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

Why notebooks are the new standard


Jupyter notebooks are rapidly becoming the default way to apply data science. They can be seen as Google Docs for analytics. Documentation, lines of code, and output from that code (text, data tables, charts, and plots) are all united.

In addition, such notebooks are easily shareable, even without a backend. Just export them as JSON documents.

Note

Jupyter notebooks preserve all output cells even if the cell during execution has been connected on a large scale Apache Spark cluster processing hundreds of gigabytes of data.

In my experience, notebooks are used mostly in the following scenarios:

  • Notebooks are an ideal tool for creating and sharing a knowledge base on best practices a core data science team executes. This way, their knowledge is documented in an executable way.
  • Notebooks are used to document the variety of available data sources in an enterprise and to provide executable quick-start code.
  • Creating master-templates - casting best practices and company...