Open Daily: 10am - 10pm | Alley door pickup 10am - 7pm 3038 Hennepin Ave Minneapolis, MN
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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Parallel R: Data Analysis in the Distributed World

Parallel R: Data Analysis in the Distributed World

Weston, Stephen
McCallum, Q.

Paperback

Programming

ISBN10: 1449309925
ISBN13: 9781449309923
Publisher: Oreilly Media
Published: Nov 29 2011
Pages: 120
Weight: 0.47
Height: 0.27 Width: 7.00 Depth: 9.19
Language: English

It's tough to argue with R as a high-quality, cross-platform, open source statistical software product--unless you're in the business of crunching Big Data. This concise book introduces you to several strategies for using R to analyze large datasets, including three chapters on using R and Hadoop together. You'll learn the basics of Snow, Multicore, Parallel, Segue, RHIPE, and Hadoop Streaming, including how to find them, how to use them, when they work well, and when they don't.

With these packages, you can overcome R's single-threaded nature by spreading work across multiple CPUs, or offloading work to multiple machines to address R's memory barrier.

  • Snow: works well in a traditional cluster environment
  • Multicore: popular for multiprocessor and multicore computers
  • Parallel: part of the upcoming R 2.14.0 release
  • R+Hadoop: provides low-level access to a popular form of cluster computing
  • RHIPE: uses Hadoop's power with R's language and interactive shell
  • Segue: lets you use Elastic MapReduce as a backend for lapply-style operations

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