Pig Design Patterns.
Title:
Pig Design Patterns.
Author:
Pasupuleti, Pradeep.
ISBN:
9781783285563
Personal Author:
Physical Description:
1 online resource (329 pages)
Contents:
Pig Design Patterns -- Table of Contents -- Pig Design Patterns -- Credits -- Foreword -- About the Author -- Acknowledgments -- About the Reviewers -- www.PacktPub.com -- Support files, eBooks, discount offers and more -- Why Subscribe? -- Free Access for Packt account holders -- Preface -- What this book covers -- Motivation for this book -- What you need for this book -- Who this book is for -- Conventions -- Reader feedback -- Customer support -- Downloading the example code -- Third-party libraries -- Datasets -- Errata -- Piracy -- Questions -- 1. Setting the Context for Design Patterns in Pig -- Understanding design patterns -- The scope of design patterns in Pig -- Hadoop demystified - a quick reckoner -- The enterprise context -- Common challenges of distributed systems -- The advent of Hadoop -- Hadoop under the covers -- Understanding the Hadoop Distributed File System -- HDFS design goals -- Working of HDFS -- Understanding MapReduce -- Understanding how MapReduce works -- The MapReduce internals -- Pig - a quick intro -- Understanding the rationale of Pig -- Understanding the relevance of Pig in the enterprise -- Working of Pig - an overview -- Firing up Pig -- The use case -- Code listing -- The dataset -- Understanding Pig through the code -- Pig's extensibility -- Operators used in code -- The EXPLAIN operator -- Understanding Pig's data model -- Primitive types -- Complex types -- The relevance of schemas -- Summary -- 2. Data Ingest and Egress Patterns -- The context of data ingest and egress -- Types of data in the enterprise -- Ingest and egress patterns for multistructured data -- Considerations for log ingestion -- The Apache log ingestion pattern -- Background -- Motivation -- Use cases -- Pattern implementation -- Code snippets -- Code for the CommonLogLoader class -- Code for the CombinedLogLoader class -- Results.
Additional information -- The Custom log ingestion pattern -- Background -- Motivation -- Use cases -- Pattern implementation -- Code snippets -- Results -- Additional information -- The image ingress and egress pattern -- Background -- Motivation -- Use cases -- Pattern implementation -- The image Ingress Implementation -- The image egress implementation -- Code snippets -- The image ingress -- Pig script -- Image to a sequence UDF snippet -- The image egress -- Pig script -- Sequence to an image UDF -- Results -- Additional information -- The ingress and egress patterns for the NoSQL data -- MongoDB ingress and egress patterns -- Background -- Motivation -- Use cases -- Pattern implementation -- The ingress implementation -- The egress implementation -- Code snippets -- The ingress code -- The egress code -- Results -- Additional information -- The HBase ingress and egress pattern -- Background -- Motivation -- Use cases -- Pattern implementation -- The ingress implementation -- The egress implementation -- Code snippets -- The ingress code -- The egress code -- Results -- Additional information -- The ingress and egress patterns for structured data -- The Hive ingress and egress patterns -- Background -- Motivation -- Use cases -- Pattern implementation -- The ingress implementation -- The egress implementation -- Code snippets -- The ingress Code -- Importing data using RCFile -- Importing data using HCatalog -- The egress code -- Results -- Additional information -- The ingress and egress patterns for semi-structured data -- The mainframe ingestion pattern -- Background -- Motivation -- Use cases -- Pattern implementation -- Code snippets -- Results -- Additional information -- XML ingest and egress patterns -- Background -- Motivation -- Motivation for ingesting raw XML -- Motivation for ingesting binary XML -- Motivation for egression of XML.
Use cases -- Pattern implementation -- The implementation of the XML raw ingestion -- The implementation of the XML binary ingestion -- Code snippets -- The XML raw ingestion code -- The XML binary ingestion code -- The XML egress code -- Pig script -- The XML storage -- Results -- Additional information -- JSON ingress and egress patterns -- Background -- Motivation -- Use cases -- Pattern implementation -- The ingress implementation -- The egress implementation -- Code snippets -- The ingress code -- The code for simple JSON -- The code for nested JSON -- The egress code -- Results -- Additional information -- Summary -- 3. Data Profiling Patterns -- Data profiling for Big Data -- Big Data profiling dimensions -- Sampling considerations for profiling Big Data -- Sampling support in Pig -- Rationale for using Pig in data profiling -- The data type inference pattern -- Background -- Motivation -- Use cases -- Pattern implementation -- Code snippets -- Pig script -- Java UDF -- Results -- Additional information -- The basic statistical profiling pattern -- Background -- Motivation -- Use cases -- Pattern implementation -- Code snippets -- Pig script -- Macro -- Results -- Additional information -- The pattern-matching pattern -- Background -- Motivation -- Use cases -- Pattern implementation -- Code snippets -- Pig script -- Macro -- Results -- Additional information -- The string profiling pattern -- Background -- Motivation -- Use cases -- Pattern implementation -- Code snippets -- Pig script -- Macro -- Results -- Additional information -- The unstructured text profiling pattern -- Background -- Motivation -- Use cases -- Pattern implementation -- Code snippets -- Pig script -- Java UDF for stemming -- Java UDF for generating TF-IDF -- Results -- Additional information -- Summary -- 4. Data Validation and Cleansing Patterns.
