Simplify Hadoop programming to create complex end-to-end Enterprise Big Data solutions with Pig Overview 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's capabilities to utilize Pig and create their own design patterns wherever applicable 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 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