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


This chapter has attempted to provide you with an overview of some of the functionality available within the Apache Spark MLlib module. It has also shown the functionality that will soon be available in terms of ANNs or artificial neural networks. You might have been impressed how well ANNs work, so there is a lot more on ANNs in a later Chapter covering DeepLearning. It is not possible to cover all the areas of MLlib due to the time and space allowed for this chapter. In addition, we now want to concentrate more on the SparkML library in the next chapter, which speeds up machine learning by supporting DataFrames and the underlying Catalyst and Tungsten optimizations.

We saw how to develop Scala-based examples for Naive Bayes classification, K-Means clustering, and ANNs. You learned how to prepare test data for these Spark MLlib routines. You also saw that they all accept the LabeledPoint structure, which contains features and labels.

Additionally, each approach takes a training and...