Apache Spark is an open source, parallel processing framework that has been around for quite some time now. One of the many uses of Apache Spark is for data analytics applications across clustered computers.

This book will help you implement some practical and proven techniques to improve aspects of programming and administration in Apache Spark. You will not only learn how to use Spark and the Python API to create high-performance analytics with big data, but also discover techniques to test, immunize, and parallelize Spark jobs.

This book covers the installation and setup of PySpark, RDD operations, big data cleaning and wrangling, and aggregating and summarizing data into useful reports. You will learn how to source data from all popular data hosting platforms, including HDFS, Hive, JSON, and S3, and deal with large datasets with PySpark to gain practical big data experience. This book will also help you to work on prototypes on local machines and subsequently go on to handle messy data in production and on a large scale.