Books & Videos

Table of Contents

  1. Chapter 1 Introduction to Data Analysis with Spark

    1. What Is Apache Spark?

    2. A Unified Stack

    3. Who Uses Spark, and for What?

    4. A Brief History of Spark

    5. Spark Versions and Releases

    6. Storage Layers for Spark

  2. Chapter 2 Downloading Spark and Getting Started

    1. Downloading Spark

    2. Introduction to Spark’s Python and Scala Shells

    3. Introduction to Core Spark Concepts

    4. Standalone Applications

    5. Conclusion

  3. Chapter 3 Programming with RDDs

    1. RDD Basics

    2. Creating RDDs

    3. RDD Operations

    4. Passing Functions to Spark

    5. Common Transformations and Actions

    6. Persistence (Caching)

    7. Conclusion

  4. Chapter 4 Working with Key/Value Pairs

    1. Motivation

    2. Creating Pair RDDs

    3. Transformations on Pair RDDs

    4. Actions Available on Pair RDDs

    5. Data Partitioning (Advanced)

    6. Conclusion

  5. Chapter 5 Loading and Saving Your Data

    1. Motivation

    2. File Formats

    3. Filesystems

    4. Structured Data with Spark SQL

    5. Databases

    6. Conclusion

  6. Chapter 6 Advanced Spark Programming

    1. Introduction

    2. Accumulators

    3. Broadcast Variables

    4. Working on a Per-Partition Basis

    5. Piping to External Programs

    6. Numeric RDD Operations

    7. Conclusion

  7. Chapter 7 Running on a Cluster

    1. Introduction

    2. Spark Runtime Architecture

    3. Deploying Applications with spark-submit

    4. Packaging Your Code and Dependencies

    5. Scheduling Within and Between Spark Applications

    6. Cluster Managers

    7. Which Cluster Manager to Use?

    8. Conclusion

  8. Chapter 8 Tuning and Debugging Spark

    1. Configuring Spark with SparkConf

    2. Components of Execution: Jobs, Tasks, and Stages

    3. Finding Information

    4. Key Performance Considerations

    5. Conclusion

  9. Chapter 9 Spark SQL

    1. Linking with Spark SQL

    2. Using Spark SQL in Applications

    3. Loading and Saving Data

    4. JDBC/ODBC Server

    5. User-Defined Functions

    6. Spark SQL Performance

    7. Conclusion

  10. Chapter 10 Spark Streaming

    1. A Simple Example

    2. Architecture and Abstraction

    3. Transformations

    4. Output Operations

    5. Input Sources

    6. 24/7 Operation

    7. Streaming UI

    8. Performance Considerations

    9. Conclusion

  11. Chapter 11 Machine Learning with MLlib

    1. Overview

    2. System Requirements

    3. Machine Learning Basics

    4. Data Types

    5. Algorithms

    6. Tips and Performance Considerations

    7. Pipeline API

    8. Conclusion