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Produkte zum Begriff Hadoop:
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Data Munging with Hadoop
The Example-Rich, Hands-On Guide to Data Munging with Apache HadoopTM Data scientists spend much of their time “munging” data: handling day-to-day tasks such as data cleansing, normalization, aggregation, sampling, and transformation. These tasks are both critical and surprisingly interesting. Most important, they deepen your understanding of your data’s structure and limitations: crucial insight for improving accuracy and mitigating risk in any analytical project. Now, two leading Hortonworks data scientists, Ofer Mendelevitch and Casey Stella, bring together powerful, practical insights for effective Hadoop-based data munging of large datasets. Drawing on extensive experience with advanced analytics, the authors offer realistic examples that address the common issues you’re most likely to face. They describe each task in detail, presenting example code based on widely used tools such as Pig, Hive, and Spark. This concise, hands-on eBook is valuable for every data scientist, data engineer, and architect who wants to master data munging: not just in theory, but in practice with the field’s #1 platform–Hadoop. Coverage includes A framework for understanding the various types of data quality checks, including cell-based rules, distribution validation, and outlier analysis Assessing tradeoffs in common approaches to imputing missing values Implementing quality checks with Pig or Hive UDFs Transforming raw data into “feature matrix” format for machine learning algorithms Choosing features and instances Implementing text features via “bag-of-words” and NLP techniques Handling time-series data via frequency- or time-domain methods Manipulating feature values to prepare for modeling Data Munging with Hadoop is part of a larger, forthcoming work entitled Data Science Using Hadoop. To be notified when the larger work is available, register your purchase of Data Munging with Hadoop at informit.com/register and check the box “I would like to hear from InformIT and its family of brands about products and special offers.”
Preis: 4.27 € | Versand*: 0 € -
Data Munging with Hadoop
The Example-Rich, Hands-On Guide to Data Munging with Apache HadoopTM Data scientists spend much of their time “munging” data: handling day-to-day tasks such as data cleansing, normalization, aggregation, sampling, and transformation. These tasks are both critical and surprisingly interesting. Most important, they deepen your understanding of your data’s structure and limitations: crucial insight for improving accuracy and mitigating risk in any analytical project. Now, two leading Hortonworks data scientists, Ofer Mendelevitch and Casey Stella, bring together powerful, practical insights for effective Hadoop-based data munging of large datasets. Drawing on extensive experience with advanced analytics, the authors offer realistic examples that address the common issues you’re most likely to face. They describe each task in detail, presenting example code based on widely used tools such as Pig, Hive, and Spark. This concise, hands-on eBook is valuable for every data scientist, data engineer, and architect who wants to master data munging: not just in theory, but in practice with the field’s #1 platform–Hadoop. Coverage includes A framework for understanding the various types of data quality checks, including cell-based rules, distribution validation, and outlier analysis Assessing tradeoffs in common approaches to imputing missing values Implementing quality checks with Pig or Hive UDFs Transforming raw data into “feature matrix” format for machine learning algorithms Choosing features and instances Implementing text features via “bag-of-words” and NLP techniques Handling time-series data via frequency- or time-domain methods Manipulating feature values to prepare for modeling Data Munging with Hadoop is part of a larger, forthcoming work entitled Data Science Using Hadoop. To be notified when the larger work is available, register your purchase of Data Munging with Hadoop at informit.com/register and check the box “I would like to hear from InformIT and its family of brands about products and special offers.”
