Book Image

Learning Apache Spark 2

Book Image

Learning Apache Spark 2

Overview of this book

Apache Spark has seen an unprecedented growth in terms of its adoption over the last few years, mainly because of its speed, diversity and real-time data processing capabilities. It has quickly become the preferred choice of tool for many Big Data professionals looking to find quick insights from large chunks of data. This book introduces you to the Apache Spark framework, and familiarizes you with all the latest features and capabilities introduced in Spark 2. Starting with a detailed introduction to Spark’s architecture and the installation procedure, this book covers everything you need to know about the Spark framework in the most practical manner. You will learn how to perform the basic ETL activities using Spark, and work with different components of Spark such as Spark SQL, as well as the Dataset and DataFrame APIs for manipulating your data. Then, you will perform machine learning using Spark MLlib, as well as perform streaming analytics and graph processing using the Spark Streaming and GraphX modules respectively. The book also gives special emphasis on deploying your Spark models, and how they can be operated in a clustered mode. During the course of the book, you will come across implementations of different real-world use-cases and examples, giving you the hands-on knowledge you need to use Apache Spark in the best possible manner.
Table of Contents (18 chapters)
Learning Apache Spark 2
Credits
About the Author
About the Reviewers
www.packtpub.com
Customer Feedback
Preface

Summary


This concludes the chapter. We have gone through a churn prediction example using the PySpark and the Jupyiter notebook. I hope this gives you a good starting point for building your own applications. The full code and the Jupyter notebook are available on this book's GitHub page.

This was the last major chapter of this book. As a part of this book our intention was to take the users who are beginning to learn Spark on a journey where they can start from the very basics to a level where they feel comfortable with Spark as framework and also about writing their own Spark applications. We've covered some interesting topics including RDDs, DataFrames, MlLib, GraphX and also how to set up Spark in a cluster mode. Any book cannot do justice to Spark as a framework, as it is continuously evolving with new and exciting features added in every release.

We hope you have enjoyed this journey and look forward to hearing from you on your experience and feedback. In the Appendix, There's More with...