Book Image

Apache Spark 2 for Beginners

By : Rajanarayanan Thottuvaikkatumana
Book Image

Apache Spark 2 for Beginners

By: Rajanarayanan Thottuvaikkatumana

Overview of this book

<p>Spark is one of the most widely-used large-scale data processing engines and runs extremely fast. It is a framework that has tools that are equally useful for application developers as well as data scientists.</p> <p>This book starts with the fundamentals of Spark 2 and covers the core data processing framework and API, installation, and application development setup. Then the Spark programming model is introduced through real-world examples followed by Spark SQL programming with DataFrames. An introduction to SparkR is covered next. Later, we cover the charting and plotting features of Python in conjunction with Spark data processing. After that, we take a look at Spark's stream processing, machine learning, and graph processing libraries. The last chapter combines all the skills you learned from the preceding chapters to develop a real-world Spark application.</p> <p>By the end of this book, you will have all the knowledge you need to develop efficient large-scale applications using Apache Spark.</p>
Table of Contents (15 chapters)
Apache Spark 2 for Beginners
Credits
About the Author
About the Reviewer
www.PacktPub.com
Preface

The Spark GraphX library


For graph processing, many libraries are available in the open source world. Giraph, Pregel, GraphLab, and Spark GraphX are some of them. Spark GraphX is one of the recent entrants into this space.

What is so special about Spark GraphX? Spark GraphX is a graph processing library built on top of the Spark data processing framework. Compared to the other graph processing libraries, Spark GraphX has a real advantage. It can make use of all the data processing capabilities of Spark. However, in reality, the performance of graph processing algorithms is not the only aspect that needs consideration.

In many applications, the data that needs to be modeled as a graph does not exist in that form naturally. In many use cases, more than the graph processing, lots of processor time and other computing resources are expended to get the data in the right format so that the graph processing algorithms can be applied. This is the sweet spot where the combination of the Spark data...