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

Architecture


Remember that, although Spark is used for the speed of its in-memory distributed processing, it doesn't provide storage. You can use the Host (local) filesystem to read and write your data, but if your data volumes are big enough to be described as big data, then it makes sense to use a cloud-based distributed storage system such as OpenStack Swift Object Storage, which can be found in many cloud environments and can also be installed in private data centers.

Note

In case very high I/O is needed, HDFS would also be an option. More information on HDFS can be found here: http://hadoop.apache.org/docs/current/hadoop-project-dist/hadoop-hdfs/HdfsDesign.html.

The development environment

The Scala language will be used for the coding samples in this book. This is because, as a scripting language, it produces less code than Java. It can also be used from the Spark shell as well as compiled with Apache Spark applications. We will be using the sbt tool to compile the Scala code, which we...