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 unsupervised learning:

"In machine learning, the problem of unsupervised learning is that of trying to find hidden structure in unlabeled data."

In contrast to supervised learning, where we have labeled data to train an algorithm, in unsupervised learning, we ask the algorithm to find a structure on its own. Let's take a look at the following sample dataset:

As you can see in the preceding graph, the data points are forming two clusters, as follows:

In fact, clustering is the most common type of unsupervised learning algorithm.