Packt is proud to introduce Pig Design Patterns, a practical guide that will help readers use Pig to design end-to-end Big Data systems. The print book is 310 pages long and costs $54.99, and is also available in all the popular eBook formats for $28.04.
About the author:
Pradeep Pasupuleti has over 16 years of experience in architecting and developing distributed and real-time data-driven systems. He has performed roles in technology consulting, advising Fortune 500 companies on their Big Data strategy, product management, systems architecture, conflict resolution, chaos and nonlinear dynamics, international policy, high-performance computing, advanced statistical techniques, risk management, visualization of high dimensional data, human-computer interaction, machine learning, information retrieval, and data mining. He has created COEs (Center of Excellence) to provide quick wins with data products that analyze high-dimensional multistructured data using scalable natural language processing and deep learning techniques.
Pig is a platform that analyzes large data sets consisting of a high-level language for expressing data analysis programs, coupled with infrastructure for evaluating these programs.
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 will help readers understand 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.
Pig Design Patterns has everything a reader needs to bridge the gap between theoretical understanding and practical implementation by using Pig in an enterprise context. Readers will also learn how to address each and every problem encountered when creating an analytics-based data product.
Pig Design Patterns covers the following topics:
Chapter 1: Setting the Context for Design Patterns in Pig
Chapter 2: Data Ingest and Egress Patterns
Chapter 3: Data Profiling Patterns
Chapter 4: Data Validation and Cleansing Patterns
Chapter 5: Data Transformation Patterns
Chapter 6: Understanding Data Reduction Patterns
Chapter 7: Advanced Patterns and Future Work
Experienced developers who are keen to solve problems of data ingestion, profiling, cleansing, transforming, and egressing data encountered in enterprises will find this an invaluable guide.For more information, please visit the book's page at: http://www.packtpub.com/pig-design-patterns/book
|Pig Design Patterns|
|Implement a hands-on programming approach using design patterns
For more information, please visit: http://www.packtpub.com/pig-design-patterns/book