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


Before we start this chapter, it is important that we discuss some trends that directly affect how we develop applications. 

Big data applications can be divided into the following three categories:

  • Batch
  • Interactive
  • Streaming or continuous applications

When Hadoop was designed, the primary focus was to provide cost-effective storage for large amounts of data. This remained the main show until it was upended by S3 and other cheaper and more reliable cloud storage alternatives. Compute on this large amounts of data in the Hadoop environment was primarily in the form of MapReduce jobs. Since Spark took the ball from Hadoop (OK! Snatched!) and started running with it, Spark also reflected batch orientation focus in the initial phase, but it did a better job than Hadoop in the case of exploiting in-memory storage. 

Note

The most compelling factor of the success of Hadoop was that the cost of storage was hundreds of times lower than traditional data warehouse technologies, such as Teradata...