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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

Interactively loading data from S3

Now let's try another exercise with the Spark shell. As part of Amazon's EMR Spark support, they have handily provided some sample data of Wikipedia traffic statistics in S3, in the format that Spark can use. To access the data, you first need to set your AWS access credentials as shell params. For instructions on signing up for EC2 and setting up the shell parameters, see the Running Spark on EC2 with the scripts section in Chapter 1, Installing Spark and Setting Up Your Cluster (S3 access requires additional keys such as fs.s3n.awsAccessKeyId/awsSecretAccessKey or the use of the s3n://user:pw@ syntax). You can also set the shell parameters as AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY. We will leave the AWS configuration out of this discussion, but it needs to be completed.

Tip

This is a slightly advanced topic and needs a few S3 configurations (which we won't cover here). The Stack Overflow has two good links on this, namely http://stackoverflow...

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Fast Data Processing with Spark 2 - Third Edition
Published in: Oct 2016
Publisher: Packt
ISBN-13: 9781785889271
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