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| Preface | |
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| Setup | |
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| Theory | |
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| Agile Big Data | |
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| Big Words Defined | |
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| Agile Big Data Teams | |
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| Recognizing the Opportunity and Problem | |
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| Adapting to Change | |
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| Agile Big Data Process | |
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| Code Review and Pair Programming | |
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| Agile Environments: Engineering Productivity | |
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| Collaboration Space | |
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| Private Space | |
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| Personal Space | |
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| Realizing Ideas with Large-Format Printing | |
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| Data | |
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| Email | |
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| Working with Raw Data | |
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| Raw Email | |
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| Structured Versus Semistructured Data | |
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| SQL | |
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| NoSQL | |
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| Serialization | |
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| Extracting and Exposing Features in Evolving Schemas | |
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| Data Pipelines | |
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| Data Perspectives | |
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| Networks | |
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| Time Series | |
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| Natural Language | |
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| Probability | |
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| Conclusion | |
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| Agile Tools | |
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| Scalability = Simplicity | |
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| Agile Big Data Processing | |
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| Setting Up a Virtual Environment for Python | |
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| Serializing Events with Avro | |
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| Avro for Python | |
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| Collecting Data | |
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| Data Processing with Pig | |
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| Installing Pig | |
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| Publishing Data with MongoDB | |
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| Installing MongoDB | |
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| Installing MongoDB's Java Driver | |
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| Installing mongo-hadoop | |
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| Pushing Data to MongoDB from Pig | |
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| Searching Data with ElasticSearch | |
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| Installation | |
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| ElasticSearch and Pig with Wonderdog | |
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| Reflecting on our Workflow | |
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| Lightweight Web Applications | |
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| Python and Flask | |
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| Presenting Our Data | |
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| Installing Bootstrap | |
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| Booting Boostrap | |
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| Visualizing Data with D3.js and nvd3.js | |
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| Conclusion | |
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| To the Cloud! | |
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| Introduction | |
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| GitHub | |
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| dotCloud | |
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| Echo on dotCloud | |
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| Python Workers | |
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| Amazon Web Services | |
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| Simple Storage Service | |
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| Elastic MapReduce | |
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| MongoDB as a Service | |
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| Instrumentation | |
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| Google Analytics | |
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| Mortar Data | |
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| Climbing the Pyramid | |
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| Collecting and Displaying Records | |
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| Putting It All Together | |
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| Collect and Serialize Our Inbox | |
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| Process and Publish Our Emails | |
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| Presenting Emails in a Browser | |
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| Serving Emails with Flask and pymongo | |
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| Rendering HTML5 with Jinja2 | |
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| Agile Checkpoint | |
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| Listing Emails | |
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| Listing Emails with MongoDB | |
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| Anatomy of a Presentation | |
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| Searching Our Email | |
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| Indexing Our Email with Pig, ElasticSearch, and Wonderdog | |
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| Searching Our Email on the Web | |
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| Conclusion | |
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| Visualizing Data with Charts | |
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| Good Charts | |
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| Extracting Entities: Email Addresses | |
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| Extracting Emails | |
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| Visualizing Time | |
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| Conclusion | |
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| Exploring Data with Reports | |
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| Building Reports with Multiple Charts | |
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| Linking Records | |
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| Extracting Keywords from Emails with TF-IDF | |
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| Conclusion | |
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| Making Predictions | |
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| Predicting Response Rates to Emails | |
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| Personalization | |
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| Conclusion | |
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| Driving Actions | |
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| Properties of Successful Emails | |
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| Better Predictions with Naive Bayes | |
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| P(Reply From & To) | |
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| P(Reply Token) | |
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| Making Predictions in Real Time | |
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| Logging Events | |
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| Conclusion | |
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| Index | |