Data validation and cleansing for Big Data -- Choosing Pig for validation and cleansing -- The constraint validation and cleansing design pattern -- Background -- Motivation -- Use cases -- Pattern implementation -- Code snippets -- Results -- Additional information -- The regex validation and cleansing design pattern -- Background -- Motivation -- Use cases -- Pattern implementation -- Code snippets -- Results -- Additional information -- The corrupt data validation and cleansing design pattern -- Background -- Motivation -- Use cases -- Pattern implementation -- Code snippets -- Results -- Additional information -- The unstructured text data validation and cleansing design pattern -- Background -- Motivation -- Use cases -- Pattern implementation -- Code snippets -- Results -- Additional information -- Summary -- 5. Data Transformation Patterns -- Data transformation processes -- The structured-to-hierarchical transformation pattern -- Background -- Motivation -- Use cases -- Pattern implementation -- Code snippets -- Results -- Additional information -- The data normalization pattern -- Background -- Motivation -- Use cases -- Pattern implementation -- Code snippets -- Results -- Additional information -- The data integration pattern -- Background -- Motivation -- Use cases -- Pattern implementation -- Code snippets -- Results -- Additional information -- The aggregation pattern -- Background -- Motivation -- Use cases -- Pattern implementation -- Code snippets -- Results -- Additional information -- The data generalization pattern -- Background -- Motivation -- Use cases -- Pattern implementation -- Code snippets -- Results -- Additional information -- Summary -- 6. Understanding Data Reduction Patterns -- Data reduction - a quick introduction -- Data reduction considerations for Big Data.
Dimensionality reduction - the Principal Component Analysis design pattern -- Background -- Motivation -- Use cases -- Pattern implementation -- Limitations of PCA implementation -- Code snippets -- Results -- Additional information -- Numerosity reduction - the histogram design pattern -- Background -- Motivation -- Use cases -- Pattern implementation -- Code snippets -- Results -- Additional information -- Numerosity reduction - sampling design pattern -- Background -- Motivation -- Use cases -- Pattern implementation -- Code snippets -- Results -- Additional information -- Numerosity reduction - clustering design pattern -- Background -- Motivation -- Use cases -- Pattern implementation -- Code snippets -- Results -- Additional information -- Summary -- 7. Advanced Patterns and Future Work -- The clustering pattern -- Background -- Motivation -- Use cases -- Pattern implementation -- Code snippets -- Results -- Additional information -- The topic discovery pattern -- Background -- Motivation -- Use cases -- Pattern implementation -- Code snippets -- Results -- Additional information -- The natural language processing pattern -- Background -- Motivation -- Use cases -- Pattern implementation -- Code snippets -- Results -- Additional information -- The classification pattern -- Background -- Motivation -- Use cases -- Pattern implementation -- Code snippets -- Results -- Additional information -- Future trends -- Emergence of data-driven patterns -- The emergence of solution-driven patterns -- Patterns addressing programmability constraints -- Summary -- Index.
Abstract:
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 data pipelines in various stages of data management. The power and flexibility of Pig will be apparent to the reader when solving complex problems while ingesting data from multiple data sources, cleansing unstructured data, profiling data, validating, transforming, and egressing large volumes of data. 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. Throughout this journey, Pig is the essential catalyst, proven to speed up data movement through successive steps in the data management life cycle. These steps help readers acquire the data, understand the qualitative profile of the data, cleanse it to make it suitable for the analytics, transform it by joining with other sources, reduce it if necessary, and then egress it to other subsystems. 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. Readers will come to understand that the design patterns described in this book are not tightly confined to problem-to-solution mapping implemented in Pig; 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.
Local Note:
Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2017. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.
Genre:
Electronic Access:
Click to View