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Big Data Analytics

You're reading from   Big Data Analytics Real time analytics using Apache Spark and Hadoop

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Product type Paperback
Published in Sep 2016
Publisher Packt
ISBN-13 9781785884696
Length 326 pages
Edition 1st Edition
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Author (1):
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Venkat Ankam Venkat Ankam
Author Profile Icon Venkat Ankam
Venkat Ankam
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Table of Contents (12) Chapters Close

Preface 1. Big Data Analytics at a 10,000-Foot View 2. Getting Started with Apache Hadoop and Apache Spark FREE CHAPTER 3. Deep Dive into Apache Spark 4. Big Data Analytics with Spark SQL, DataFrames, and Datasets 5. Real-Time Analytics with Spark Streaming and Structured Streaming 6. Notebooks and Dataflows with Spark and Hadoop 7. Machine Learning with Spark and Hadoop 8. Building Recommendation Systems with Spark and Mahout 9. Graph Analytics with GraphX 10. Interactive Analytics with SparkR Index

Spark applications

Let's understand the difference between spark Shell and spark applications and how they are created and submitted.

Spark Shell versus Spark applications

Spark lets you access your datasets through a simple, yet specialized, Spark shell for Scala, Python, R, and SQL. Users do not need to create a full application to explore the data. They can start exploring data with commands that can be converted to programs later. This provides higher developer productivity. A Spark application is a complete program with SparkContext that is submitted with the spark-submit command.

Scala programs are generally written using Scala IDE or IntelliJ IDEA and SBT is used to compile the programs. Java programs are generally written in Eclipse and compiled with Maven. Python and R programs can be written in any text editor and also using IDEs such as Eclipse. Once the Scala and Java programs are written, they are compiled and executed with the spark-submit command as shown in the following...

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