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

Chapter 9. Apache SystemML

So far, we have only covered components that came along with the standard distribution of Apache Spark (except HDFS, Kafka and Flume, of course). However, Apache Spark can also serve as runtime for third-party components, making it as some sort of operating system for big data applications. In this chapter, we want to introduce Apache SystemML, an amazing piece of technology initially developed by the IBM Almaden Research Lab in California. Apache SystemML went through many transformation stages and has now become an Apache top level project.

In this chapter, we will cover the following topics to get a greater insight into the subject:

  • Using SystemML for your own machine learning applications on top of Apache Spark
  • Learning the fundamental differences between SystemML and other machine learning libraries for Apache Spark
  • Discovering the reason why another machine library exists for Apache Spark