Books & Videos

Table of Contents

  1. Chapter 1 Getting Started

    1. Programming Environment Setup

    2. Example 1: Simplest Possible App in Cascading

    3. Build and Run

    4. Cascading Taxonomy

    5. Example 2: The Ubiquitous Word Count

    6. Flow Diagrams

    7. Predictability at Scale

  2. Chapter 2 Extending Pipe Assemblies

    1. Example 3: Customized Operations

    2. Scrubbing Tokens

    3. Example 4: Replicated Joins

    4. Stop Words and Replicated Joins

    5. Comparing with Apache Pig

    6. Comparing with Apache Hive

  3. Chapter 3 Test-Driven Development

    1. Example 5: TF-IDF Implementation

    2. Example 6: TF-IDF with Testing

    3. A Word or Two About Testing

  4. Chapter 4 Scalding—A Scala DSL for Cascading

    1. Why Use Scalding?

    2. Getting Started with Scalding

    3. Example 3 in Scalding: Word Count with Customized Operations

    4. A Word or Two about Functional Programming

    5. Example 4 in Scalding: Replicated Joins

    6. Build Scalding Apps with Gradle

    7. Running on Amazon AWS

  5. Chapter 5 Cascalog—A Clojure DSL for Cascading

    1. Why Use Cascalog?

    2. Getting Started with Cascalog

    3. Example 1 in Cascalog: Simplest Possible App

    4. Example 4 in Cascalog: Replicated Joins

    5. Example 6 in Cascalog: TF-IDF with Testing

    6. Cascalog Technology and Uses

  6. Chapter 6 Beyond MapReduce

    1. Applications and Organizations

    2. Lingual, a DSL for ANSI SQL

    3. Pattern, a DSL for Predictive Model Markup Language

  7. Chapter 7 The Workflow Abstraction

    1. Key Insights

    2. Pattern Language

    3. Literate Programming

    4. Separation of Concerns

    5. Functional Relational Programming

    6. Enterprise vs. Start-Ups

  8. Chapter 8 Case Study: City of Palo Alto Open Data

    1. Why Open Data?

    2. City of Palo Alto

    3. Moving from Raw Sources to Data Products

    4. Calibrating Metrics for the Recommender

    5. Spatial Indexing

    6. Personalization

    7. Recommendations

    8. Build and Run

    9. Key Points of the Recommender Workflow

  1. Appendix Troubleshooting Workflows

    1. Build and Runtime Problems

    2. Anti-Patterns

    3. Workflow Bottlenecks

    4. Other Resources

  2. Index

  3. Colophon