5 Best PySpark Books – Learning PySpark in a Simple Way
We will see in this tutorial the best books to learn pyspark whether for beginners or advanced users.
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No Time to Waste? Here’s My #1 Favorite!
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Introduction
Today, the explosion of digital data is forcing new ways of analyzing data. This is where Spark comes in. Apache Spark is a Big Data engine that over the years has become one of the largest distributed processing frameworks in the world. It is now present in the majority of major digital companies, but also increasingly in other large institutions such as banking, food and beverage, healthcare and many other areas.
Spark is an incredible analysis factory, thanks to its ability to manage massive amounts of data distributed across a multitude of clusters, Spark has been adopted as the standard.
Using the Python-based API (Called PySpark) that wraps the SPARK engine, we can use the SPARK-based data pipelines and allow programmers (Data Scientists, Data Engineer …) to use the python-based programming language to develop machine learning models for simplicity.
To deepen your knowledge of PySpark, I propose you to list the best current books for learning PySpark. This list will be divided in two parts, for beginners on the use of PySpark and a second part for more experienced users on the subject.
Best 5 PySpark Books
Pyspark Books for Beginners
Learning PySpark by Tomasz Drabas and Denny Lee
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If you are new to Pyspark, this book takes you through the basics of Spark. For users who want to use python coupled with the SPARK ecosystem, this book is for you.
In this book you will learn :
- How to solve graph problems and how to learn in depth using GraphFrames and TensorFrames respectively.
- Using Spark DataFrames using SQL Spark
- Analyze and transform data and use it to form machine learning models (using MLib)
- Program your applications using spark-submit and deploy them on a cluster.
This already represents a solid knowledge base on the use of Pyspark and does not require an in-depth knowledge of SPARK.
PySpark Recipes: A Problem-Solution Approach with PySpark2 by Raju Kumar Mishra
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This book brings solutions to all the programming problems we may encounter on Big DATA processing. In particular, it will help you to learn the concepts of RDDs. For beginners, this book also covers the Numpy library present in Python (widely used in datascience), which will facilitate the understanding of PySpark.
This book covers the following themes:
- Understanding the advanced features of PySpark2 and SparkSQL
- How to optimize your code for better performance
- Using SparkSQL with Python
- Use Spark Streaming and Spark MLlib with Python
- Perform a graphical analysis with GraphFrames
PySpark Cookbook by de Denny Lee and Tomasz Drabas
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This book offers more than 60 recipes for implementing Big Data processing and analysis using Apache Spark and Python.
At the end of this book you will be able to use the Python API for Apache Spark to solve all the problems associated with creating data intensive applications.
You will learn to :
- Configure a local instance of PySpark in a virtual environment and install Jupyter in local and multi-node environments
- Create PySpark DataFrames from several file formats (for example the json format)
- Explore the regression and clustering models available in the ML module
- Use DataFrames to transform the data used for modeling
Pyspark Book for Advanced Users
Frank Kane’s Taming Big Data with Apache Spark and Python by Frank Kane
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In this book you will learn how to configure Spark on a single system or cluster. You will learn how to use SPARK RDD to analyze large volumes of data and how to develop and execute SPARK tasks using Python.
To simplify the understanding of SPARK, this book offers 15 interactive examples to better understand the SPARK ecosystem and to implement these SPARK projects in real time without any problem.
This book will deal with the following themes:
- Install and run Apache Spark on your computer or on a cluster.
- Analyze large datasets on multiple processors
- Implement Machine Learning on Spark using the MLlib library
- Process continuous streams of data in real time using the Spark streaming module.
- Perform complex network analysis using the GraphX Spark library
- Use Amazon’s Elastic MapReduce service to run your Spark tasks on a cluster
Learn PySpark: Build Python-based Machine Learning and Deep Learning Models by Pramod Singh
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In this book, you will review the basic principles of PySpark (including the basic architecture of SPARK). You will learn how to use PySPARK to process large volumes of data (how to ingest, clean and process data). You will also see how to create workflows to anlyse data in streaming using Pyspark.
This book covers the following themes:
- Developing pipelines for streaming data processing using PySpark
- Create machine learning and deep learning models
- Using graphical analysis with PySpark
- Create sequence embedding from text data
Conclusion
In this tutorial, we have seen the best books to get a better understanding of PySpark. This list is exhaustive, there are many references and books on this subject, but it will help both novices and experienced people to have a good base on PySpark.
You can also consult video tutorials on youtube. Here is a link to a tutorial I find interesting for novices on PySpark :
Feel free to tell me in comments if you need information about one of these books or about the use of PySpark. I propose on my website some tutorials about PySpark’s particular functions, don’t hesitate to consult them.
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