Preis: 5.34 € | Versand*: 0 € -
Virtualizing Hadoop: How to Install, Deploy, and Optimize Hadoop in a Virtualized Architecture
Plan and Implement Hadoop Virtualization for Maximum Performance, Scalability, and Business Agility Enterprises running Hadoop must absorb rapid changes in big data ecosystems, frameworks, products, and workloads. Virtualized approaches can offer important advantages in speed, flexibility, and elasticity. Now, a world-class team of enterprise virtualization and big data experts guide you through the choices, considerations, and tradeoffs surrounding Hadoop virtualization. The authors help you decide whether to virtualize Hadoop, deploy Hadoop in the cloud, or integrate conventional and virtualized approaches in a blended solution. First, Virtualizing Hadoop reviews big data and Hadoop from the standpoint of the virtualization specialist. The authors demystify MapReduce, YARN, and HDFS and guide you through each stage of Hadoop data management. Next, they turn the tables, introducing big data experts to modern virtualization concepts and best practices. Finally, they bring Hadoop and virtualization together, guiding you through the decisions you’ll face in planning, deploying, provisioning, and managing virtualized Hadoop. From security to multitenancy to day-to-day management, you’ll find reliable answers for choosing your best Hadoop strategy and executing it. Coverage includes the following: • Reviewing the frameworks, products, distributions, use cases, and roles associated with Hadoop • Understanding YARN resource management, HDFS storage, and I/O • Designing data ingestion, movement, and organization for modern enterprise data platforms • Defining SQL engine strategies to meet strict SLAs • Considering security, data isolation, and scheduling for multitenant environments • Deploying Hadoop as a service in the cloud • Reviewing the essential concepts, capabilities, and terminology of virtualization • Applying current best practices, guidelines, and key metrics for Hadoop virtualization • Managing multiple Hadoop frameworks and products as one unified system • Virtualizing master and worker nodes to maximize availability and performance • Installing and configuring Linux for a Hadoop environment
Preis: 19.25 € | Versand*: 0 € -
Virtualizing Hadoop: How to Install, Deploy, and Optimize Hadoop in a Virtualized Architecture
Plan and Implement Hadoop Virtualization for Maximum Performance, Scalability, and Business Agility Enterprises running Hadoop must absorb rapid changes in big data ecosystems, frameworks, products, and workloads. Virtualized approaches can offer important advantages in speed, flexibility, and elasticity. Now, a world-class team of enterprise virtualization and big data experts guide you through the choices, considerations, and tradeoffs surrounding Hadoop virtualization. The authors help you decide whether to virtualize Hadoop, deploy Hadoop in the cloud, or integrate conventional and virtualized approaches in a blended solution. First, Virtualizing Hadoop reviews big data and Hadoop from the standpoint of the virtualization specialist. The authors demystify MapReduce, YARN, and HDFS and guide you through each stage of Hadoop data management. Next, they turn the tables, introducing big data experts to modern virtualization concepts and best practices. Finally, they bring Hadoop and virtualization together, guiding you through the decisions you’ll face in planning, deploying, provisioning, and managing virtualized Hadoop. From security to multitenancy to day-to-day management, you’ll find reliable answers for choosing your best Hadoop strategy and executing it. Coverage includes the following: • Reviewing the frameworks, products, distributions, use cases, and roles associated with Hadoop • Understanding YARN resource management, HDFS storage, and I/O • Designing data ingestion, movement, and organization for modern enterprise data platforms • Defining SQL engine strategies to meet strict SLAs • Considering security, data isolation, and scheduling for multitenant environments • Deploying Hadoop as a service in the cloud • Reviewing the essential concepts, capabilities, and terminology of virtualization • Applying current best practices, guidelines, and key metrics for Hadoop virtualization • Managing multiple Hadoop frameworks and products as one unified system • Virtualizing master and worker nodes to maximize availability and performance • Installing and configuring Linux for a Hadoop environment
Preis: 25.67 € | Versand*: 0 €
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Was ist der Unterschied zwischen Rhythmus, Melodie und Harmonie?
Rhythmus bezieht sich auf die zeitliche Organisation von Klängen und betont die regelmäßige Abfolge von betonten und unbetonten Noten. Melodie bezieht sich auf die Abfolge von Tönen, die eine musikalische Linie bilden und eine bestimmte Tonhöhe haben. Harmonie bezieht sich auf die vertikale Anordnung von Tönen und beschreibt, wie sie zusammenklingen und welche Akkorde oder Harmonien gebildet werden.
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Wie kann man verschiedene musikalische Elemente wie Melodie, Rhythmus und Harmonie kombinieren, um eine beeindruckende Komposition zu erstellen?
