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Shared Data Clusters

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Shared Data Clusters

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Author : Dilip M. Ranade
language : en
Publisher: John Wiley & Sons
Release Date : 2003-02-17

Shared Data Clusters written by Dilip M. Ranade and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2003-02-17 with Computers categories.

Clustering is a vital methodology in the data storage world. Its goal is to maximize cost-effectiveness, availability, flexibility, and scalability. Clustering has changed considerably for the better due to Storage Area Networks, which provide access to data from any node in the cluster. Explains how clusters with shared storage work and the components in the cluster that need to work together Reviews where a cluster should be deployed and how to use one for best performance Author is Lead Technical Engineer for VERITAS Cluster File Systems and has worked on clusters and file systems for the past ten years

Shared Data Clusters

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Author : Dilip M. Ranade
language : en
Publisher: Wiley
Release Date : 2002-08-09

Shared Data Clusters written by Dilip M. Ranade and has been published by Wiley this book supported file pdf, txt, epub, kindle and other format this book has been release on 2002-08-09 with Computers categories.

Data Clustering In C

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Author : Guojun Gan
language : en
Publisher: CRC Press
Release Date : 2011-03-28

Data Clustering In C written by Guojun Gan and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-03-28 with Business & Economics categories.

Data clustering is a highly interdisciplinary field, the goal of which is to divide a set of objects into homogeneous groups such that objects in the same group are similar and objects in different groups are quite distinct. Thousands of theoretical papers and a number of books on data clustering have been published over the past 50 years. However,

An Architecture For Fast And General Data Processing On Large Clusters

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Author : Matei Zaharia
language : en
Publisher: Morgan & Claypool
Release Date : 2016-05-01

An Architecture For Fast And General Data Processing On Large Clusters written by Matei Zaharia and has been published by Morgan & Claypool this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-05-01 with Computers categories.

The past few years have seen a major change in computing systems, as growing data volumes and stalling processor speeds require more and more applications to scale out to clusters. Today, a myriad data sources, from the Internet to business operations to scientific instruments, produce large and valuable data streams. However, the processing capabilities of single machines have not kept up with the size of data. As a result, organizations increasingly need to scale out their computations over clusters. At the same time, the speed and sophistication required of data processing have grown. In addition to simple queries, complex algorithms like machine learning and graph analysis are becoming common. And in addition to batch processing, streaming analysis of real-time data is required to let organizations take timely action. Future computing platforms will need to not only scale out traditional workloads, but support these new applications too. This book, a revised version of the 2014 ACM Dissertation Award winning dissertation, proposes an architecture for cluster computing systems that can tackle emerging data processing workloads at scale. Whereas early cluster computing systems, like MapReduce, handled batch processing, our architecture also enables streaming and interactive queries, while keeping MapReduce's scalability and fault tolerance. And whereas most deployed systems only support simple one-pass computations (e.g., SQL queries), ours also extends to the multi-pass algorithms required for complex analytics like machine learning. Finally, unlike the specialized systems proposed for some of these workloads, our architecture allows these computations to be combined, enabling rich new applications that intermix, for example, streaming and batch processing. We achieve these results through a simple extension to MapReduce that adds primitives for data sharing, called Resilient Distributed Datasets (RDDs). We show that this is enough to capture a wide range of workloads. We implement RDDs in the open source Spark system, which we evaluate using synthetic and real workloads. Spark matches or exceeds the performance of specialized systems in many domains, while offering stronger fault tolerance properties and allowing these workloads to be combined. Finally, we examine the generality of RDDs from both a theoretical modeling perspective and a systems perspective. This version of the dissertation makes corrections throughout the text and adds a new section on the evolution of Apache Spark in industry since 2014. In addition, editing, formatting, and links for the references have been added.

Sql Server Big Data Clusters

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Author : Benjamin Weissman
language : en
Publisher: Apress
Release Date : 2020-05-23

Sql Server Big Data Clusters written by Benjamin Weissman and has been published by Apress this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-05-23 with Computers categories.

Use this guide to one of SQL Server 2019’s most impactful features—Big Data Clusters. You will learn about data virtualization and data lakes for this complete artificial intelligence (AI) and machine learning (ML) platform within the SQL Server database engine. You will know how to use Big Data Clusters to combine large volumes of streaming data for analysis along with data stored in a traditional database. For example, you can stream large volumes of data from Apache Spark in real time while executing Transact-SQL queries to bring in relevant additional data from your corporate, SQL Server database. Filled with clear examples and use cases, this book provides everything necessary to get started working with Big Data Clusters in SQL Server 2019. You will learn about the architectural foundations that are made up from Kubernetes, Spark, HDFS, and SQL Server on Linux. You then are shown how to configure and deploy Big Data Clusters in on-premises environments or in the cloud. Next, you are taught about querying. You will learn to write queries in Transact-SQL—taking advantage of skills you have honed for years—and with those queries you will be able to examine and analyze data from a wide variety of sources such as Apache Spark. Through the theoretical foundation provided in this book and easy-to-follow example scripts and notebooks, you will be ready to use and unveil the full potential of SQL Server 2019: combining different types of data spread across widely disparate sources into a single view that is useful for business intelligence and machine learning analysis. What You Will LearnInstall, manage, and troubleshoot Big Data Clusters in cloud or on-premise environments Analyze large volumes of data directly from SQL Server and/or Apache Spark Manage data stored in HDFS from SQL Server as if it were relational data Implement advanced analytics solutions through machine learning and AI Expose different data sources as a single logical source using data virtualization Who This Book Is For Data engineers, data scientists, data architects, and database administrators who want to employ data virtualization and big data analytics in their environments

The Defintive Guide To Building Highly Scalable Enterprise File Serving Solutions

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Author : Realtimepublishers.com
language : en
Publisher: Realtimepublishers.com
Release Date : 2005

The Defintive Guide To Building Highly Scalable Enterprise File Serving Solutions written by Realtimepublishers.com and has been published by Realtimepublishers.com this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005 with Computers categories.

