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

Feature algorithms


In real-world use cases, it is not very easy to get the raw data in the appropriate form of features and labels in order to train the model. Doing lots of pre-processing is very common. Unlike other data processing paradigms, Spark in conjunction with the Spark machine learning library provides a comprehensive set of tools and algorithms for this purpose. This pre-processing algorithms can be put into three categories:

  • Feature extraction

  • Feature transformation

  • Feature selection

The process of extracting the features from the raw data is feature extraction. The HashingTF that was used in the preceding use case is a good example of an algorithm that converts terms of text data to feature vectors. The process of transforming features into different formats is feature transformation. The process of selecting a subset of features from a super set is feature selection. Covering all these is beyond the scope of this chapter, but the next section is going to discuss an Estimator,...