Man kann verschiedene musikalische Elemente kombinieren, indem man eine Melodie mit einem passenden Rhythmus und harmonischen Akkorden verbindet. Durch die richtige Balance und Abstimmung dieser Elemente entsteht eine harmonische und ansprechende Komposition. Es ist wichtig, kreativ zu sein und experimentierfreudig zu bleiben, um ein einzigartiges und beeindruckendes Musikstück zu schaffen.
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Wie können verschiedene musikalische Elemente wie Melodie, Harmonie und Rhythmus effektiv miteinander kombiniert werden, um eine gelungene Komposition zu erschaffen?
Durch sorgfältige Auswahl und Abstimmung der einzelnen Elemente können Melodie, Harmonie und Rhythmus harmonisch miteinander verschmelzen. Die Verwendung von Kontrasten und Variationen in den Elementen kann Spannung und Interesse erzeugen. Eine klare Struktur und ein roter Faden helfen dabei, die verschiedenen Elemente zu einem stimmigen Gesamtbild zusammenzufügen.
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Wie können verschiedene musikalische Elemente wie Melodie, Harmonie und Rhythmus miteinander kombiniert werden, um eine ansprechende musikalische Komposition zu erstellen?
Durch das Zusammenspiel von Melodie, Harmonie und Rhythmus können verschiedene musikalische Elemente miteinander kombiniert werden, um eine ansprechende Komposition zu erstellen. Die Melodie kann durch die harmonische Begleitung und den rhythmischen Groove unterstützt werden, um eine zusammenhängende und interessante Musikstück zu schaffen. Die richtige Balance und Kombination dieser Elemente ist entscheidend für die Qualität und Wirkung der Komposition.
Ähnliche Suchbegriffe für Hadoop:
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Big Data Analytics Beyond Hadoop: Real-Time Applications with Storm, Spark, and More Hadoop Alternatives
Master alternative Big Data technologies that can do what Hadoop can't: real-time analytics and iterative machine learning. When most technical professionals think of Big Data analytics today, they think of Hadoop. But there are many cutting-edge applications that Hadoop isn't well suited for, especially real-time analytics and contexts requiring the use of iterative machine learning algorithms. Fortunately, several powerful new technologies have been developed specifically for use cases such as these. Big Data Analytics Beyond Hadoop is the first guide specifically designed to help you take the next steps beyond Hadoop. Dr. Vijay Srinivas Agneeswaran introduces the breakthrough Berkeley Data Analysis Stack (BDAS) in detail, including its motivation, design, architecture, Mesos cluster management, performance, and more. He presents realistic use cases and up-to-date example code for: Spark, the next generation in-memory computing technology from UC Berkeley Storm, the parallel real-time Big Data analytics technology from Twitter GraphLab, the next-generation graph processing paradigm from CMU and the University of Washington (with comparisons to alternatives such as Pregel and Piccolo) Halo also offers architectural and design guidance and code sketches for scaling machine learning algorithms to Big Data, and then realizing them in real-time. He concludes by previewing emerging trends, including real-time video analytics, SDNs, and even Big Data governance, security, and privacy issues. He identifies intriguing startups and new research possibilities, including BDAS extensions and cutting-edge model-driven analytics. Big Data Analytics Beyond Hadoop is an indispensable resource for everyone who wants to reach the cutting edge of Big Data analytics, and stay there: practitioners, architects, programmers, data scientists, researchers, startup entrepreneurs, and advanced students.
