As an addendum to my year-end review of machine learning and deep learning, I offer this survey of SQL engines. SQL is the most widely used language for data science according to O’Reilly’s 2016 Data Science Salary Survey. Most projects require at least some SQL operations, and many need nothing but SQL. This review covers six open source leaders: Hive, Impala,
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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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A group of scientists affiliated with IBM and several universities report on a detailed analysis of MapReduce and Spark performance across four different workloads. In this benchmark, Spark outperformed MapReduce on Word Count, k-Means and Page Rank, while MapReduce outperformed Spark on Sort. On the ADT Dev Watch blog Dave Ramel summarizes the paper, arguing that it “brings into question..Databricks Daytona GraySort claim”. This point refers to Databricks’ record-setting
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O’Reilly releases its 2015 Data Science Salary Survey. The report, authored by John King and Roger Magoulas summarizes results from an ongoing web survey. The 2015 survey includes responses from “over 600” participants, down from the “over 800” tabulated in 2014. The authors note that the survey includes self-selected respondents from the O’Reilly audience and may not generalize to the
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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
Read moreIn a previous post, I summarized some myths about SAS and R — arguments offered by proponents of one or the other that deserve to be dismissed. In this post, I will review some arguments that do make sense — things to consider if you are an aspiring analyst or if you are an executive making decisions about software for
Read moreWhich is better for analytics, SAS or R? One frequently sees discussions on this topic in social media; for examples, see here, here, here, here, here and here. Like many debates in social media, the degree of conviction is often inverse to the quantity of information, and these discussions often produce more heat than light. The question is serious. Many
Read moreSAS supports several different products that run “inside” Hadoop based on two different in-memory architectures: (1) The SAS High Performance Analytics suite, originally designed to run in dedicated Teradata and Greenplum appliances, includes five modules: Statistics, Data Mining, Text Mining, Econometrics and Optimization. (2) A second set of products — SAS Visual Analytics, SAS Visual Statistics and SAS In-Memory Statistics for Hadoop
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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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A reader complains that I did not include Python in a survey of Machine Learning in Hadoop. It’s a fair point. There was a lively debate last year between R and Python advocates, variously described as a war or a boxing match. Matt Asay argued that Python is displacing R; Sharon Machlis and David Smith countered. In this post I review the
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ML/AI 