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)

Summary

In this chapter, we first learned how to separate logic from the Spark engine. We then looked at a component that was well-tested in separation without the Spark engine, and we carried out integration testing using SparkSession. For this, we created a SparkSession test by reusing the component that was already well-tested. By doing that, we did not have to cover all edge cases in the integration test and our test was much faster. We then learned how to leverage partial functions to supply mocked data that's provided at the testing phase. We also covered ScalaCheck for property-based testing. By the end of this chapter, we had tested our code in different versions of Spark and learned how to change our DataFrame mock test to RDD.

In the next chapter, we will learn how to leverage the Spark GraphX API.