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 DataFrame operations to transform

The data from the API has an RDD underneath it, and so there is no way that the DataFrame could be mutable. In DataFrame, the immutability is even better because we can add and subtract columns from it dynamically, without changing the source dataset.

In this section, we will cover the following topics:

  • Understanding DataFrame immutability
  • Creating two leaves from the one root DataFrame
  • Adding a new column by issuing transformation

We will start by using data from operations to transform our DataFrame. First, we need to understand DataFrame immutability and then we will create two leaves, but this time from the one root DataFrame. We will then issue a transformation that is a bit different than the RDD. This will add a new column to our resulting DataFrame because we are manipulating it this way in a DataFrame. If we want to map data,...