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You're reading from Fast Data Processing with Spark 2 - Third Edition

Product type Book

Published in Oct 2016

Publisher Packt

ISBN-13 9781785889271

Pages 274 pages

Edition 3rd Edition

Languages

Scala

Concepts

Data Processing

Author (1):

Holden Karau

Table of Contents (18) Chapters

Fast Data Processing with Spark 2 Third Edition

Credits

About the Author

About the Reviewers

www.PacktPub.com

Preface

Installing Spark and Setting Up Your Cluster

Using the Spark Shell

Building and Running a Spark Application

Creating a SparkSession Object

Loading and Saving Data in Spark

Manipulating Your RDD

Spark 2.0 Concepts

Spark SQL

Foundations of Datasets/DataFrames – The Proverbial Workhorse for DataScientists

Spark with Big Data

Machine Learning with Spark ML Pipelines

GraphX

Spark's machine learning algorithm table

Apache Spark covers a wide spectrum of machine learning algorithms. The algorithms implemented in Spark 2.0.0 consist of packages: org.apache.spark.ml for Scala and Java and pyspark.ml for Python.

Tip

Prior to 1.6.0, the libraries were in the org.apache.spark.mllib and pyspark.mllib packages, but from 2.0, the MLlib APIs are in maintenance mode. So you should use the ML APIs. In this chapter, we will do so, with clarifying notes wherever needed.

The following table summarizes the machine learning algorithms and data transformation features available in Spark 2.0.0:

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Authors (1)

Holden Karau

Holden Karau is a software development engineer and is active in the open source. She has worked on a variety of search, classification, and distributed systems problems at IBM, Alpine, Databricks, Google, Foursquare, and Amazon. She graduated from the University of Waterloo with a bachelor's of mathematics degree in computer science. Other than software, she enjoys playing with fire and hula hoops, and welding.

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Algorithm	Feature	Notes
Basic statistics	Summary statistics	Here mean, stdev, count, max, min, and numNonZeros are all part of `dataframe.count()`, `dataframe.describe()`, and sql.functions
	Correlations and covariance	Here, sql.functions are invoked as `dataframe.stat.corr`(0 and cov)
	Stratified sampling	This provides two methods, `sampleBykey` and `sampleByKeyExact`, with and without...