams, Spark Streaming, and MapR-DB

    Many of the systems we want to monitor happen as a stream of events. Examples include event data from web…

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  • Software Development👁 Image
    Tugdual GrallMarch 11th, 2016
    0 454

    Getting Started with MapR Streams

    MapR Streams is a new distributed messaging system for streaming event data at scale, and it’s integrated into the MapR…

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  • Software Development👁 Image
    Chase HooleyNovember 11th, 2015
    0 782

    Getting Started with the MapR Command Line (Part I)

    The Apache Hadoop community has give us a great set of tools that allow us to interact with the Hadoop…

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