Book Image

Learning PySpark

By : Tomasz Drabas, Denny Lee
Book Image

Learning PySpark

By: Tomasz Drabas, Denny Lee

Overview of this book

Apache Spark is an open source framework for efficient cluster computing with a strong interface for data parallelism and fault tolerance. This book will show you how to leverage the power of Python and put it to use in the Spark ecosystem. You will start by getting a firm understanding of the Spark 2.0 architecture and how to set up a Python environment for Spark. You will get familiar with the modules available in PySpark. You will learn how to abstract data with RDDs and DataFrames and understand the streaming capabilities of PySpark. Also, you will get a thorough overview of machine learning capabilities of PySpark using ML and MLlib, graph processing using GraphFrames, and polyglot persistence using Blaze. Finally, you will learn how to deploy your applications to the cloud using the spark-submit command. By the end of this book, you will have established a firm understanding of the Spark Python API and how it can be used to build data-intensive applications.
Table of Contents (20 chapters)
Learning PySpark
Credits
Foreword
About the Authors
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface
Index

DataFrame scenario – on-time flight performance


To showcase the types of queries you can do with DataFrames, let's look at the use case of on-time flight performance. We will analyze the Airline On-Time Performance and Causes of Flight Delays: On-Time Data (http://bit.ly/2ccJPPM), and join this with the airports dataset, obtained from the Open Flights Airport, airline, and route data (http://bit.ly/2ccK5hw), to better understand the variables associated with flight delays.

Tip

For this section, we will be using Databricks Community Edition (a free offering of the Databricks product), which you can get at https://databricks.com/try-databricks. We will be using visualizations and pre-loaded datasets within Databricks to make it easier for you to focus on writing the code and analyzing the results.

If you would prefer to run this on your own environment, you can find the datasets available in our GitHub repository for this book at https://github.com/drabastomek/learningPySpark.

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