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)

Using RDD in an immutable way

Now that we know how to create a chain of execution using RDD inheritance, let's learn how to use RDD in an immutable way.

In this section, we will go through the following topics:

  • Understating DAG immutability
  • Creating two leaves from the one root RDD
  • Examining results from both leaves

Let's first understand directed acyclic graph immutability and what it gives us. We will then be creating two leaves from one node RDD, and checking if both leaves are behaving totally independently if we create a transformation on one of the leaf RDD's. We will then examine results from both leaves of our current RDD and check if any transformation on any leaf does not change or impact the root RDD. It is imperative to work like this because we have found that we will not be able to create yet another leaf from the root RDD, because the root RDD will...