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  • Cloud Computing: Automating the Virtualized Data Center
    Cloud Computing: Automating the Virtualized Data Center
    The complete guide to provisioning and managing cloud-based Infrastructure as a Service (IaaS) data center solutions Cloud computing will revolutionize the way IT resources are deployed, configured, and managed for years to come. Service providers and customers each stand to realize tremendous value from this paradigm shift—if they can take advantage of it. Cloud Computing brings together the realistic, start-to-finish guidance they need to plan, implement, and manage cloud solution architectures for tomorrow’s virtualized data centers. It introduces cloud “newcomers” to essential concepts, and offers experienced operations professionals detailed guidance on delivering Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS).   This book’s replicable solutions and fully-tested best practices will help enterprises, service providers, consultants, and Cisco partners meet the challenge of provisioning end-to-end cloud infrastructures. Drawing on extensive experience working with leading cloud vendors and integrators, the authors present detailed operations workflow examples, proven techniques for operating cloud-based network, compute, and storage infrastructure; a comprehensive management reference architecture; and a complete case study demonstrating rapid, lower-cost solutions design.   Cloud Computing will be an indispensable resource for all network/IT professionals and managers involved with planning, implementing, or managing the next generation of cloud computing services.   Venkata (Josh) Josyula, Ph.D., CCIE® No. 13518 is a Distinguished Services Engineer in Cisco Services Technology Group (CSTG) and advises Cisco customers on OSS/BSS architecture and solutions. Malcolm Orr, Solutions Architect for Cisco’s Services Technology Solutions, advises telecoms and enterprise clients on architecting, building, and operating OSS/BSS and cloud management stacks. He is Cisco’s lead architect for several Tier 1 public cloud projects. Greg Page has spent the last eleven years with Cisco in technical consulting roles relating to data center architecture/technology and service provider security. He is now exclusively focused on developing cloud/IaaS solutions with service providers and systems integrator partners.   ·        Review the key concepts needed to successfully deploy clouds and cloud-based services ·        Transition common enterprise design patterns and use cases to the cloud ·        Master architectural principles and infrastructure designs for “real-time” managed IT services ·        Understand the Cisco approach to cloud-related technologies, systems, and services ·        Develop a cloud management architecture using ITIL, TMF, and ITU-TMN standards ·        Implement best practices for cloud service provisioning, activation, and management ·        Automate cloud infrastructure to simplify service delivery, monitoring, and assurance ·        Choose and implement the right billing/chargeback approaches for your business ·        Design and build IaaS services, from start to finish ·        Manage the unique capacity challenges associated with sporadic, real-time demand ·        Provide a consistent and optimal cloud user experience   This book is part of the Networking Technology Series from Cisco Press®, which offers networking professionals valuable information for constructing efficient networks, understanding new technologies, and building successful careers.   Category:  Cloud Computing Covers: Virtualized Data Centers  
    Preis: 18.18 € | Versand*: 0 €
  • Big Data Demystified
    Big Data Demystified
    The full text downloaded to your computer With eBooks you can: search for key concepts, words and phrases make highlights and notes as you study share your notes with friends eBooks are downloaded to your computer and accessible either offline through the Bookshelf (available as a free download), available online and also via the iPad and Android apps. Upon purchase, you'll gain instant access to this eBook. Time limit The eBooks products do not have an expiry date. You will continue to access your digital ebook products whilst you have your Bookshelf installed. 'Big Data' refers to a new class of data, to which 'big' doesn't quite do it justice. Much like an ocean is more than simply a deeper swimming pool, big data is fundamentally different to traditional data and needs a whole new approach. Packed with examples and case studies, this clear, comprehensive book will show you how to accumulate and utilise 'big data' in order to develop your business strategy. Big Data Demystified is your practical guide to help you draw deeper insights from the vast information at your fingertips; you will be able to understand customer motivations, speed up production lines, and even offer personalised experiences to each and every customer. With 20 years of industry experience, David Stephenson shows how big data can give you the best competitive edge, and why it is integral to the future of your business.
