Book Image

Apache Spark 2.x Cookbook

By : Rishi Yadav
Book Image

Apache Spark 2.x Cookbook

By: Rishi Yadav

Overview of this book

While Apache Spark 1.x gained a lot of traction and adoption in the early years, Spark 2.x delivers notable improvements in the areas of API, schema awareness, Performance, Structured Streaming, and simplifying building blocks to build better, faster, smarter, and more accessible big data applications. This book uncovers all these features in the form of structured recipes to analyze and mature large and complex sets of data. Starting with installing and configuring Apache Spark with various cluster managers, you will learn to set up development environments. Further on, you will be introduced to working with RDDs, DataFrames and Datasets to operate on schema aware data, and real-time streaming with various sources such as Twitter Stream and Apache Kafka. You will also work through recipes on machine learning, including supervised learning, unsupervised learning & recommendation engines in Spark. Last but not least, the final few chapters delve deeper into the concepts of graph processing using GraphX, securing your implementations, cluster optimization, and troubleshooting.
Table of Contents (19 chapters)
Title Page
Credits
About the Author
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface

Introduction


The following is Wikipedia's definition of machine learning:

"Machine learning is a scientific discipline that explores the construction and study of algorithms that can learn from data."

Essentially, machine learning is the process of using past data to make predictions about the future. Machine learning heavily depends upon statistical analysis and methodology.

In statistics, there are four types of measurement scales:

Scale type

Description

Nominal scale

  • =, ≠
  • Identifies categories
  • Can't be numeric
  • Example: male, female

Ordinal scale

  • =, ≠, <, >
  • Nominal scale +
  • Ranks from least important to most important
  • Example: corporate hierarchy

Interval scale

  • =, ≠, <, >, +, -
  • Ordinal scale + distance between observations
  • Numbers assigned to observations indicate order
  • Difference between any consecutive values is same as others
  • 60° temperature is not doubly hot than 30°

Ratio scale

  • =, ≠, <, >, +, ×, ÷
  • Interval scale +ratios of observations
  • $20 is twice as costly as $10

Another distinction that...