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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…mehr

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Produktbeschreibung
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.ApproachA 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 forThe 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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Autorenporträt
Pradeep Pasupuleti has over 17 years of experience in architecting and developing distributed and real-time data-driven systems. Currently, he focuses on developing robust data platforms and data products that are fuelled by scalable machine-learning algorithms, and delivering value to customers in addressing business problems by applying his deep technical insights.Pradeep founded Datatma expressly to humanize Big Data, simplify it, and unravel new value on a previously unimaginable scale in economy and scope. He has created COE (Centers of Excellence) to provide quick wins with data products that analyze high-dimensional multistructured data using scalable natural language processing and deep learning techniques. He has performed roles in technology consulting and advising Fortune 500 companies.Beulah Salome Purra has over 11 years of experience and specializes in building large-scale distributed systems. Her core expertise lies in working on Big Data Analytics. In her current role at ATMECS, her focus is on building robust and scalable data products that extract value from the organization's huge data assets.