Data Clustering

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Author : Charu C. Aggarwal
language : en
Publisher: CRC Press
Release Date : 2018-09-03

Data Clustering written by Charu C. Aggarwal and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-09-03 with Business & Economics categories.

Research on the problem of clustering tends to be fragmented across the pattern recognition, database, data mining, and machine learning communities. Addressing this problem in a unified way, Data Clustering: Algorithms and Applications provides complete coverage of the entire area of clustering, from basic methods to more refined and complex data clustering approaches. It pays special attention to recent issues in graphs, social networks, and other domains. The book focuses on three primary aspects of data clustering: Methods, describing key techniques commonly used for clustering, such as feature selection, agglomerative clustering, partitional clustering, density-based clustering, probabilistic clustering, grid-based clustering, spectral clustering, and nonnegative matrix factorization Domains, covering methods used for different domains of data, such as categorical data, text data, multimedia data, graph data, biological data, stream data, uncertain data, time series clustering, high-dimensional clustering, and big data Variations and Insights, discussing important variations of the clustering process, such as semisupervised clustering, interactive clustering, multiview clustering, cluster ensembles, and cluster validation In this book, top researchers from around the world explore the characteristics of clustering problems in a variety of application areas. They also explain how to glean detailed insight from the clustering process—including how to verify the quality of the underlying clusters—through supervision, human intervention, or the automated generation of alternative clusters.

Cloud Data Sharing With Ibm Spectrum Scale

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Author : Nikhil Khandelwal
language : en
Publisher: IBM Redbooks
Release Date : 2017-02-14

Cloud Data Sharing With Ibm Spectrum Scale written by Nikhil Khandelwal and has been published by IBM Redbooks this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-02-14 with Computers categories.

This IBM® RedpaperTM publication provides information to help you with the sizing, configuration, and monitoring of hybrid cloud solutions using the Cloud data sharing feature of IBM Spectrum ScaleTM. IBM Spectrum Scale, formerly IBM General Parallel File System (IBM GPFSTM), is a scalable data and file management solution that provides a global namespace for large data sets along with several enterprise features. Cloud data sharing allows for the sharing and use of data between various cloud object storage types and IBM Spectrum Scale. Cloud data sharing can help with the movement of data in both directions, between file systems and cloud object storage, so that data is where it needs to be, when it needs to be there. This paper is intended for IT architects, IT administrators, storage administrators, and those who want to learn more about sizing, configuration, and monitoring of hybrid cloud solutions using IBM Spectrum Scale and Cloud data sharing.

Storage Area Network Essentials

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Author : Richard Barker
language : en
Publisher: John Wiley & Sons
Release Date : 2002-11-06

Storage Area Network Essentials written by Richard Barker and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2002-11-06 with Computers categories.

The inside scoop on a leading-edge data storage technology The rapid growth of e-commerce and the need to have all kinds ofapplications operating at top speed at the same time, all on a 24/7basis while connected to the Internet, is overwhelming traditionaldata storage methods. The solution? Storage Area Networks(SANs)--the data communications technology that's expected torevolutionize distributed computing. Written by top technologyexperts at VERITAS Software Global Corporation, this book takesreaders through all facets of storage networking, explaining how aSAN can help consolidate conventional server storage onto networks,how it makes applications highly available no matter how much datais being stored, and how this in turn makes data access andmanagement faster and easier. System and network managersconsidering storage networking for their enterprises, as well asapplication developers and IT staff, will find invaluable advice onthe design and deployment of the technology and how it works.Detailed, up-to-date coverage includes: The evolution of the technology and what is expected fromSANs Killer applications for SANs Full coverage of storage networking and what it means for theenterprise's information processing architecture Individual chapters devoted to the storage, network, andsoftware components of storage networking Issues for implementation and adoption

Sharing Data And Models In Software Engineering

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Author : Tim Menzies
language : en
Publisher: Morgan Kaufmann
Release Date : 2014-12-22

Sharing Data And Models In Software Engineering written by Tim Menzies and has been published by Morgan Kaufmann this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-12-22 with Computers categories.

Data Science for Software Engineering: Sharing Data and Models presents guidance and procedures for reusing data and models between projects to produce results that are useful and relevant. Starting with a background section of practical lessons and warnings for beginner data scientists for software engineering, this edited volume proceeds to identify critical questions of contemporary software engineering related to data and models. Learn how to adapt data from other organizations to local problems, mine privatized data, prune spurious information, simplify complex results, how to update models for new platforms, and more. Chapters share largely applicable experimental results discussed with the blend of practitioner focused domain expertise, with commentary that highlights the methods that are most useful, and applicable to the widest range of projects. Each chapter is written by a prominent expert and offers a state-of-the-art solution to an identified problem facing data scientists in software engineering. Throughout, the editors share best practices collected from their experience training software engineering students and practitioners to master data science, and highlight the methods that are most useful, and applicable to the widest range of projects. Shares the specific experience of leading researchers and techniques developed to handle data problems in the realm of software engineering Explains how to start a project of data science for software engineering as well as how to identify and avoid likely pitfalls Provides a wide range of useful qualitative and quantitative principles ranging from very simple to cutting edge research Addresses current challenges with software engineering data such as lack of local data, access issues due to data privacy, increasing data quality via cleaning of spurious chunks in data

Shared Data Clusters

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