What this book covers

Chapter 1, Understanding Spark, provides an introduction into the Spark world with an overview of the technology and the jobs organization concepts.

Chapter 2, Resilient Distributed Datasets, covers RDDs, the fundamental, schema-less data structure available in PySpark.

Chapter 3, DataFrames, provides a detailed overview of a data structure that bridges the gap between Scala and Python in terms of efficiency.

Chapter 4, Prepare Data for Modeling, guides the reader through the process of cleaning up and transforming data in the Spark environment.

Chapter 5, Introducing MLlib, introduces the machine learning library that works on RDDs and reviews the most useful machine learning models.

Chapter 6, Introducing the ML Package, covers the current mainstream machine learning library and provides an overview of all the models currently available.

Chapter 7, GraphFrames, will guide you through the new structure that makes solving problems with graphs easy.

Chapter 8, TensorFrames, introduces the bridge between Spark and the Deep Learning world of TensorFlow.

Chapter 9, Polyglot Persistence with Blaze, describes how Blaze can be paired with Spark for even easier abstraction of data from various sources.

Chapter 10, Structured Streaming, provides an overview of streaming tools available in PySpark.

Chapter 11, Packaging Spark Applications, will guide you through the steps of modularizing your code and submitting it for execution to Spark through command-line interface.

For more information, we have provided two bonus chapters as follows:

Installing Spark: https://www.packtpub.com/sites/default/files/downloads/InstallingSpark.pdf

Free Spark Cloud Offering: https://www.packtpub.com/sites/default/files/downloads/FreeSparkCloudOffering.pdf