Pig Design Patterns
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Pig Design Patterns
Author: Pradeep Pasupuleti
language: en
Publisher: Packt Publishing Ltd
Release Date: 2014-04-17
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.
MapReduce Design Patterns
Author: Donald Miner
language: en
Publisher: "O'Reilly Media, Inc."
Release Date: 2012-11-21
Until now, design patterns for the MapReduce framework have been scattered among various research papers, blogs, and books. This handy guide brings together a unique collection of valuable MapReduce patterns that will save you time and effort regardless of the domain, language, or development framework you’re using. Each pattern is explained in context, with pitfalls and caveats clearly identified to help you avoid common design mistakes when modeling your big data architecture. This book also provides a complete overview of MapReduce that explains its origins and implementations, and why design patterns are so important. All code examples are written for Hadoop. Summarization patterns: get a top-level view by summarizing and grouping data Filtering patterns: view data subsets such as records generated from one user Data organization patterns: reorganize data to work with other systems, or to make MapReduce analysis easier Join patterns: analyze different datasets together to discover interesting relationships Metapatterns: piece together several patterns to solve multi-stage problems, or to perform several analytics in the same job Input and output patterns: customize the way you use Hadoop to load or store data "A clear exposition of MapReduce programs for common data processing patterns—this book is indespensible for anyone using Hadoop." --Tom White, author of Hadoop: The Definitive Guide
Efficient Data Processing with Apache Pig
"Efficient Data Processing with Apache Pig" Efficient Data Processing with Apache Pig is the definitive guide to mastering high-performance data transformation and pipeline design in today’s complex big data landscape. The book opens with a thorough examination of Apache Pig’s evolution, architectural foundations, and its crucial role within distributed data ecosystems. Readers gain a strategic perspective on where Pig excels compared to frameworks like MapReduce, Hive, and Spark, alongside practical guidance for deploying robust, enterprise-grade environments that prioritize scalability, multi-tenancy, and production resilience. Spanning fundamental data modeling practices, advanced Pig Latin techniques, and deep dives into resource optimization, this book is tailored for engineers, architects, and data professionals seeking practical strategies for building efficient, reliable pipelines. Each chapter balances conceptual clarity with technical depth—exploring schema evolution, advanced joins, aggregation patterns, modular scripting, and the intricacies of performance tuning. Readers also benefit from comprehensive coverage of extending Pig with custom UDFs, integrating with external data sources, and the nuances of workflow orchestration across Oozie, Airflow, and cloud-native platforms. The book moves beyond code and configuration, addressing critical considerations in security, compliance, and data governance—from authentication and encryption to auditing and lifecycle management. It concludes with actionable frameworks for migration, modernization, and hybrid architectures, coupled with future-focused discussions on AI integration, the evolving open-source ecosystem, and innovative real-world use cases at scale. Efficient Data Processing with Apache Pig is both a practical reference and an indispensable roadmap for leveraging Pig to its full potential in modern data environments.