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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
 Karau Karau
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Karau
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Table of Contents (18) Chapters Close

Fast Data Processing with Spark 2 Third Edition
Credits
About the Author
About the Reviewers
www.PacktPub.com
Preface
1. Installing Spark and Setting Up Your Cluster FREE CHAPTER 2. Using the Spark Shell 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 something else


If neither sbt nor Maven suits your needs, you may decide to use another build system. Thankfully, Spark supports building a fat JAR file with all its dependencies, which makes it easy to include in the build system of your choice. Simply, run the sbt/sbt assembly in the Spark directory and copy the resulting assembly JAR file at core/target/spark-core-assembly-1.5.2.jar to your build dependencies, and you are good to go. It is more common to use the spark-assembly-1.5.2-hadoop2.6.0.jar file. These files exist in $SPARK_HOME$/lib (if users use a prebuilt version) or in $SPARK_HOME$/ assembly/target/scala-2.10/ (if users build the source code with Maven or sbt).

Tip

No matter what your build system is, you may find yourself wanting to use a patched version of the Spark libraries. In this case, you can deploy your Spark library locally. I recommend giving it a different version number to ensure that sbt/Maven picks up the modified version. You can...

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