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Think of these technologies as the software framework within Hadoop that performs all the complicated processing needed for distributed computing. Spark represents the next generation of computing on Hadoop compared to MapReduce , providing advanced capabilities like machine learning, stream processing, and in-memory computing. Writing MapReduce or Spark applications from scratch can be complicated, so there are a variety of APIs or interfaces to other tools, like R, available for users.

Great question! And also the purpose of this blog. With files all over the place and no required structure or schema, getting a dataset useful for modeling might seem difficult.

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Hive employs schema-on-read design — which means that structure is applied to the data during reading or execution of the query, rather than having to decide when the data was written or stored. This provides tremendous flexibility in how the data can be used. It also provides storage and data management advantages as the Hive query can be saved as a lightweight metadata object, rather than having to write the complete results to a file. There are a variety of ways to use Hive tables in R.

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One is SparkR from Apache. This R package is available only with the Spark distribution not on CRAN , which makes getting started a pretty big investment. In preparation for this post, I followed the tutorial for getting started with R Server on HDInsight to deploy a Hadoop cluster, and then followed the instructions in the section for accessing data in Hive. Within minutes, I was up and running with a cluster and had experimented with the sample Hive data!

For an even deeper tutorial, check out this post from Microsoft.

Big Data Analytics with R and Hadoop : Vignesh Prajapati :

It has a very lightweight memory footprint in R, even for massive datasets. Hopefully this article has been helpful to understanding the value of using Hadoop data in R. For more information about Microsoft R Server, please see our recent webinars here and here. For more information on Hadoop, please visit our resource center. Andy is a Principal Consultant at BlueGranite.

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This book is also aimed at those who know Hadoop and want to build some intelligent applications over Big data with R packages. It would be helpful if readers have basic knowledge of R. Frequently Bought Together. Big Data Analytics with R and Hadoop.

Add 3 Items to Cart. Rate Product. A half baked product , just trying to encash on the popularity of Big Data and making fool of gullible new comers. I was extremely disappointed that I wasted my money on it. Deal with an archaic version of hadoop and Map reduce..


For most of the critical steps , links to 3'rd party blogs are provided , which in anyways come up on Google's first page. I wont recommend this book to any one.. Spending some time on wikipedia and Abhi Certified Buyer , Ranchi Jul, Exceptionally good with authoritative technical tips and tricks.

Very relavant in current big data scenario. Have doubts regarding this product?