Preis: 32.09 € | Versand*: 0 € -
Hadoop 2 Quick-Start Guide: Learn the Essentials of Big Data Computing in the Apache Hadoop 2 Ecosystem
Get Started Fast with Apache Hadoop® 2, YARN, and Today’s Hadoop Ecosystem With Hadoop 2.x and YARN, Hadoop moves beyond MapReduce to become practical for virtually any type of data processing. Hadoop 2.x and the Data Lake concept represent a radical shift away from conventional approaches to data usage and storage. Hadoop 2.x installations offer unmatched scalability and breakthrough extensibility that supports new and existing Big Data analytics processing methods and models. Hadoop® 2 Quick-Start Guide is the first easy, accessible guide to Apache Hadoop 2.x, YARN, and the modern Hadoop ecosystem. Building on his unsurpassed experience teaching Hadoop and Big Data, author Douglas Eadline covers all the basics you need to know to install and use Hadoop 2 on personal computers or servers, and to navigate the powerful technologies that complement it. Eadline concisely introduces and explains every key Hadoop 2 concept, tool, and service, illustrating each with a simple “beginning-to-end” example and identifying trustworthy, up-to-date resources for learning more. This guide is ideal if you want to learn about Hadoop 2 without getting mired in technical details. Douglas Eadline will bring you up to speed quickly, whether you’re a user, admin, devops specialist, programmer, architect, analyst, or data scientist. Coverage Includes Understanding what Hadoop 2 and YARN do, and how they improve on Hadoop 1 with MapReduceUnderstanding Hadoop-based Data Lakes versus RDBMS Data WarehousesInstalling Hadoop 2 and core services on Linux machines, virtualized sandboxes, or clustersExploring the Hadoop Distributed File System (HDFS)Understanding the essentials of MapReduce and YARN application programmingSimplifying programming and data movement with Apache Pig, Hive, Sqoop, Flume, Oozie, and HBaseObserving application progress, controlling jobs, and managing workflowsManaging Hadoop efficiently with Apache Ambari–including recipes for HDFS to NFSv3 gateway, HDFS snapshots, and YARN configurationLearning basic Hadoop 2 troubleshooting, and installing Apache Hue and Apache Spark
Preis: 16.04 € | Versand*: 0 € -
Hadoop 2 Quick-Start Guide: Learn the Essentials of Big Data Computing in the Apache Hadoop 2 Ecosystem
Get Started Fast with Apache Hadoop® 2, YARN, and Today’s Hadoop Ecosystem With Hadoop 2.x and YARN, Hadoop moves beyond MapReduce to become practical for virtually any type of data processing. Hadoop 2.x and the Data Lake concept represent a radical shift away from conventional approaches to data usage and storage. Hadoop 2.x installations offer unmatched scalability and breakthrough extensibility that supports new and existing Big Data analytics processing methods and models. Hadoop® 2 Quick-Start Guide is the first easy, accessible guide to Apache Hadoop 2.x, YARN, and the modern Hadoop ecosystem. Building on his unsurpassed experience teaching Hadoop and Big Data, author Douglas Eadline covers all the basics you need to know to install and use Hadoop 2 on personal computers or servers, and to navigate the powerful technologies that complement it. Eadline concisely introduces and explains every key Hadoop 2 concept, tool, and service, illustrating each with a simple “beginning-to-end” example and identifying trustworthy, up-to-date resources for learning more. This guide is ideal if you want to learn about Hadoop 2 without getting mired in technical details. Douglas Eadline will bring you up to speed quickly, whether you’re a user, admin, devops specialist, programmer, architect, analyst, or data scientist. Coverage Includes Understanding what Hadoop 2 and YARN do, and how they improve on Hadoop 1 with MapReduceUnderstanding Hadoop-based Data Lakes versus RDBMS Data WarehousesInstalling Hadoop 2 and core services on Linux machines, virtualized sandboxes, or clustersExploring the Hadoop Distributed File System (HDFS)Understanding the essentials of MapReduce and YARN application programmingSimplifying programming and data movement with Apache Pig, Hive, Sqoop, Flume, Oozie, and HBaseObserving application progress, controlling jobs, and managing workflowsManaging Hadoop efficiently with Apache Ambari–including recipes for HDFS to NFSv3 gateway, HDFS snapshots, and YARN configurationLearning basic Hadoop 2 troubleshooting, and installing Apache Hue and Apache Spark
Preis: 21.39 € | Versand*: 0 € -
Expert Hadoop Administration: Managing, Tuning, and Securing Spark, YARN, and HDFS