    Preis: 16.04 € | Versand*: 0 €
  • Understanding Big Data Scalability: Big Data Scalability Series, Part I
    Understanding Big Data Scalability: Big Data Scalability Series, Part I
    Get Started Scaling Your Database Infrastructure for High-Volume Big Data Applications  “Understanding Big Data Scalability presents the fundamentals of scaling databases from a single node to large clusters. It provides a practical explanation of what ‘Big Data’ systems are, and fundamental issues to consider when optimizing for performance and scalability. Cory draws on many years of experience to explain issues involved in working with data sets that can no longer be handled with single, monolithic relational databases.... His approach is particularly relevant now that relational data models are making a comeback via SQL interfaces to popular NoSQL databases and Hadoop distributions.... This book should be especially useful to database practitioners new to scaling databases beyond traditional single node deployments.” —Brian O’Krafka, software architect  Understanding Big Data Scalability presents a solid foundation for scaling Big Data infrastructure and helps you address each crucial factor associated with optimizing performance in scalable and dynamic Big Data clusters.   Database expert Cory Isaacson offers practical, actionable insights for every technical professional who must scale a database tier for high-volume applications. Focusing on today’s most common Big Data applications, he introduces proven ways to manage unprecedented data growth from widely diverse sources and to deliver real-time processing at levels that were inconceivable until recently.   Isaacson explains why databases slow down, reviews each major technique for scaling database applications, and identifies the key rules of database scalability that every architect should follow.   You’ll find insights and techniques proven with all types of database engines and environments, including SQL, NoSQL, and Hadoop. Two start-to-finish case studies walk you through planning and implementation, offering specific lessons for formulating your own scalability strategy. Coverage includes  Understanding the true causes of database performance degradation in today’s Big Data environments Scaling smoothly to petabyte-class databases and beyond Defining database clusters for maximum scalability and performance Integrating NoSQL or columnar databases that aren’t “drop-in” replacements for RDBMSes Scaling application components: solutions and options for each tier Recognizing when to scale your data tier—a decision with enormous consequences for your application environment Why data relationships may be even more important in non-relational databases Why virtually every database scalability implementation still relies on sharding, and how to choose the best approach How to set clear objectives for architecting high-performance Big Data implementations  The Big Data Scalability Series is a comprehensive, four-part series, containing information on many facets of database performance and scalability. Understanding Big Data Scalability is the first book in the series.   Learn more and join the conversation about Big Data scalability at bigdatascalability.com.  
    Preis: 6.41 € | Versand*: 0 €
  • Jankowski, Timo: Fußball - Von Big Data zu Smart Data
    Jankowski, Timo: Fußball - Von Big Data zu Smart Data
    Fußball - Von Big Data zu Smart Data , Das Thema Big Data ist unaufhaltsam in die Fußballwelt eingezogen und wird mit Sicherheit auch nicht mehr verschwinden. Es wird weiterhin an Bedeutung gewinnen, da die Datenqualität und die praktische Umsetzung dieser Daten bereits zahlreiche beeindruckende Erfolge vorweisen können. Zu Beginn des Buchs wird auf die Problematik des Schwarz-Weiß-Denkens, das im Fußball weit verbreitet ist, eingegangen. Im zweiten Teil rückt dann das Thema Big Data im Fußball in den Vordergrund. Dies geschieht vor allem immer im Hinblick auf die Umwandlung in Smart Data mit vielen praktischen Beispielen, sodass jeder Trainer und Interessierte zahlreiche Anregungen für die eigene Arbeit in der Planung, auf dem Platz und in der Evaluierung bekommt. Zahlreiche Key-Performance-Indikatoren (KPIs) werden unter die Lupe genommen und es wird aufgezeigt, wie Datenanalyse auf dem Weg zum Erfolg helfen kann. Ziel dieses Werks ist es, das Thema Big Data im Fußball zu entmystifizieren, weshalb im letzten Abschnitt die erfolgreiche Qualifikation der Juniorennationalmannschaft von Fidschi für die U20-Weltmeisterschaft 2023 beschrieben wird. Dieses Beispiel zeigt, wie die richtige Mischung aus objektiven Daten und den menschlichen Komponenten in der Praxis zum Erfolg führen kann. Dieses Buch plädiert dafür, die tief verwurzelten Werte und die Ursprünglichkeit des Fußballs unbedingt beizubehalten und zeigt auf, wie sich beide Seiten - Bauchgefühl und Datenanalyse - gewinnbringend miteinander verbinden lassen. Fußball - von Big Data zu Smart Data ist DAS Standardwerk für alle Trainer, die das Thema Big Data angehen wollen und Tipps für die Umsetzung auf dem Platz benötigen. , Bücher > Bücher & Zeitschriften
    Preis: 28.00 € | Versand*: 0 €

