Pig design patterns : simplify Hadoop programming to create complex end-to-end enterprise big data solutions with Pig

Simplify Hadoop programming to create complex end-to-end Enterprise Big Data solutions with PigOverviewQuickly understand how to use Pig to design end-to-end Big Data systemsImplement a hands-on programming approach using design patterns to solve commonly occurring enterprise Big Data challengesEnhances userss capabilities to utilize Pig and create their own design patterns wherever applicableIn DetailPig 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 Pigs 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 resultsWhat you will learn from this bookUnderstand Pigs relevance in an enterprise contextUse Pig in design patterns that enable the data movement across platforms during and after analytical processingSee how Pig can co-exist with other components of the Hadoop ecosystem to create Big Data solutions using design patternsSimplify the process of creating complex data pipelines using transformations, aggregations, enrichment, cleansing, filtering, reformatting, lookups, and data type conversionsApply the knowledge of Pig in design patterns that deal with integration of Hadoop with other systems to enable multi-platform analyticsComprehend the design patterns and use Pig in cases related to complex analysis of pure structured data

In DetailPig 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.

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.

In DetailPig 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. 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 Simplify Hadoop programming to create complex end-to-end Enterprise Big Data solutions with Pig 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. 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 Simplify Hadoop programming to create complex endtoend Enterprise Big Data solutions with Pig 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. 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. 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. If you are an 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. Pig makes Hadoop programming simple, intuitive, and fun to work with. It removes the complexity from Map Reduce programming by giving the programmer immense power through its flexibility. What used to be extremely lengthy and intricate code written in other high level languages can now be written in almost one tenth of the size using its easy to understand constructs. Pig has proven to be the easiest way to learn how to program Hadoop clusters, as evidenced by its widespread adoption. This comprehensive guide enables readers to readily use design patterns to simplify the creation of complex da