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Author: Kevin Schmidt
Publisher: "O'Reilly Media, Inc."
ISBN: 1449364055
Category : Computers
Languages : en
Pages : 173
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Book Description
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
Author: Kevin Schmidt
Publisher: "O'Reilly Media, Inc."
ISBN: 1449364055
Category : Computers
Languages : en
Pages : 173
Get Book Here
Book Description
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
Author: Kevin Schmidt
Publisher: O'Reilly Media
ISBN: 9781449363628
Category : Computers
Languages : en
Pages : 155
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Book Description
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
Author: Edward Capriolo
Publisher: "O'Reilly Media, Inc."
ISBN: 1449319335
Category : Computers
Languages : en
Pages : 351
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Book Description
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
Author: Edward Capriolo
Publisher: "O'Reilly Media, Inc."
ISBN: 1449326986
Category : Computers
Languages : en
Pages : 350
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Book Description
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
Author: Wolfgang Lehner
Publisher: Springer Science & Business Media
ISBN: 1461468566
Category : Computers
Languages : en
Pages : 209
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Book Description
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.
Author: Amarkant Singh
Publisher:
ISBN: 9781782173434
Category : Computers
Languages : en
Pages : 242
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Book Description
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.
Author: Donald Miner
Publisher: "O'Reilly Media, Inc."
ISBN: 1449341985
Category : Computers
Languages : en
Pages : 417
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Book Description
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
Author: Frank Kane
Publisher: Packt Publishing Ltd
ISBN: 1787288307
Category : Computers
Languages : en
Pages : 289
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Book Description
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.
Author: Claus Topke
Publisher: Packt Publishing Ltd
ISBN: 1803240156
Category : Computers
Languages : en
Pages : 296
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Book Description
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.
Author: Ethan McCallum
Publisher: "O'Reilly Media, Inc."
ISBN: 1449309925
Category : Computers
Languages : en
Pages : 123
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Book Description
R is a wonderful thing, indeed: in recent years this free, open-source product has become a popular toolkit for statistical analysis and programming. Two of R's limitations -- that it is single-threaded and memory-bound -- become especially troublesome in the current era of large-scale data analysis. It's possible to break past these boundaries by putting R on the parallel path. Parallel R will describe how to give R parallel muscle. Coverage will include stalwarts such as snow and multicore, and also newer techniques such as Hadoop and Amazon's cloud computing platform.
