Download or read book Programming Elastic MapReduce written by Kevin Schmidt and published by "O'Reilly Media, Inc.". This book was released on 2013-12-10 with total page 264 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

Download or read book Programming Elastic MapReduce written by Kevin Schmidt and published by "O'Reilly Media, Inc.". This book was released on 2013-12-10 with total page 173 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

Download or read book Programming Elastic MapReduce written by Kevin Schmidt. Christopher Phillips and published by . This book was released on 2013 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Learning Big Data with Amazon Elastic MapReduce written by Amarkant Singh and published by . This book was released on 2014-10-10 with total page 242 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is aimed at developers and system administrators who want to learn about Big Data analysis using Amazon Elastic MapReduce. Basic Java programming knowledge is required. You should be comfortable with using command-line tools. Prior knowledge of AWS, API, and CLI tools is not assumed. Also, no exposure to Hadoop and MapReduce is expected.

Download or read book Functional Programming in C written by Oliver Sturm and published by John Wiley and Sons. This book was released on 2011-04-11 with total page 288 pages. Available in PDF, EPUB and Kindle. Book excerpt: Presents a guide to the features of C♯, covering such topics as functions, generics, iterators, currying, caching, order functions, sequences, monads, and MapReduce.

Download or read book Programming Hive written by Edward Capriolo and published by "O'Reilly Media, Inc.". This book was released on 2012-09-26 with total page 351 pages. Available in PDF, EPUB and Kindle. Book excerpt: Need to move a relational database application to Hadoop? This comprehensive guide introduces you to Apache Hive, Hadoop’s data warehouse infrastructure. You’ll quickly learn how to use Hive’s SQL dialect—HiveQL—to summarize, query, and analyze large datasets stored in Hadoop’s distributed filesystem. This example-driven guide shows you how to set up and configure Hive in your environment, provides a detailed overview of Hadoop and MapReduce, and demonstrates how Hive works within the Hadoop ecosystem. You’ll also find real-world case studies that describe how companies have used Hive to solve unique problems involving petabytes of data. Use Hive to create, alter, and drop databases, tables, views, functions, and indexes Customize data formats and storage options, from files to external databases Load and extract data from tables—and use queries, grouping, filtering, joining, and other conventional query methods Gain best practices for creating user defined functions (UDFs) Learn Hive patterns you should use and anti-patterns you should avoid Integrate Hive with other data processing programs Use storage handlers for NoSQL databases and other datastores Learn the pros and cons of running Hive on Amazon’s Elastic MapReduce

Download or read book Programming MapReduce with Scalding written by Antonios Chalkiopoulos and published by Packt Publishing Ltd. This book was released on 2014-06-25 with total page 225 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is an easy-to-understand, practical guide to designing, testing, and implementing complex MapReduce applications in Scala using the Scalding framework. It is packed with examples featuring log-processing, ad-targeting, and machine learning. This book is for developers who are willing to discover how to effectively develop MapReduce applications. Prior knowledge of Hadoop or Scala is not required; however, investing some time on those topics would certainly be beneficial.

Download or read book Web Scale Data Management for the Cloud written by Wolfgang Lehner and published by Springer Science & Business Media. This book was released on 2013-04-06 with total page 209 pages. Available in PDF, EPUB and Kindle. Book excerpt: The efficient management of a consistent and integrated database is a central task in modern IT and highly relevant for science and industry. Hardly any critical enterprise solution comes without any functionality for managing data in its different forms. Web-Scale Data Management for the Cloud addresses fundamental challenges posed by the need and desire to provide database functionality in the context of the Database as a Service (DBaaS) paradigm for database outsourcing. This book also discusses the motivation of the new paradigm of cloud computing, and its impact to data outsourcing and service-oriented computing in data-intensive applications. Techniques with respect to the support in the current cloud environments, major challenges, and future trends are covered in the last section of this book. A survey addressing the techniques and special requirements for building database services are provided in this book as well.

