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  • Book Overview & Buying Pig Design Patterns
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Pig Design Patterns

Pig Design Patterns

By : Pradeep Pasupuleti
4.3 (6)
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Pig Design Patterns

Pig Design Patterns

4.3 (6)
By: Pradeep Pasupuleti
Buy this Book

Overview of this book

Table of Contents (16 chapters)
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Pig Design Patterns
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Pig Design Patterns
Credits
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Credits
Foreword
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Foreword
About the Author
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About the Author
Acknowledgments
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Acknowledgments
About the Reviewers
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About the Reviewers
www.PacktPub.com
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www.PacktPub.com
Preface
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Preface
Lock Free Chapter
1
Setting the Context for Design Patterns in Pig
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Setting the Context for Design Patterns in Pig
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Understanding design patterns
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The scope of design patterns in Pig
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Hadoop demystified – a quick reckoner
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Pig – a quick intro
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Understanding Pig through the code
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Summary
2
Data Ingest and Egress Patterns
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Data Ingest and Egress Patterns
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The context of data ingest and egress
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Types of data in the enterprise
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Ingest and egress patterns for multistructured data
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The ingress and egress patterns for the NoSQL data
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The ingress and egress patterns for structured data
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The ingress and egress patterns for semi-structured data
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JSON ingress and egress patterns
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Summary
3
Data Profiling Patterns
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Data Profiling Patterns
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Data profiling for Big Data
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Rationale for using Pig in data profiling
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The data type inference pattern
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The basic statistical profiling pattern
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The pattern-matching pattern
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The string profiling pattern
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The unstructured text profiling pattern
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Summary
4
Data Validation and Cleansing Patterns
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Data Validation and Cleansing Patterns
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Data validation and cleansing for Big Data
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Choosing Pig for validation and cleansing
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The constraint validation and cleansing design pattern
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The regex validation and cleansing design pattern
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The corrupt data validation and cleansing design pattern
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The unstructured text data validation and cleansing design pattern
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Summary
5
Data Transformation Patterns
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Data Transformation Patterns
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Data transformation processes
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The structured-to-hierarchical transformation pattern
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The data normalization pattern
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The data integration pattern
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The aggregation pattern
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The data generalization pattern
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Summary
6
Understanding Data Reduction Patterns
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Understanding Data Reduction Patterns
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Data reduction – a quick introduction
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Data reduction considerations for Big Data
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Dimensionality reduction – the Principal Component Analysis design pattern
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Numerosity reduction – the histogram design pattern
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Numerosity reduction – sampling design pattern
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Numerosity reduction – clustering design pattern
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Summary
7
Advanced Patterns and Future Work
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Advanced Patterns and Future Work
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The clustering pattern
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The topic discovery pattern
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The natural language processing pattern
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The classification pattern
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Future trends
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Summary
1
Index
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Index

The data generalization pattern

The data generalization pattern deals with transforming the data by creating concept hierarchies and replacing the data with these hierarchies.

Background

This design pattern explores the implementation of data generalization through a Pig script. Data generalization is the process of creating top-level summary layers called concept hierarchies that describe the underlying data concept in a general form. It is a form of descriptive approach in which the data is grouped and replaced by higher level categories or concepts by using concept hierarchies. For example, the raw values of the attribute age can be replaced with conceptual labels (such as adult, teenager, or toddler), or they can be replaced by interval labels (0 to 5, 13 to 19, and so on). These labels, in turn, can be recursively organized into higher level concepts, resulting in a concept hierarchy for the attribute.

Motivation

In the context of Big Data, a typical analytics pipeline on huge volumes of...

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