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


We saw that containerization is the new way to go and that managing an abundance of containers requires orchestrations. We learned that Kubernetes is such an orchestrator and we saw how it can be used to create a simple Apache Spark cluster within minutes. We used a local test installation for playing around with Kubernetes, but the example shown can be used out-of-the-box on any Kubernetes installation.

We also saw that using Kubernetes as a service in the cloud can be very beneficial when used in conjunction with Apache Spark, since in the cloud the underlying Docker containers are charged on an hourly basis, therefore making it possible to elastically grow and shrink the Apache Spark cluster on the fly, as needed.

Finally, using Kubernetes in the cloud and in the local data center as well allows a broader usage scenario in a so-called hybrid cloud approach. In such an approach, depending on constraints such as data protection laws and resource requirements, an Apache Spark cluster...