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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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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

Building your Spark job with Maven


Maven is an open source Apache project that builds the Spark jobs in Java or Scala. As of Version 2.0.0, the building Spark site states that Maven is the official recommendation for packaging Spark and is the "build of reference" too. As with sbt, you can include the Spark dependency through Maven Central, simplifying our build process. Also, similar to sbt is the ability of Spark and all of our dependencies to put everything in a single JAR file using a plugin or build Spark as a monolithic JAR file using the sbt/sbt assembly for inclusion.

To illustrate the build process for the Spark jobs with Maven, this section will use Java as an example, as Maven is more commonly used to build the Java tasks. As a first step, let's take a Spark job that already works and go through the process of creating a build file for it. We can start by copying the GroupByTest example into a new directory and generating the Maven template, as shown here:

mkdir example-java-build...
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