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

RDDs versus DataFrames versus Datasets


To make it clear, we are discouraging you from using RDDs unless there is a strong reason to do so for the following reasons:

  • RDDs, on an abstraction level, are equivalent to assembler or machine code when it comes to system programming
  • RDDs express how to do something and not what is to be achieved, leaving no room for optimizers
  • RDDs have proprietary syntax; SQL is more widely known

Whenever possible, use Datasets because their static typing makes them faster. As long as you are using statically typed languages such as Java or Scala, you are fine. Otherwise, you have to stick with DataFrames.