Mapreduce Design Patterns

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Mapreduce Design Patterns

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Author : Donald Miner
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2012-11-21

Mapreduce Design Patterns written by Donald Miner and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-11-21 with Computers categories.

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

Mapreduce Design Patterns

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Author : Donald Miner
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2012

Mapreduce Design Patterns written by Donald Miner and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012 with categories.

Mapreduce Design Patterns

DOWNLOAD
Author : Donald Miner
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2012-11-21

Mapreduce Design Patterns written by Donald Miner and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-11-21 with Computers categories.

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

Mapreduce Design Patterns

DOWNLOAD
Author : Donald Miner
language : en
Publisher:
Release Date : 2012

Mapreduce Design Patterns written by Donald Miner and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012 with Apache Hadoop categories.

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.

Data Intensive Text Processing With Mapreduce

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Author : Jimmy Lin
language : en
Publisher: Springer Nature
Release Date : 2022-05-31

Data Intensive Text Processing With Mapreduce written by Jimmy Lin and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-05-31 with Computers categories.

Our world is being revolutionized by data-driven methods: access to large amounts of data has generated new insights and opened exciting new opportunities in commerce, science, and computing applications. Processing the enormous quantities of data necessary for these advances requires large clusters, making distributed computing paradigms more crucial than ever. MapReduce is a programming model for expressing distributed computations on massive datasets and an execution framework for large-scale data processing on clusters of commodity servers. The programming model provides an easy-to-understand abstraction for designing scalable algorithms, while the execution framework transparently handles many system-level details, ranging from scheduling to synchronization to fault tolerance. This book focuses on MapReduce algorithm design, with an emphasis on text processing algorithms common in natural language processing, information retrieval, and machine learning. We introduce the notion of MapReduce design patterns, which represent general reusable solutions to commonly occurring problems across a variety of problem domains. This book not only intends to help the reader "think in MapReduce", but also discusses limitations of the programming model as well. Table of Contents: Introduction / MapReduce Basics / MapReduce Algorithm Design / Inverted Indexing for Text Retrieval / Graph Algorithms / EM Algorithms for Text Processing / Closing Remarks

Hadoop In Action

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Author : Chuck Lam
language : en
Publisher: Simon and Schuster
Release Date : 2010-11-30

Hadoop In Action written by Chuck Lam and has been published by Simon and Schuster this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010-11-30 with Computers categories.

Hadoop in Action teaches readers how to use Hadoop and write MapReduce programs. The intended readers are programmers, architects, and project managers who have to process large amounts of data offline. Hadoop in Action will lead the reader from obtaining a copy of Hadoop to setting it up in a cluster and writing data analytic programs. The book begins by making the basic idea of Hadoop and MapReduce easier to grasp by applying the default Hadoop installation to a few easy-to-follow tasks, such as analyzing changes in word frequency across a body of documents. The book continues through the basic concepts of MapReduce applications developed using Hadoop, including a close look at framework components, use of Hadoop for a variety of data analysis tasks, and numerous examples of Hadoop in action. Hadoop in Action will explain how to use Hadoop and present design patterns and practices of programming MapReduce. MapReduce is a complex idea both conceptually and in its implementation, and Hadoop users are challenged to learn all the knobs and levers for running Hadoop. This book takes you beyond the mechanics of running Hadoop, teaching you to write meaningful programs in a MapReduce framework. This book assumes the reader will have a basic familiarity with Java, as most code examples will be written in Java. Familiarity with basic statistical concepts (e.g. histogram, correlation) will help the reader appreciate the more advanced data processing examples. Purchase of the print book comes with an offer of a free PDF, ePub, and Kindle eBook from Manning. Also available is all code from the book.

Programming Elastic Mapreduce

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Author : Kevin Schmidt
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2013-12-10

Programming Elastic Mapreduce written by Kevin Schmidt and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-12-10 with Computers categories.

Although you don’t need a large computing infrastructure to process massive amounts of data with Apache Hadoop, it can still be difficult to get started. This practical guide shows you how to quickly launch data analysis projects in the cloud by using Amazon Elastic MapReduce (EMR), the hosted Hadoop framework in Amazon Web Services (AWS). Authors Kevin Schmidt and Christopher Phillips demonstrate best practices for using EMR and various AWS and Apache technologies by walking you through the construction of a sample MapReduce log analysis application. Using code samples and example configurations, you’ll learn how to assemble the building blocks necessary to solve your biggest data analysis problems. Get an overview of the AWS and Apache software tools used in large-scale data analysis Go through the process of executing a Job Flow with a simple log analyzer Discover useful MapReduce patterns for filtering and analyzing data sets Use Apache Hive and Pig instead of Java to build a MapReduce Job Flow Learn the basics for using Amazon EMR to run machine learning algorithms Develop a project cost model for using Amazon EMR and other AWS tools

Data Algorithms

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Author : Mahmoud Parsian
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2015-07-13

Data Algorithms written by Mahmoud Parsian and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-07-13 with Computers categories.

