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

Why do we need just another library?


In order to answer this question, we have to know something about SystemML's history, which began ten years ago in 2007 as a research project in the IBM Almaden Research Lab in California. The project was driven by the intention to improve the workflow of data scientists, especially those who want to improve and add functionality to existing machine learning algorithms.

Note

So, SystemML is a declarative markup language that can transparently distribute work on Apache Spark. It supports Scale-up using multithreading and SIMD instructions on CPUs as well as GPUs and also Scale-out using a cluster, and of course, both together.

Finally, there is a cost-based optimizer in place to generate low-level execution plans taking statistics about the Dataset sizes into account. In other words, Apache SystemML is for machine learning, what Catalyst and Tungsten are for DataFrames.

Why on Apache Spark?

Apache Spark solves a lot of common issues in data processing and machine...