This is the eBook of the printed book and may not include any media, website access codes, or print supplements that may come packaged with the bound book. The Comprehensive, Up-to-Date Apache Hadoop Administration Handbook and Reference “Sam Alapati has worked with production Hadoop clusters for six years. His unique depth of experience has enabled him to write the go-to resource for all administrators looking to spec, size, expand, and secure production Hadoop clusters of any size.” —Paul Dix, Series Editor In Expert Hadoop® Administration, leading Hadoop administrator Sam R. Alapati brings together authoritative knowledge for creating, configuring, securing, managing, and optimizing production Hadoop clusters in any environment. Drawing on his experience with large-scale Hadoop administration, Alapati integrates action-oriented advice with carefully researched explanations of both problems and solutions. He covers an unmatched range of topics and offers an unparalleled collection of realistic examples. Alapati demystifies complex Hadoop environments, helping you understand exactly what happens behind the scenes when you administer your cluster. You’ll gain unprecedented insight as you walk through building clusters from scratch and configuring high availability, performance, security, encryption, and other key attributes. The high-value administration skills you learn here will be indispensable no matter what Hadoop distribution you use or what Hadoop applications you run. Understand Hadoop’s architecture from an administrator’s standpoint Create simple and fully distributed clusters Run MapReduce and Spark applications in a Hadoop cluster Manage and protect Hadoop data and high availability Work with HDFS commands, file permissions, and storage management Move data, and use YARN to allocate resources and schedule jobs Manage job workflows with Oozie and Hue Secure, monitor, log, and optimize Hadoop Benchmark and troubleshoot Hadoop
Preis: 31.02 € | Versand*: 0 €
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Wie kann man verschiedene musikalische Elemente wie Melodie, Rhythmus und Harmonie in einer Komposition miteinander verbinden, um ein stimmiges musikalisches Werk zu schaffen?
Indem man die verschiedenen Elemente so kombiniert, dass sie sich gegenseitig ergänzen und unterstützen. Durch die Verwendung von musikalischen Strukturen und Formen, um eine kohärente Entwicklung des Stücks zu gewährleisten. Außerdem durch die bewusste Auswahl von Instrumenten und Klangfarben, um die gewünschte Atmosphäre und Emotion zu erzeugen.
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Wie können verschiedene musikalische Elemente wie Melodie, Harmonie und Rhythmus in einer Komposition miteinander verknüpft werden, um eine kohärente und ansprechende Musikstück zu erschaffen?
Durch die Verwendung von Tonleitern und Akkorden kann eine harmonische Grundlage geschaffen werden, die Melodie und Harmonie miteinander verbindet. Der Rhythmus kann durch die Wahl von Tempo, Taktart und Betonungselementen die Struktur des Stücks unterstützen und zusammen mit Melodie und Harmonie eine einheitliche Komposition bilden. Die Variation und Wiederholung dieser Elemente können dazu beitragen, dass das Musikstück kohärent und ansprechend wirkt.
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Wie kann man verschiedene musikalische Elemente wie Melodie, Rhythmus und Harmonie in einer Komposition miteinander verbinden, um eine stimmige und ausdrucksstarke Musikstück zu erschaffen?
Indem man die Melodie, den Rhythmus und die Harmonie so kombiniert, dass sie sich gegenseitig ergänzen und verstärken. Durch die Verwendung von musikalischen Techniken wie Kontrapunkt, Modulation und Variation kann man die Elemente miteinander verweben. Wichtig ist auch, eine klare Struktur und Entwicklung im Musikstück zu schaffen, um eine kohärente und ausdrucksstarke Komposition zu erreichen.
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Wie kann man verschiedene musikalische Elemente wie Melodie, Harmonie und Rhythmus in einer Komposition effektiv miteinander verbinden, um eine kohärente und ausdrucksstarke Musikstück zu erschaffen?
Durch die Verwendung von musikalischen Motiven, die in verschiedenen Instrumenten und Stimmen wiederholt und variiert werden, kann man eine Verbindung zwischen Melodie, Harmonie und Rhythmus herstellen. Die Auswahl und Anordnung der harmonischen Akkorde sowie die rhythmische Struktur sollten darauf abzielen, die emotionale Wirkung des Stücks zu verstärken. Eine sorgfältige Dynamik- und Artikulationsgestaltung kann dazu beitragen, die musikalischen Elemente nahtlos miteinander zu verbinden und dem Stück Ausdruckskraft zu verleihen.
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