Download or read book Parallel R written by Q. Ethan McCallum and published by "O'Reilly Media, Inc.". This book was released on 2011-10-21 with total page 123 pages. Available in PDF, EPUB and Kindle. Book excerpt: It’s tough to argue with R as a high-quality, cross-platform, open source statistical software product—unless you’re in the business of crunching Big Data. This concise book introduces you to several strategies for using R to analyze large datasets, including three chapters on using R and Hadoop together. You’ll learn the basics of Snow, Multicore, Parallel, Segue, RHIPE, and Hadoop Streaming, including how to find them, how to use them, when they work well, and when they don’t. With these packages, you can overcome R’s single-threaded nature by spreading work across multiple CPUs, or offloading work to multiple machines to address R’s memory barrier. Snow: works well in a traditional cluster environment Multicore: popular for multiprocessor and multicore computers Parallel: part of the upcoming R 2.14.0 release R+Hadoop: provides low-level access to a popular form of cluster computing RHIPE: uses Hadoop’s power with R’s language and interactive shell Segue: lets you use Elastic MapReduce as a backend for lapply-style operations

Download or read book R High Performance Programming written by Aloysius Lim and published by Packt Publishing Ltd. This book was released on 2015-01-29 with total page 176 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is for programmers and developers who want to improve the performance of their R programs by making them run faster with large data sets or who are trying to solve a pesky performance problem.

Download or read book MapReduce Design Patterns written by Donald Miner and published by "O'Reilly Media, Inc.". This book was released on 2012-11-21 with total page 417 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

Download or read book Programming Hive written by Edward Capriolo and published by "O'Reilly Media, Inc.". This book was released on 2012-09-19 with total page 350 pages. Available in PDF, EPUB and Kindle. Book excerpt: Need to move a relational database application to Hadoop? This comprehensive guide introduces you to Apache Hive, Hadoop’s data warehouse infrastructure. You’ll quickly learn how to use Hive’s SQL dialect—HiveQL—to summarize, query, and analyze large datasets stored in Hadoop’s distributed filesystem. This example-driven guide shows you how to set up and configure Hive in your environment, provides a detailed overview of Hadoop and MapReduce, and demonstrates how Hive works within the Hadoop ecosystem. You’ll also find real-world case studies that describe how companies have used Hive to solve unique problems involving petabytes of data. Use Hive to create, alter, and drop databases, tables, views, functions, and indexes Customize data formats and storage options, from files to external databases Load and extract data from tables—and use queries, grouping, filtering, joining, and other conventional query methods Gain best practices for creating user defined functions (UDFs) Learn Hive patterns you should use and anti-patterns you should avoid Integrate Hive with other data processing programs Use storage handlers for NoSQL databases and other datastores Learn the pros and cons of running Hive on Amazon’s Elastic MapReduce

Download or read book Programming Pig written by Alan Gates and published by "O'Reilly Media, Inc.". This book was released on 2011-09-29 with total page 223 pages. Available in PDF, EPUB and Kindle. Book excerpt: This guide is an ideal learning tool and reference for Apache Pig, the open source engine for executing parallel data flows on Hadoop. With Pig, you can batch-process data without having to create a full-fledged application—making it easy for you to experiment with new datasets. Programming Pig introduces new users to Pig, and provides experienced users with comprehensive coverage on key features such as the Pig Latin scripting language, the Grunt shell, and User Defined Functions (UDFs) for extending Pig. If you need to analyze terabytes of data, this book shows you how to do it efficiently with Pig. Delve into Pig’s data model, including scalar and complex data types Write Pig Latin scripts to sort, group, join, project, and filter your data Use Grunt to work with the Hadoop Distributed File System (HDFS) Build complex data processing pipelines with Pig’s macros and modularity features Embed Pig Latin in Python for iterative processing and other advanced tasks Create your own load and store functions to handle data formats and storage mechanisms Get performance tips for running scripts on Hadoop clusters in less time

Download or read book Frank Kane s Taming Big Data with Apache Spark and Python written by Frank Kane and published by Packt Publishing Ltd. This book was released on 2017-06-30 with total page 289 pages. Available in PDF, EPUB and Kindle. Book excerpt: Frank Kane's hands-on Spark training course, based on his bestselling Taming Big Data with Apache Spark and Python video, now available in a book. Understand and analyze large data sets using Spark on a single system or on a cluster. About This Book Understand how Spark can be distributed across computing clusters Develop and run Spark jobs efficiently using Python A hands-on tutorial by Frank Kane with over 15 real-world examples teaching you Big Data processing with Spark Who This Book Is For If you are a data scientist or data analyst who wants to learn Big Data processing using Apache Spark and Python, this book is for you. If you have some programming experience in Python, and want to learn how to process large amounts of data using Apache Spark, Frank Kane's Taming Big Data with Apache Spark and Python will also help you. What You Will Learn Find out how you can identify Big Data problems as Spark problems Install and run Apache Spark on your computer or on a cluster Analyze large data sets across many CPUs using Spark's Resilient Distributed Datasets Implement machine learning on Spark using the MLlib library Process continuous streams of data in real time using the Spark streaming module Perform complex network analysis using Spark's GraphX library Use Amazon's Elastic MapReduce service to run your Spark jobs on a cluster In Detail Frank Kane's Taming Big Data with Apache Spark and Python is your companion to learning Apache Spark in a hands-on manner. Frank will start you off by teaching you how to set up Spark on a single system or on a cluster, and you'll soon move on to analyzing large data sets using Spark RDD, and developing and running effective Spark jobs quickly using Python. Apache Spark has emerged as the next big thing in the Big Data domain – quickly rising from an ascending technology to an established superstar in just a matter of years. Spark allows you to quickly extract actionable insights from large amounts of data, on a real-time basis, making it an essential tool in many modern businesses. Frank has packed this book with over 15 interactive, fun-filled examples relevant to the real world, and he will empower you to understand the Spark ecosystem and implement production-grade real-time Spark projects with ease. Style and approach Frank Kane's Taming Big Data with Apache Spark and Python is a hands-on tutorial with over 15 real-world examples carefully explained by Frank in a step-by-step manner. The examples vary in complexity, and you can move through them at your own pace.