  • Wie beeinflusst künstliche Intelligenz den Bereich der Informatik? Welche Rolle spielen Big Data und Data Mining in der Informatik?

    Künstliche Intelligenz optimiert Prozesse in der Informatik, automatisiert Aufgaben und ermöglicht neue Anwendungen wie maschinelles Lernen. Big Data und Data Mining sind wichtige Werkzeuge in der Informatik, um große Datenmengen zu analysieren, Muster zu erkennen und fundierte Entscheidungen zu treffen. Sie spielen eine zentrale Rolle bei der Entwicklung von KI-Systemen und der Optimierung von Algorithmen.

  • Wann spricht man von Big Data?

    Wann spricht man von Big Data? Man spricht von Big Data, wenn es sich um eine große Menge an Daten handelt, die in hoher Geschwindigkeit generiert werden und in verschiedenen Formaten vorliegen. Zudem müssen diese Daten eine Vielzahl von Variablen und Merkmalen enthalten, die mit herkömmlichen Datenverarbeitungstechniken nur schwer oder gar nicht verarbeitet werden können. Ein weiteres Merkmal von Big Data ist die Notwendigkeit, fortgeschrittene Analysetechniken wie maschinelles Lernen oder künstliche Intelligenz einzusetzen, um wertvolle Erkenntnisse aus den Daten zu gewinnen. Letztendlich ist Big Data gekennzeichnet durch die Herausforderung, die Daten effizient zu speichern, zu verarbeiten und zu analysieren, um daraus Mehrwert für Unternehmen oder Organisationen zu generieren.

  • Was sind die potenziellen Anwendungen von Big Data in verschiedenen Branchen?

    Big Data kann in verschiedenen Branchen wie Gesundheitswesen, Einzelhandel und Finanzdienstleistungen eingesetzt werden, um Einblicke in Kundenverhalten, Trends und Muster zu gewinnen. In der Produktion kann Big Data zur Optimierung von Prozessen und zur Vorhersage von Wartungsbedarf genutzt werden. Im Bereich der Logistik kann Big Data helfen, Lieferketten effizienter zu gestalten und Kosten zu senken.

  • Wie beeinflusst die Nutzung von Big Data die Entscheidungsfindung in Unternehmen?

    Die Nutzung von Big Data ermöglicht Unternehmen, fundierte Entscheidungen auf Basis von umfangreichen Datenanalysen zu treffen. Durch die Analyse großer Datenmengen können Trends und Muster identifiziert werden, die bei der Entscheidungsfindung berücksichtigt werden. Dies führt zu einer effizienteren und präziseren Entscheidungsfindung in Unternehmen.

Ähnliche Suchbegriffe für Big Data:

