Last week, Forrester published its 2015 “Wave” report for Big Data Predictive Analytics Solutions. You can pay $2,495 and buy it directly from Forrester (here), or you can get the same report for free from SAS (here). The report is inaptly named, as it commingles software that scales to Big Data (such as Alpine Chorus) with software that does not
Read moreFirst, a review of last year’s predictions: (1) Apache Spark matures as the preferred platform for advanced analytics in Hadoop. At the New York Strata/Hadoop World conference in October, if you took a drink each time a speaker said “Spark”, you would struggle to make it past noon. At my lunch table, every single person said his company is currently
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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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Analytic users are not all the same; in most organizations, there are a number of different user “personalities”, or personas, with distinct needs. If you develop an analytics architecture for your organization or develop analytic software to sell to others, it is important to understand these personas. In this essay, I profile four personas: Power Analyst Data Scientist Business Analyst
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Dell announced this morning that it has acquired Statsoft, a privately held company that distributes Statistica, a suite of software for statistics and data mining. Terms of sale were not announced. Founded by academics in 1984, Statsoft has developed a loyal following at the low end of the analytics market, where it offers a reasonably priced alternative to SAS
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Much has changed since I last blogged on this subject a year ago (here and here). This is the first of a three-part blog covering the current state of play for machine learning in Hadoop. I use the term “machine learning” deliberately, to refer to tools that can learn from data in an automated or semi-automated manner; this includes traditional
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A few predictions for the coming year. (1) Apache Spark matures as the preferred platform for advanced analytics in Hadoop. Spark will achieve top-level project status in Apache by July; that milestone, together with inclusion in Cloudera CDH5, will validate the project’s rapid maturation. Organizations will increasingly question the value of “point solutions” for Hadoop analytics versus Spark’s integrated platform
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