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This is an introductory book on PySpark.
This book is about PySpark: Python API for Spark.
Apache Spark is an analytics engine for large-scale
data processing. Spark is the open source cluster
computing system that makes data analytics fast
to write and fast to run. This book provides a
large set of recipes for implementing big data
processing and analytics using Spark and Python.
The goal of this book is to show working examples
in PySpark so that you can do your ETL and
analytics easier. You may cut and paste examples to
deliver your applications in PySpark.
This book introduces PySpark (Python API for Spark).
You can use PySpark to tackle big datasets quickly
through simple APIs in Python. You will learn how to
express parallel tasks and computations with just a
few lines of code, and cover applications from ETL,
simple batch jobs to stream processing and machine
With this book, you may dive into Spark capabilities
such as RDDs (resilient distributed datasets),
DataFrames (data as a table of rows and columns),
in memory caching, and the interactive PySpark
shell, where you may leverage Spark's powerful built
in libraries, including Spark SQL, Spark Streaming,
In this book, you will learn Spark's transformations
and actions by a set of well-defined and working
examples. All examples are tested and working: this
means that you can copy-cut-paste to your desired
PySpark applications. Writing PySpark is much easier
than writing Spark applications in Java and PySpark
applications are not bulky at all when compared to
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Parsian, Mahmoud, "PySpark Algorithms" (2019). Faculty Book Gallery. 458.