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Using R for Big Data with Spark

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Using R for Big Data with Spark

Using R for Big Data with Spark

HDRips | MP4/AVC, ~592 kb/s | 1280x720 | Duration: 02:20:02 | English: AAC, 128 kb/s (2 ch) | 731 MB

Genre: Development / Programming

Data analysts familiar with R will learn to leverage the power of Spark, distributed computing and cloud storage in this course that shows you how to use your R skills in a big data environment.
You'll learn to create Spark clusters on the Amazon Web Services (AWS) platform; perform cluster based data modeling using Gaussian generalized linear models, binomial generalized linear models, Naive Bayes, and K-means modeling; access data from S3 Spark DataFrames and other formats like CSV, Json, and HDFS; and do cluster based data manipulation operations with tools like SparkR and SparkSQL. By course end, you'll be capable of working with massive data sets not possible on a single computer. This hands-on class requires each learner to set-up their own extremely low-cost, easily terminated AWS account.

Discover how to use your R skills in a big data distributed cloud computing cluster environment
Gain hands-on experience setting up Spark clusters on Amazon's AWS cloud services platform
Understand how to control a cloud instance on AWS using SSH or PuTTY
Explore basic distributed modeling techniques like GLM, Naive Bayes, and K-means
Learn to do cloud based data manipulation and processing using SparkR and SparkSQL
Understand how to access data from the CSV, Json, HDFS, and S3 formats

Using R for Big Data with Spark

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  1. 使用R与Spark用于大数据分析 熟悉R的数据分析师将会利用Spark的强大功能,本教程中的分布式计算和云存储将会为你讲解如何使用R的技巧与大数据环境中。 你将会学习在AWS平台上创建Spark集群;使用高斯广义线性模型、二项广义线性模型、朴素贝叶斯方法以及K均值模型执行基于数据建模的集群;从S3 Spark DataFrames和其他格式,如CSV、JSON和HDFS访问数据;通过如SparkR和SparkSQL这样的工具执行基于数据操作操作的集群。学习完本教程后,你能够处理不可能在一台单一计算机上海量数据集。这部实用的教程需要每个学习者建立他们自己的成本极低的AWS账号。 主要内容:了解在大数据分布式云计算集群环境中使用R的技巧;获得在AWS云服务平台上建立Spark集群实战经验;理解如何使用SSH或PuTTY控制在AWS上的云实例;探索基本的分布式建模技术,如GLM、朴素贝叶斯等;使用SparkR和SparkSQL学习处理基于云的数据处理;理解如何从CSV、JSON、HDFS以及S3格式访问数据。
    wilde(特殊组-翻译)1个月前 (10-28)