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Fast Data Processing with Spark 2

You're reading from   Fast Data Processing with Spark 2 Accelerate your data for rapid insight

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Product type Paperback
Published in Oct 2016
Publisher Packt
ISBN-13 9781785889271
Length 274 pages
Edition 3rd Edition
Languages
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Authors (2):
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Krishna Sankar Krishna Sankar
Author Profile Icon Krishna Sankar
Krishna Sankar
Holden Karau Holden Karau
Author Profile Icon Holden Karau
Holden Karau
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Toc

Table of Contents (13) Chapters Close

Preface 1. Installing Spark and Setting Up Your Cluster 2. Using the Spark Shell FREE CHAPTER 3. Building and Running a Spark Application 4. Creating a SparkSession Object 5. Loading and Saving Data in Spark 6. Manipulating Your RDD 7. Spark 2.0 Concepts 8. Spark SQL 9. Foundations of Datasets/DataFrames – The Proverbial Workhorse for DataScientists 10. Spark with Big Data 11. Machine Learning with Spark ML Pipelines 12. GraphX

Loading a simple text file

Let's download a Dataset and do some experimentation. One of the (if not the best) books for machine learning is The Elements of Statistical Learning, Trevor Hastie, Jerome H. Friedman, Robert Tibshirani, Springer. The book site has an interesting set of Datasets. Let's grab the spam Dataset using the following command:

wget http://www-stat.stanford.edu/~tibs/ElemStatLearn/ datasets/spam.data

Alternatively, you can find the spam Dataset from the GitHub link at https://github.com/xsankar/fdps-v3.

Note

All the examples assume that you have downloaded the repository in the fdps-v3 directory in your home folder, that is, ~/fdps-v3/. Please adjust the directory name if you have downloaded them somewhere else.

Now, load it as a text file into Spark with the following commands inside your Spark shell:

scala> val inFile = sc.textFile("data/spam.data")
scala> inFile.count()

This loads the spam.data file into Spark with each line being a separate entry...

You have been reading a chapter from
Fast Data Processing with Spark 2 - Third Edition
Published in: Oct 2016
Publisher: Packt
ISBN-13: 9781785889271
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