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

The concept of continuous applications


Streaming apps tend to grow in complexity. Streaming computations don't run in isolation; they interact with storage systems, batch applications, and machine learning libraries. Therefore, the notion of continuous applications--in contrast to batch processing--emerged, and basically means the composite of batch processing and real-time stream processing with a clear focus of the streaming part being the main driver of the application, and just accessing the data created or processed by batch processes for further augmentation. Continuous applications never stop and continuously produce data as new data arrives.

True unification - same code, same engine

So a continuous application could also be implemented on top of RDDs and DStreams but would require the use of use two different APIs. In Apache Spark Structured Streaming the APIs are unified. This unification is achieved by seeing a structured stream as a relational table without boundaries where new...