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

Cloud-based deployments


There are three different abstraction levels of cloud systems--Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS). We will see how to use and install Apache Spark on all of these.

The new way to do IaaS is Docker and Kubernetes as opposed to virtual machines, basically providing a way to automatically set up an Apache Spark cluster within minutes. This will be covered inChapter 14, Apache Spark on Kubernetes.The advantage of Kubernetes is that it can be used among multiple different cloud providers as it is an open standard and also based on open source.

You even can use Kubernetes in a local data center and transparently and dynamically move workloads between local, dedicated, and public cloud data centers. PaaS, in contrast, takes away from you the burden of installing and operating an Apache Spark cluster because this is provided as a service.

There is an ongoing discussion whether Docker is IaaS or PaaS but, in our opinion, this is just a form of a lightweight preinstalled virtual machine. We will cover more on PaaS inChapter 13, Apache Spark with Jupyter Notebooks on IBM DataScience Experience.This is particularly interesting because the offering is completely based on open source technologies, which enables you to replicate the system on any other data center.

One of the open source components we'll introduce is Jupyter notebooks, a modern way to do data science in a cloud based collaborative environment. But in addition to Jupyter, there is also Apache Zeppelin, which we'll cover briefly in Chapter 14, Apache Spark on Kubernetes.