Book Image

Learning PySpark

By : Tomasz Drabas, Denny Lee
Book Image

Learning PySpark

By: Tomasz Drabas, Denny Lee

Overview of this book

Apache Spark is an open source framework for efficient cluster computing with a strong interface for data parallelism and fault tolerance. This book will show you how to leverage the power of Python and put it to use in the Spark ecosystem. You will start by getting a firm understanding of the Spark 2.0 architecture and how to set up a Python environment for Spark. You will get familiar with the modules available in PySpark. You will learn how to abstract data with RDDs and DataFrames and understand the streaming capabilities of PySpark. Also, you will get a thorough overview of machine learning capabilities of PySpark using ML and MLlib, graph processing using GraphFrames, and polyglot persistence using Blaze. Finally, you will learn how to deploy your applications to the cloud using the spark-submit command. By the end of this book, you will have established a firm understanding of the Spark Python API and how it can be used to build data-intensive applications.
Table of Contents (20 chapters)
Learning PySpark
Credits
Foreword
About the Authors
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface
Index

Polyglot persistence


Neal Ford introduced the, somewhat similar, term polyglot programming in 2006. He used it to illustrate the fact that there is no such thing as a one-size-fits-all solution and advocated using multiple programming languages that were more suitable for certain problems.

In the parallel world of data, any business that wants to remain competitive needs to adapt a range of technologies that allows it to solve the problems in a minimal time, thus minimizing the costs.

Storing transactional data in Hadoop files is possible, but makes little sense. On the other hand, processing petabytes of Internet logs using a Relational Database Management System (RDBMS) would also be ill-advised. These tools were designed to tackle specific types of tasks; even though they can be co-opted to solve other problems, the cost of adapting the tools to do so would be enormous. It is a virtual equivalent of trying to fit a square peg in a round hole.

For example, consider a company that sells musical...