Download or read book Network Programming and Automation Essentials written by Claus Topke and published by Packt Publishing Ltd. This book was released on 2023-04-07 with total page 296 pages. Available in PDF, EPUB and Kindle. Book excerpt: Unleash the power of automation by mastering network programming fundamentals using Python and Go best practices Purchase of the print or Kindle book includes a free PDF eBook Key Features Understand the fundamentals of network programming and automation Learn tips and tricks to transition from traditional networking to automated networks Solve everyday problems with automation frameworks in Python and Go Book Description Network programming and automation, unlike traditional networking, is a modern-day skill that helps in configuring, managing, and operating networks and network devices. This book will guide you with important information, helping you set up and start working with network programming and automation. With Network Programming and Automation Essentials, you'll learn the basics of networking in brief. You'll explore the network programming and automation ecosystem, learn about the leading programmable interfaces, and go through the protocols, tools, techniques, and technologies associated with network programming. You'll also master network automation using Python and Go with hands-on labs and real network emulation in this comprehensive guide. By the end of this book, you'll be well equipped to program and automate networks efficiently. What you will learn Understand the foundation of network programming Explore software-defined networks and related families Recognize the differences between Go and Python through comparison Leverage the best practices of Go and Python Create your own network automation testing framework using network emulation Acquire skills in using automation frameworks and strategies for automation Who this book is for This book is for network architects, network engineers, and software professionals looking to integrate programming into networks. Network engineers following traditional techniques can use this book to transition into modern-day network automation and programming. Familiarity with networking concepts is a prerequisite.

Download or read book Human Computer Interaction User Interface Design Development and Multimodality written by Masaaki Kurosu and published by Springer. This book was released on 2017-06-28 with total page 734 pages. Available in PDF, EPUB and Kindle. Book excerpt: The two-volume set LNCS 10271 and 10272 constitutes the refereed proceedings of the 19th International Conference on Human-Computer Interaction, HCII 2017, held in Vancouver, BC, Canada, in July 2017. The total of 1228 papers presented at the 15 colocated HCII 2017 conferences was carefully reviewed and selected from 4340 submissions. The papers address the latest research and development efforts and highlight the human aspects of design and use of computing systems. They cover the entire field of Human-Computer Interaction, addressing major advances in knowledge and effective use of computers in a variety of application areas. The papers included in this volume cover the following topics: HCI theory and education; HCI, innovation and technology acceptance; interaction design and evaluation methods; user interface development; methods, tools, and architectures; multimodal interaction; and emotions in HCI.

Download or read book Enterprise Data Workflows with Cascading written by Paco Nathan and published by "O'Reilly Media, Inc.". This book was released on 2013-07-11 with total page 170 pages. Available in PDF, EPUB and Kindle. Book excerpt: There is an easier way to build Hadoop applications. With this hands-on book, you’ll learn how to use Cascading, the open source abstraction framework for Hadoop that lets you easily create and manage powerful enterprise-grade data processing applications—without having to learn the intricacies of MapReduce. Working with sample apps based on Java and other JVM languages, you’ll quickly learn Cascading’s streamlined approach to data processing, data filtering, and workflow optimization. This book demonstrates how this framework can help your business extract meaningful information from large amounts of distributed data. Start working on Cascading example projects right away Model and analyze unstructured data in any format, from any source Build and test applications with familiar constructs and reusable components Work with the Scalding and Cascalog Domain-Specific Languages Easily deploy applications to Hadoop, regardless of cluster location or data size Build workflows that integrate several big data frameworks and processes Explore common use cases for Cascading, including features and tools that support them Examine a case study that uses a dataset from the Open Data Initiative