Pig Design Patterns

Pig Design Patterns
Pig Design Patterns is published by in April 2014. This book has 310 pages in English, ISBN-13 9781783285556.

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

What you will learn from this book

  • Understand Pig’s relevance in an enterprise context
  • Use Pig in design patterns that enable the 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 the knowledge of Pig in design patterns that deal with integration of Hadoop with other systems to enable multi-platform analytics
  • Comprehend the design patterns and use Pig in cases related to complex analysis of pure structured data

Approach

A comprehensive practical guide that walks you through the multiple stages of data management in enterprise and gives you numerous design patterns with appropriate code examples to solve frequent problems in each of these stages. The chapters are organized to mimick the sequential data flow evidenced in Analytics platforms, but they can also be read independently to solve a particular group of problems in the Big Data life cycle.

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.

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