Combine the power of Apache Spark and Python to build effective big data applications. Apache Spark is an open source framework for efficient cluster computing with a strong interface for data parallelism and fault tolerance.
The PySpark Cookbook presents effective and time-saving recipes for leveraging the power of Python and putting it to use in the Spark ecosystem.
You'll start by learning the Apache Spark architecture and how to set up a Python environment for Spark. You'll then get familiar with the modules available in PySpark and start using them effortlessly.
In addition to this, you'll discover how to abstract data with RDDs and DataFrames, and understand the streaming capabilities of PySpark. You'll then move on to using ML and MLlib in order to solve any problems related to the machine learning capabilities of PySpark and use GraphFrames to solve graph-processing problems. Finally, you will explore how to deploy your applications to the cloud using the spark-submit command.
By the end of this book, you will be able to use the Python API for Apache Spark to solve any problems associated with building data-intensive applications. The PySpark Cookbook is for you if you are a Python developer looking for hands-on recipes for using the Apache Spark 2. A thorough understanding of Python and some familiarity with Spark will help you get the best out of the book. 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. You will get familiar with the modules available in 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. This book takes a very comprehensive, step-by-step approach so you understand how the Spark ecosystem can be used with Python to develop efficient, scalable solutions.
Every chapter is standalone and written in a very easy-to-understand manner, with a focus on both the hows and the whys of each concept. This site comply with DMCA digital copyright. We do not store files not owned by us, or without the permission of the owner.
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