If you are ready to dive into the MapReduce framework for processing large datasets, this practical book takes you step by step through the algorithms and tools you need to build distributed MapReduce applications with Apache Hadoop or Apache Spark. Each chapter provides a recipe for solving a massive computational problem, such as building a recommendation system. You’ll learn how to implement the appropriate MapReduce solution with code that you can use in your projects. Dr. Mahmoud Parsian covers basic design patterns, optimization techniques, and data mining and machine learning solutions for problems in bioinformatics, genomics, statistics, and social network analysis. This book also includes an overview of MapReduce, Hadoop, and Spark. Topics include: Market basket analysis for a large set of transactions Data mining algorithms (K-means, KNN, and Naive Bayes) Using huge genomic data to sequence DNA and RNA Naive Bayes theorem and Markov chains for data and market prediction Recommendation algorithms and pairwise document similarity Linear regression, Cox regression, and Pearson correlation Allelic frequency and mining DNA Social network analysis (recommendation systems, counting triangles, sentiment analysis)

Mapreduce Design Patterns

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Author : Manuel Bultman
language : en
Publisher: CreateSpace
Release Date : 2014-11-26

Mapreduce Design Patterns written by Manuel Bultman and has been published by CreateSpace this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-11-26 with categories.

Introduction Data warehousing is a success, judging by its 25 year history of use across all industries. Business intelligence met the needs it was designed for: to give non-technical people within the organization access to important, shared data. During the same period that data warehousing and BI matured, the automation and instrumenting of almost all processes and activities changed the data landscape in most companies. Where there were only a few applications and minimal monitoring 25 years ago, there is ubiquitous computing and data available about every activity today. Data warehouses have not been able to keep up with business demands for new sources of information, new types of data, more complex analysis and greater speed. Companies can put this data to use in countless ways, but for most it remains uncollected or unused, locked away in silos within IT. There has been a gradual maturing of data use in organizations. In the early days of BI it was enough to provide access to core financial and customer transactions. Better access enabled process changes, and these led to the need for more data and more varied uses of information. These changes put increasing strain on information processing and delivery capabilities that were designed under assumptions of stability and common use. Most companies now have a backlog of new data and analysis requests that BI groups are struggling to meet. Big data is not simply about growing data volumes - it's also about the fact that the data being collected today is different in ways that make it unwieldy for conventional databases and BI tools. Big data is also about new technologies that were developed to support the storage, retrieval and processing of this new data. The technologies originated in the world of web applications and internet-based companies, but they are now spreading into enterprise applications of all sorts. New technology coupled with new data enables new practices like real-time monitoring of operations across retail channels, supply chain practices at finer grain and faster speed, and analysis of customers at the level of individual activities and behaviors. Until recently, large scale data collection and analysis capabilities like these would have required a Wal-Mart sized investment, limiting them to large organizations. These capabilities are now available to all, regardless of company size or budget. This is creating a rush to adopt big data technologies. As the use of big data grows, the need for data management will grow. Many organizations already struggle to manage existing data. Big data adds complexity, which will only increase the challenge. The combination of new data and new technology requires new data management capabilities and processes to capture the promised long-term value. Wal-Mart handles more than a million customer transactions each hour and imports those into databases estimated to contain more than 2.5 petabytes of data. Radio frequency identification (RFID) systems used by retailers and others can generate 100 to 1,000 times the data of conventional bar code systems. Facebook handles more than 250 million photo uploads and the interactions of 800 million active users with more than 900 million objects (pages, groups, etc.) - each day. More than 5 billion people are calling, texting, tweeting and browsing on mobile phones worldwide. Organizations are inundated with data - terabytes and petabytes of it. To put it in context, 1 terabyte contains 2,000 hours of CD-quality music and 10 terabytes could store the entire US Library of Congress print collection. Exabytes, zettabytes and yottabytes definitely are on the horizon . Data is pouring in from every conceivable direction: from operational and transactional systems, from scanning and facilities management systems, from inbound and outbound customer contact points, from mobile media and the Web .

Design Patterns For Cloud Native Applications

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Author : Kasun Indrasiri
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2021-05-17

Design Patterns For Cloud Native Applications written by Kasun Indrasiri and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-05-17 with Computers categories.

With the immense cost savings and scalability the cloud provides, the rationale for building cloud native applications is no longer in question. The real issue is how. With this practical guide, developers will learn about the most commonly used design patterns for building cloud native applications using APIs, data, events, and streams in both greenfield and brownfield development. You'll learn how to incrementally design, develop, and deploy large and effective cloud native applications that you can manage and maintain at scale with minimal cost, time, and effort. Authors Kasun Indrasiri and Sriskandarajah Suhothayan highlight use cases that effectively demonstrate the challenges you might encounter at each step. Learn the fundamentals of cloud native applications Explore key cloud native communication, connectivity, and composition patterns Learn decentralized data management techniques Use event-driven architecture to build distributed and scalable cloud native applications Explore the most commonly used patterns for API management and consumption Examine some of the tools and technologies you'll need for building cloud native systems