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


Apache Spark in the cloud is the perfect solution for data scientists and data engineers who want to concentrate on getting the actual work done without being concerned about the operation of an Apache Spark cluster.

We saw that Apache Spark in the cloud is much more than just installing Apache Spark on a couple of virtual machines. It comes as a whole package for the data scientist, completely based on open-source components, which makes it easy to migrate to other cloud providers or to local datacenters if necessary.

We also learned that, in a typical data science project, the variety of skills is huge, which is taken care of by supporting all common programming languages and open-source data analytics frameworks on top of Apache Spark and Jupyter notebooks, and by completely eliminating the necessity for operational skills required to maintain the Apache Spark cluster.

Sometimes just one level of increased access to the underlying infrastructure is necessary. Maybe some specific...