Chapter 1 Introduction to Data Analysis with Spark
　　What Is Apache Spark?
　　A Unified Stack
　　Who Uses Spark, and for What?
　　A Brief History of Spark
　　Spark Versions and Releases
　　Storage Layers for Spark
Chapter 2 Downloading Spark and Getting Started
　　Downloading Spark
　　Introduction to Spark’s Python and Scala Shells
　　Introduction to Core Spark Concepts
　　Standalone Applications
　　Conclusion
Chapter 3 Programming with RDDs
　　RDD Basics
　　Creating RDDs
　　RDD Operations
　　Passing Functions to Spark
　　Common Transformations and Actions
　　Persistence (Caching)
　　Conclusion
Chapter 4 Working with Key/Value Pairs
　　Motivation
　　Creating Pair RDDs
　　Transformations on Pair RDDs
　　Actions Available on Pair RDDs
　　Data Partitioning (Advanced)
　　Conclusion
Chapter 5 Loading and Saving Your Data
　　Motivation
　　File Formats
　　Filesystems
　　Structured Data with Spark SQL
　　Databases
　　Conclusion
Chapter 6 Advanced Spark Programming
　　Introduction
　　Accumulators
　　Broadcast Variables
　　Working on a Per-Partition Basis
　　Piping to External Programs
　　Numeric RDD Operations
　　Conclusion
Chapter 7 Running on a Cluster
　　Introduction
　　Spark Runtime Architecture
　　Deploying Applications with spark-submit
　　Packaging Your Code and Dependencies
　　Scheduling Within and Between Spark Applications
　　Cluster Managers
　　Which Cluster Manager to Use?
　　Conclusion
Chapter 8 Tuning and Debugging Spark
　　Configuring Spark with SparkConf
　　Components of Execution: Jobs, Tasks, and Stages
　　Finding Information
　　Key Performance Considerations
　　Conclusion
Chapter 9 Spark SQL
　　Linking with Spark SQL
　　Using Spark SQL in Applications
　　Loading and Saving Data
　　JDBC/ODBC Server
　　User-Defined Functions
　　Spark SQL Performance
　　Conclusion
Chapter 10 Spark Streaming
　　A Simple Example
　　Architecture and Abstraction
　　Transformations
　　Output Operations
　　Input Sources
　　24/7 Operation
　　Streaming UI
　　Performance Considerations
　　Conclusion
Chapter 11 Machine Learning with MLlib
　　Overview
　　System Requirements
　　Machine Learning Basics
　　Data Types
　　Algorithms
　　Tips and Performance Considerations
　　Pipeline API
　　Conclusion