What this book covers
Chapter 1, Architecture and Installation, will help you get started on the journey of learning Spark. This will walk you through key architectural components before helping you write your first Spark application.
Chapter 2, Transformations and Actions with Spark RDDs, will help you understand the basic constructs as Spark RDDs and help you understand the difference between transformations, actions, and lazy evaluation, and how you can share data.
Chapter 3, ELT with Spark, will help you with data loading, transformation, and saving it back to external storage systems.
Chapter 4, Spark SQL, will help you understand the intricacies of the DataFrame and Dataset API before a discussion of the under-the-hood power of the Catalyst optimizer and how it ensures that your client applications remain performant irrespective of your client AP.
Chapter 5, Spark Streaming, will help you understand the architecture of Spark Streaming, sliding window operations, caching, persistence, check-pointing, fault-tolerance before discussing structured streaming and how it revolutionizes Stream processing.
Chapter 6, Machine Learning with Spark, is where the rubber hits the road, and where you understand the basics of machine learning before looking at the various types of machine learning, and feature engineering utility functions, and finally looking at the algorithms provided by Spark MLlib API.
Chapter 7, GraphX, will help you understand the importance of Graph in today’s world, before understanding terminology such vertex, edge, Motif etc. We will then look at some of the graph algorithms in GraphX and also talk about GraphFrames.
Chapter 8, Operating in Clustered mode, helps the user understand how Spark can be deployed as standalone, or with YARN or Mesos.
Chapter 9, Building a Recommendation system, will help the user understand the intricacies of a recommendation system before building one with an ALS model.
Chapter 10, Customer Churn Predicting, will help the user understand the importance of Churn prediction before using a random forest classifier to predict churn on a telecommunication dataset.
Appendix, There's More with Spark, is where we cover the topics around performance tuning, sizing your executors, and security before walking the user through setting up PySpark with Jupyter notebook.
