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Pig Design Patterns

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Pradeep Pasupuleti

Simplify Hadoop programming to create complex end-to-end Enterprise Big Data solutions with Pig.
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Book Details

ISBN 139781783285556
Paperback310 pages

About This Book

  • Quickly understand how to use Pig to design end-to-end Big Data systems
  • Implement a hands-on programming approach using design patterns to solve commonly occurring enterprise Big Data challenges
  • Enhances users’ capabilities to utilize Pig and create their own design patterns wherever applicable

Who This Book Is For

The experienced developer who is already familiar with Pig and is looking for a use case standpoint where they can relate to the problems of data ingestion, profiling, cleansing, transforming, and egressing data encountered in the enterprises. Knowledge of Hadoop and Pig is necessary for readers to grasp the intricacies of Pig design patterns better.

Table of Contents

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

What You Will Learn

  • Understand Pig's relevance in an enterprise context
  • Use Pig in design patterns that enable data movement across platforms during and after analytical processing
  • See how Pig can co-exist with other components of the Hadoop ecosystem to create Big Data solutions using design patterns
  • Simplify the process of creating complex data pipelines using transformations, aggregations, enrichment, cleansing, filtering, reformatting, lookups, and data type conversions
  • Apply knowledge of Pig in design patterns that deal with integration of Hadoop with other systems to enable multi-platform analytics
  • Comprehend design patterns and use Pig in cases related to complex analysis of pure structured data

In Detail

Pig Design Patterns is a comprehensive guide that will enable readers to readily use design patterns that simplify the creation of complex data pipelines in various stages of data management. This book focuses on using Pig in an enterprise context, bridging the gap between theoretical understanding and practical implementation. Each chapter contains a set of design patterns that pose and then solve technical challenges that are relevant to the enterprise use cases.

The book covers the journey of Big Data from the time it enters the enterprise to its eventual use in analytics, in the form of a report or a predictive model. By the end of the book, readers will appreciate Pig's real power in addressing each and every problem encountered when creating an analytics-based data product. Each design pattern comes with a suggested solution, analyzing the trade-offs of implementing the solution in a different way, explaining how the code works, and the results.

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