Book Image

PySpark Cookbook

By : Denny Lee, Tomasz Drabas
Book Image

PySpark Cookbook

By: Denny Lee, Tomasz Drabas

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. The PySpark Cookbook presents effective and time-saving recipes for leveraging the power of Python and putting it to use in the Spark ecosystem. You’ll start by learning the Apache Spark architecture and how to set up a Python environment for Spark. You’ll then get familiar with the modules available in PySpark and start using them effortlessly. In addition to this, you’ll discover how to abstract data with RDDs and DataFrames, and understand the streaming capabilities of PySpark. You’ll then move on to using ML and MLlib in order to solve any problems related to the machine learning capabilities of PySpark and use GraphFrames to solve graph-processing problems. Finally, you will explore how to deploy your applications to the cloud using the spark-submit command. By the end of this book, you will be able to use the Python API for Apache Spark to solve any problems associated with building data-intensive applications.
Table of Contents (13 chapters)
Title Page
Packt Upsell


Now that we have a thorough understanding of how RDDs and DataFrames work and what they can do, we can start preparing ourselves and our data for modeling. 

Someone famous (Albert Einstein) once said (paraphrasing):

"The universe and the problems with any dataset are infinite, and I am not sure about the former."

The preceding is of course a joke. However, any dataset you work with, be it acquired at work, found online, collected yourself, or obtained through any other means, is dirty until proven otherwise; you should not trust it, you should not play with it, you should not even look at it until such time that you have proven to yourself that it is sufficiently clean (there is no such thing as totally clean).

What problems can your dataset have? Well, to name a few:

  • Duplicated observations: These arise through systemic and operator's faults
  • Missing observations: These can emerge due to sensor problems, respondents' unwillingness to provide an answer to a question, or simply some...