
Enterprise Data Workflows with Cascading
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Content
- Intro
- Copyright
- Table of Contents
- Preface
- Requirements
- Enterprise Data Workflows
- Complexity, More So Than Bigness
- Origins of the Cascading API
- Using Code Examples
- Safari® Books Online
- How to Contact Us
- Kudos
- Chapter 1. Getting Started
- Programming Environment Setup
- Example 1: Simplest Possible App in Cascading
- Build and Run
- Cascading Taxonomy
- Example 2: The Ubiquitous Word Count
- Flow Diagrams
- Predictability at Scale
- Chapter 2. Extending Pipe Assemblies
- Example 3: Customized Operations
- Scrubbing Tokens
- Example 4: Replicated Joins
- Stop Words and Replicated Joins
- Comparing with Apache Pig
- Comparing with Apache Hive
- Chapter 3. Test-Driven Development
- Example 5: TF-IDF Implementation
- Example 6: TF-IDF with Testing
- A Word or Two About Testing
- Chapter 4. Scalding-A Scala DSL for Cascading
- Why Use Scalding?
- Getting Started with Scalding
- Example 3 in Scalding: Word Count with Customized Operations
- A Word or Two about Functional Programming
- Example 4 in Scalding: Replicated Joins
- Build Scalding Apps with Gradle
- Running on Amazon AWS
- Chapter 5. Cascalog-A Clojure DSL for Cascading
- Why Use Cascalog?
- Getting Started with Cascalog
- Example 1 in Cascalog: Simplest Possible App
- Example 4 in Cascalog: Replicated Joins
- Example 6 in Cascalog: TF-IDF with Testing
- Cascalog Technology and Uses
- Chapter 6. Beyond MapReduce
- Applications and Organizations
- Lingual, a DSL for ANSI SQL
- Using the SQL Command Shell
- Using the JDBC Driver
- Integrating with Desktop Tools
- Pattern, a DSL for Predictive Model Markup Language
- Getting Started with Pattern
- Predefined App for PMML
- Integrating Pattern into Cascading Apps
- Customer Experiments
- Technology Roadmap for Pattern
- Chapter 7. The Workflow Abstraction
- Key Insights
- Pattern Language
- Literate Programming
- Separation of Concerns
- Functional Relational Programming
- Enterprise vs. Start-Ups
- Chapter 8. Case Study: City of Palo Alto Open Data
- Why Open Data?
- City of Palo Alto
- Moving from Raw Sources to Data Products
- Calibrating Metrics for the Recommender
- Spatial Indexing
- Personalization
- Recommendations
- Build and Run
- Key Points of the Recommender Workflow
- Appendix A. Troubleshooting Workflows
- Build and Runtime Problems
- Anti-Patterns
- Workflow Bottlenecks
- Other Resources
- Index
- About the Author
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