Updated March 8, 2016. After publishing this post, I met with several IBM executives at Spark Summit East, who confirmed the accuracy of the original post and provided additional detail, which I’ve included in this version. Updates are in bold red italics. IBM also provided the low-resolution image. IBM has a good story to tell — one out of ten contributors
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Spark Summit Europe, Oracle Open World and IBM Insights all met last week, as did Cloudera’s Wrangle conference for data scientists. But in the really important news, KC beats the Mets to take the Series. Top news from the Spark Summit is Typesafe’s announcement of Spark support, plus some insight into what’s coming in Spark 1.6. I will publish a
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With its customary PR blitz, IBM announces that it has added Spark integration to several products, including SPSS. IBM gets a small pat on the head for adding Spark support to its Analytics Server software, under the premise that something is better than nothing. There is a very narrow pool of SPSS users who will benefit from this enhancement. Spark integration is
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Strata+Hadoop World week is a good opportunity to update the list of platforms for high-performance advanced analytics. Vendors are hustling this week to announce their latest enhancements; I’ll post updates as needed. First some definition. The scope of this analysis includes software with the following properties: Support for supervised and unsupervised machine learning Support for distributed processing Open platform or multi-vendor
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Can we leverage distributed computing for machine learning and predictive analytics? The question keeps surfacing in different contexts, so I thought I’d take a few minutes to write an overview of the topic. The question is important for four reasons: Source data for analytics frequently resides in distributed data platforms, such as MPP appliances or Hadoop; In many cases, the
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ML/AI 