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| Preface | |
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| Getting Started | |
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| Programming Environment Setup | |
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| Example 1: Simplest Possible App in Cascading | |
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| Build and Run | |
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| Cascading Taxonomy | |
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| Example 2: The Ubiquitous Word Count | |
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| Flow Diagrams | |
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| Predictability at Scale | |
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| Extending Pipe Assemblies | |
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| Example 3: Customized Operations | |
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| Scrubbing Tokens | |
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| Example 4: Replicated Joins | |
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| Stop Words and Replicated Joins | |
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| Comparing with Apache Pig | |
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| Comparing with Apache Hive | |
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| Test-Driven Development | |
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| Example 5: TF-IDF Implementation | |
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| Example 6: TF-IDF with Testing | |
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| A Word or Two About Testing | |
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| Scalding-A Scala DSL for Cascading | |
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| Why Use Scalding? | |
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| Getting Started with Scalding | |
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| Example 3 in Scalding: Word Count with Customized Operations | |
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| A Word or Two about Functional Programming | |
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| Example 4 in Scalding: Replicated Joins | |
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| Build Scalding Apps with Gradle | |
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| Running on Amazon AWS | |
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| Cascalog-A Clojure DSL for Cascading | |
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| Why Use Cascalog? | |
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| Getting Started with Cascalog | |
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| Example 1 in Cascalog: Simplest Possible App | |
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| Example 4 in Cascalog: Replicated Joins | |
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| Example 6 in Cascalog: TF-IDF with Testing | |
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| Cascalog Technology and Uses | |
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| Beyond MapReduce | |
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| Applications and Organizations | |
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| Lingual, a DSL for ANSI SQL | |
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| Using the SQL Command Shell | |
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| Using the JDBC Driver | |
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| Integrating with Desktop Tools | |
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| Pattern, a DSL for Predictive Model Markup Language | |
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| Getting Started with Pattern | |
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| Predefined App for PMML | |
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| Integrating Pattern into Cascading Apps | |
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| Customer Experiments | |
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| Technology Roadmap for Pattern | |
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| The Workflow Abstraction | |
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| Key Insights | |
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| Pattern Language | |
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| Literate Programming | |
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| Separation of Concerns | |
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| Functional Relational Programming | |
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| Enterprise vs. Start-Ups | |
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| Case Study: City of Palo Alto Open Data | |
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| Why Open Data? | |
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| City of Palo Alto | |
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| Moving from Raw Sources to Data Products | |
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| Calibrating Metrics for the Recommender | |
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| Spatial Indexing | |
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| Personalization | |
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| Recommendations | |
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| Build and Run | |
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| Key Points of the Recommender Workflow | |
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| Troubleshooting Workflows | |
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| Index | |