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)

Basics of RDD operation

Let's now go through some RDD operational basics. The best way to understand what something does is to look at the documentation so that we can get a rigorous understanding of what a function performs.

The reason why this is very important is that the documentation is the golden source of how a function is defined and what it is designed to be used as. By reading the documentation, we make sure that we are as close to the source as possible in our understanding. The link to the relevant documentation is https://spark.apache.org/docs/latest/rdd-programming-guide.html.

So, let's start with the map function. The map function returns an RDD by applying the f function to each element of this RDD. In other words, it works the same as the map function we see in Python. On the other hand, the filter function returns a new RDD containing only the elements...