Book Image

Mastering Apache Spark 2.x - Second Edition

Book Image

Mastering Apache Spark 2.x - Second Edition

Overview of this book

Apache Spark is an in-memory, cluster-based Big Data processing system that provides a wide range of functionalities such as graph processing, machine learning, stream processing, and more. This book will take your knowledge of Apache Spark to the next level by teaching you how to expand Spark’s functionality and build your data flows and machine/deep learning programs on top of the platform. The book starts with a quick overview of the Apache Spark ecosystem, and introduces you to the new features and capabilities in Apache Spark 2.x. You will then work with the different modules in Apache Spark such as interactive querying with Spark SQL, using DataFrames and DataSets effectively, streaming analytics with Spark Streaming, and performing machine learning and deep learning on Spark using MLlib and external tools such as H20 and Deeplearning4j. The book also contains chapters on efficient graph processing, memory management and using Apache Spark on the cloud. By the end of this book, you will have all the necessary information to master Apache Spark, and use it efficiently for Big Data processing and analytics.
Table of Contents (21 chapters)
Title Page
Credits
About the Author
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface
10
Deep Learning on Apache Spark with DeepLearning4j and H2O

Summary


In closing this chapter, we invite you to work your way through each of the Scala code-based examples in the following chapters. The rate at which Apache Spark has evolved is impressive, and important to note is the frequency of the releases. So even though, at the time of writing, Spark has reached 2.2, we are sure that you will be using a later version.

If you encounter problems, report them at www.stackoverflow.com and tag them accordingly; you'll receive feedback within minutes--the user community is very active. Another way of getting information and help is subscribing to the Apache Spark mailing list: [email protected].

By the end of this chapter, you should have a good idea what's waiting for you in this book. We've dedicated our effort to showing you practical examples that are, on the one hand, practical recipes to solve day-to-day problems, but on the other hand, also support you in understanding the details of things taking place behind the scenes. This is very important for writing good data products and a key differentiation from others.

The next chapter focuses on ApacheSparkSQL. We believe that this is one of the hottest topics that has been introduced to Apache Spark for two reasons.

First, SQL is a very old and established language for data processing. It was invented by IBM in the 1970s and soon will be nearly half a century old. However, what makes SQL different from other programming languages is that, in SQL, you don't declare how something is done but what should be achieved. This gives a lot of room for downstream optimizations.

This leads us to the second reason. As structured data processing continuously becomes the standard way of data analysis in Apache Spark, optimizers such as Tungsten and Catalyst play an important role; so important that we've dedicated two entire chapters to the topic. So stay tuned and enjoy!