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Spark for Data Science

You're reading from   Spark for Data Science Analyze your data and delve deep into the world of machine learning with the latest Spark version, 2.0

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Product type Paperback
Published in Sep 2016
Publisher Packt
ISBN-13 9781785885655
Length 344 pages
Edition 1st Edition
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Authors (2):
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Bikramaditya Singhal Bikramaditya Singhal
Author Profile Icon Bikramaditya Singhal
Bikramaditya Singhal
Srinivas Duvvuri Srinivas Duvvuri
Author Profile Icon Srinivas Duvvuri
Srinivas Duvvuri
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Toc

Table of Contents (12) Chapters Close

Preface 1. Big Data and Data Science – An Introduction FREE CHAPTER 2. The Spark Programming Model 3. Introduction to DataFrames 4. Unified Data Access 5. Data Analysis on Spark 6. Machine Learning 7. Extending Spark with SparkR 8. Analyzing Unstructured Data 9. Visualizing Big Data 10. Putting It All Together 11. Building Data Science Applications

The Spark engine

To program with Spark, a basic understanding of Spark components is needed. In this section, some of the important Spark components along with their execution mechanism will be explained so that developers and data scientists can write programs and build applications.

Before getting into the details, we suggest you take a look at the following diagram so that the descriptions of the Spark gears are more comprehensible as you read further:

The Spark engine

Driver program

The Spark shell is an example of a driver program. A driver program is a process that executes in the JVM and runs the user's main function on it. It has a SparkContext object which is a connection to the underlying cluster manager. A Spark application is initiated when the driver starts and it completes when the driver stops. The driver, through an instance of SparkContext, coordinates all processes within a Spark application.

Primarily, an RDD lineage Directed Acyclic Graph (DAG) is built on the driver side with data...

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