Book Image

Apache Spark 2 for Beginners

By : Rajanarayanan Thottuvaikkatumana
Book Image

Apache Spark 2 for Beginners

By: Rajanarayanan Thottuvaikkatumana

Overview of this book

<p>Spark is one of the most widely-used large-scale data processing engines and runs extremely fast. It is a framework that has tools that are equally useful for application developers as well as data scientists.</p> <p>This book starts with the fundamentals of Spark 2 and covers the core data processing framework and API, installation, and application development setup. Then the Spark programming model is introduced through real-world examples followed by Spark SQL programming with DataFrames. An introduction to SparkR is covered next. Later, we cover the charting and plotting features of Python in conjunction with Spark data processing. After that, we take a look at Spark's stream processing, machine learning, and graph processing libraries. The last chapter combines all the skills you learned from the preceding chapters to develop a real-world Spark application.</p> <p>By the end of this book, you will have all the knowledge you need to develop efficient large-scale applications using Apache Spark.</p>
Table of Contents (15 chapters)
Apache Spark 2 for Beginners
Credits
About the Author
About the Reviewer
www.PacktPub.com
Preface

Understanding machine learning


In traditional computing, input data is fed to a program to generate output. But in machine learning, input data and output data are fed to a machine learning algorithm to generate a function or program that can be used to predict the output of an input according to the learning done on the input/output dataset fed to the machine learning algorithm.

The data available in the wild may be classified into groups, it may form clusters, or it may fit into certain relationships. These are different kinds of machine learning problem. For example, if there is a databank of pre-owned car sale prices with its associated attributes or features, it is possible to predict the price of a car just by knowing the associated attributes or features. Regression algorithms are used to solve these kinds of problem. If there is a databank of spam and non-spam e-mails, then when a new e-mail comes, it is possible to predict whether the new e-mail is spam or non-spam. Classification...