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Parallel R: Data Analysis in the Distributed World
₹725.00
Overview of Parallel R: Data Analysis in the Distributed World Book
| Title | Parallel R: Data Analysis in the Distributed World |
| Author | Q.Ethan McCallum, Stephen Weston |
| ISBN-13 | 9789350236802 |
| Publication | SHROFF PUB |
It's tough to argue with R as a high-quality, cross-platform, open source statistical software productóunless 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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Overview of Parallel R: Data Analysis in the Distributed World Book
It's tough to argue with R as a high-quality, cross-platform, open source statistical software productóunless 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
Book Type :
Databases & Big Data
Book Binding :
Not Available
Language :
English
No. of Pages :
140 Pages
ISBN - 10 :
935023680X
ISBN - 13 :
9789350236802
Parallel R: Data Analysis in the Distributed World Published On :
1 January 2012
Publisher :
Shroff/O'Reilly
Edition :
First Edition
About Q.Ethan McCallum, Stephen Weston:
Q.Ethan McCallum, Stephen Weston is the author of Parallel R: Data Analysis in the Distributed World.
Disclaimer:
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- Table of Contents, Index, Syllabus, summary and image of Parallel R: Data Analysis in the Distributed World may be of a different edition or of the same title.
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Overview of Parallel R: Data Analysis in the Distributed World Book
| Title | Parallel R: Data Analysis in the Distributed World |
| Author | Q.Ethan McCallum, Stephen Weston |
| ISBN-13 | 9789350236802 |
| Publication | SHROFF PUB |
It's tough to argue with R as a high-quality, cross-platform, open source statistical software productóunless 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