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Scala for Data Science

You're reading from   Scala for Data Science Leverage the power of Scala with different tools to build scalable, robust data science applications

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Product type Paperback
Published in Jan 2016
Publisher
ISBN-13 9781785281372
Length 416 pages
Edition 1st Edition
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Author (1):
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 Bugnion Bugnion
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Bugnion
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Table of Contents (22) Chapters Close

Scala for Data Science
Credits
About the Author
About the Reviewers
www.PacktPub.com
Preface
1. Scala and Data Science FREE CHAPTER 2. Manipulating Data with Breeze 3. Plotting with breeze-viz 4. Parallel Collections and Futures 5. Scala and SQL through JDBC 6. Slick – A Functional Interface for SQL 7. Web APIs 8. Scala and MongoDB 9. Concurrency with Akka 10. Distributed Batch Processing with Spark 11. Spark SQL and DataFrames 12. Distributed Machine Learning with MLlib 13. Web APIs with Play 14. Visualization with D3 and the Play Framework Pattern Matching and Extractors Index

Installing Spark


In previous chapters, we included dependencies by specifying them in a build.sbt file, and relying on SBT to fetch them from the Maven Central repositories. For Apache Spark, downloading the source code or pre-built binaries explicitly is more common, since Spark ships with many command line scripts that greatly facilitate launching jobs and interacting with a cluster.

Head over to http://spark.apache.org/downloads.html and download Spark 1.5.2, choosing the "pre-built for Hadoop 2.6 or later" package. You can also build Spark from source if you need customizations, but we will stick to the pre-built version since it requires no configuration.

Clicking Download will download a tarball, which you can unpack with the following command:

$ tar xzf spark-1.5.2-bin-hadoop2.6.tgz

This will create a spark-1.5.2-bin-hadoop2.6 directory. To verify that Spark works correctly, navigate to spark-1.5.2-bin-hadoop2.6/bin and launch the Spark shell using ./spark-shell. This is just a Scala...

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