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Apache Spark for Data Science Cookbook

You're reading from  Apache Spark for Data Science Cookbook

Product type Book
Published in Dec 2016
Publisher
ISBN-13 9781785880100
Pages 392 pages
Edition 1st Edition
Languages
Concepts
Author (1):
Padma Priya Chitturi Padma Priya Chitturi
Profile icon Padma Priya Chitturi
LinkedIn
Padma Priya Chitturi is Analytics Lead at Fractal Analytics Pvt Ltd and has over five years of experience in Big Data processing. Currently, she is part of capability development at Fractal and responsible for solution development for analytical problems across multiple business domains at large scale. Prior to this, she worked for an Airlines product on a real-time processing platform serving one million user requests/sec at Amadeus Software Labs. She has worked on realizing large-scale deep networks (Jeffrey deans work in Google brain) for image classification on the big data platform Spark. She works closely with Big Data technologies such as Spark, Storm, Cassandra and Hadoop. She was an open source contributor to Apache Storm.
See other products by Padma Priya Chitturi

Table of Contents (17) Chapters

Apache Spark for Data Science Cookbook
Credits
About the Author
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface
Big Data Analytics with Spark Tricky Statistics with Spark Data Analysis with Spark Clustering, Classification, and Regression Working with Spark MLlib NLP with Spark Working with Sparkling Water - H2O Data Visualization with Spark Deep Learning on Spark Working with SparkR

Creating SparkR DataFrames

A DataFrame is a distributed collection of data organized into named columns. It is conceptually equivalent to a table in a relational database or a DataFrame in R, but with rich optimizations. SparkR DataFrames scale to large datasets using the support for distributed computation in Spark. In this recipe, we'll see how to create SparkR DataFrames from different sources, such as JSON, CSV, local R DataFrames, and Hive tables.

Getting ready

To step through this recipe, you will need a running Spark Cluster either in pseudo distributed mode or in one of the distributed modes, that is, standalone, YARN, or Mesos. Also, install RStudio. Please refer to the Installing R recipe for details on the installation of R. Please refer to the Creating a SparkR standalone application from Rstudio recipe for details on working with the SparkR package.

How to do it…

In this recipe, we'll see how to create SparkR data frames in Spark 1.6.0 as well as Spark 2.0.2:

  1. Use createDataFrame...

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Authors (1)

Profile icon Padma Priya Chitturi
LinkedIn
Padma Priya Chitturi is Analytics Lead at Fractal Analytics Pvt Ltd and has over five years of experience in Big Data processing. Currently, she is part of capability development at Fractal and responsible for solution development for analytical problems across multiple business domains at large scale. Prior to this, she worked for an Airlines product on a real-time processing platform serving one million user requests/sec at Amadeus Software Labs. She has worked on realizing large-scale deep networks (Jeffrey deans work in Google brain) for image classification on the big data platform Spark. She works closely with Big Data technologies such as Spark, Storm, Cassandra and Hadoop. She was an open source contributor to Apache Storm.
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See other products by Padma Priya Chitturi

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