Book Image

Hands-On Big Data Analytics with PySpark

By : Rudy Lai, Bartłomiej Potaczek
Book Image

Hands-On Big Data Analytics with PySpark

By: Rudy Lai, Bartłomiej Potaczek

Overview of this book

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. In this book, 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 for testing, immunizing, and parallelizing Spark jobs. 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 help you work on prototypes on local machines and subsequently go on to handle messy data in production and at scale. This book covers installing and setting up PySpark, RDD operations, big data cleaning and wrangling, and aggregating and summarizing data into useful reports. You will also learn how to implement some practical and proven techniques to improve certain aspects of programming and administration in Apache Spark. By the end of the book, you will be able to build big data analytical solutions using the various PySpark offerings and also optimize them effectively.
Table of Contents (15 chapters)

Installing Pyspark and Setting up Your Development Environment

In this chapter, we are going to introduce Spark and learn the core concepts, such as, SparkContext, and Spark tools such as SparkConf and Spark shell. The only prerequisite is the knowledge of basic Python concepts and the desire to seek insight from big data. We will learn how to analyze and discover patterns with Spark SQL to improve our business intelligence. Also, you will be able to quickly iterate through your solution by setting to PySpark for your own computer. By the end of the book, you will be able to work with real-life messy data sets using PySpark to get practical big data experience.

In this chapter, we will cover the following topics:

  • An overview of PySpark
  • Setting up Spark on Windows and PySpark
  • Core concepts in Spark and PySpark