Data
Big
Unternehmen
Daten
Entscheidungen
Treffen
Entscheidungsfindung
Muster
Fundierte
Informatik
  • Big Data Fundamentals: Concepts, Drivers & Techniques
    Big Data Fundamentals: Concepts, Drivers & Techniques
    “This text should be required reading for everyone in contemporary business.” --Peter Woodhull, CEO, Modus21 “The one book that clearly describes and links Big Data concepts to business utility.” --Dr. Christopher Starr, PhD“Simply, this is the best Big Data book on the market!” --Sam Rostam, Cascadian IT Group“...one of the most contemporary approaches I’ve seen to Big Data fundamentals...” --Joshua M. Davis, PhDThe Definitive Plain-English Guide to Big Data for Business and Technology Professionals Big Data Fundamentals provides a pragmatic, no-nonsense introduction to Big Data. Best-selling IT author Thomas Erl and his team clearly explain key Big Data concepts, theory and terminology, as well as fundamental technologies and techniques. All coverage is supported with case study examples and numerous simple diagrams. The authors begin by explaining how Big Data can propel an organization forward by solving a spectrum of previously intractable business problems. Next, they demystify key analysis techniques and technologies and show how a Big Data solution environment can be built and integrated to offer competitive advantages.Discovering Big Data’s fundamental concepts and what makes it different from previous forms of data analysis and data scienceUnderstanding the business motivations and drivers behind Big Data adoption, from operational improvements through innovationPlanning strategic, business-driven Big Data initiativesAddressing considerations such as data management, governance, and securityRecognizing the 5 “V” characteristics of datasets in Big Data environments: volume, velocity, variety, veracity, and valueClarifying Big Data’s relationships with OLTP, OLAP, ETL, data warehouses, and data martsWorking with Big Data in structured, unstructured, semi-structured, and metadata formatsIncreasing value by integrating Big Data resources with corporate performance monitoringUnderstanding how Big Data leverages distributed and parallel processingUsing NoSQL and other technologies to meet Big Data’s distinct data processing requirementsLeveraging statistical approaches of quantitative and qualitative analysisApplying computational analysis methods, including machine learning
    Preis: 18.18 € | Versand*: 0 €
  • Intro to Python for Computer Science and Data Science: Learning to Program with AI, Big Data and The Cloud
    Intro to Python for Computer Science and Data Science: Learning to Program with AI, Big Data and The Cloud
    Introduction to Python for Computer Science and Data Science takes a unique, modular approach to teaching and learning introductory Python programming that is relevant for both computer science and data science audiences. The Deitels cover the most current topics and applications to prepare you for your career. Jupyter Notebooks supplements provide opportunities to test your programming skills. Fully implemented case studies in artificial intelligence technologies and big data let you apply your knowledge to interesting projects in the business, industry, government and academia sectors. Hundreds of hands-on examples, exercises and projects offer a challenging and entertaining introduction to Python and data science.
    Preis: 90.94 € | Versand*: 0 €
  • Intro to Python for Computer Science and Data Science: Learning to Program with AI, Big Data and The Cloud, Global Edition
    Intro to Python for Computer Science and Data Science: Learning to Program with AI, Big Data and The Cloud, Global Edition
    A ground-breaking, flexible approach to computer science and data science The Deitels' Introduction to Python for Computer Science and Data Science: Learning to Program with AI, Big Data and the Cloud offers a unique approach to teaching introductory Python programming, appropriate for both computer-science and data-science audiences. Providing the most current coverage of topics and applications, the book is paired with extensive traditional supplements as well as Jupyter Notebooks supplements. Real-world datasets and artificial-intelligence technologies allow students to work on projects making a difference in business, industry, government and academia. Hundreds of examples, exercises, projects (EEPs) and implementation case studies give students an engaging, challenging and entertaining introduction to Python programming and hands-on data science. The book's modular architecture enables instructors to conveniently adapt the text to a wide range of computer-science and data-science courses offered to audiences drawn from many majors. Computer-science instructors can integrate as much or as little data-science and artificial-intelligence topics as they'd like, and data-science instructors can integrate as much or as little Python as they'd like. The book aligns with the latest ACM/IEEE CS-and-related computing curriculum initiatives and with the Data Science Undergraduate Curriculum Proposal sponsored by the National Science Foundation.
    Preis: 53.49 € | Versand*: 0 €
  • Cloud Computing: Automating the Virtualized Data Center
    Cloud Computing: Automating the Virtualized Data Center
    The complete guide to provisioning and managing cloud-based Infrastructure as a Service (IaaS) data center solutions Cloud computing will revolutionize the way IT resources are deployed, configured, and managed for years to come. Service providers and customers each stand to realize tremendous value from this paradigm shift—if they can take advantage of it. Cloud Computing brings together the realistic, start-to-finish guidance they need to plan, implement, and manage cloud solution architectures for tomorrow’s virtualized data centers. It introduces cloud “newcomers” to essential concepts, and offers experienced operations professionals detailed guidance on delivering Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS).   This book’s replicable solutions and fully-tested best practices will help enterprises, service providers, consultants, and Cisco partners meet the challenge of provisioning end-to-end cloud infrastructures. Drawing on extensive experience working with leading cloud vendors and integrators, the authors present detailed operations workflow examples, proven techniques for operating cloud-based network, compute, and storage infrastructure; a comprehensive management reference architecture; and a complete case study demonstrating rapid, lower-cost solutions design.   Cloud Computing will be an indispensable resource for all network/IT professionals and managers involved with planning, implementing, or managing the next generation of cloud computing services.   Venkata (Josh) Josyula, Ph.D., CCIE® No. 13518 is a Distinguished Services Engineer in Cisco Services Technology Group (CSTG) and advises Cisco customers on OSS/BSS architecture and solutions. Malcolm Orr, Solutions Architect for Cisco’s Services Technology Solutions, advises telecoms and enterprise clients on architecting, building, and operating OSS/BSS and cloud management stacks. He is Cisco’s lead architect for several Tier 1 public cloud projects. Greg Page has spent the last eleven years with Cisco in technical consulting roles relating to data center architecture/technology and service provider security. He is now exclusively focused on developing cloud/IaaS solutions with service providers and systems integrator partners.   ·        Review the key concepts needed to successfully deploy clouds and cloud-based services ·        Transition common enterprise design patterns and use cases to the cloud ·        Master architectural principles and infrastructure designs for “real-time” managed IT services ·        Understand the Cisco approach to cloud-related technologies, systems, and services ·        Develop a cloud management architecture using ITIL, TMF, and ITU-TMN standards ·        Implement best practices for cloud service provisioning, activation, and management ·        Automate cloud infrastructure to simplify service delivery, monitoring, and assurance ·        Choose and implement the right billing/chargeback approaches for your business ·        Design and build IaaS services, from start to finish ·        Manage the unique capacity challenges associated with sporadic, real-time demand ·        Provide a consistent and optimal cloud user experience   This book is part of the Networking Technology Series from Cisco Press®, which offers networking professionals valuable information for constructing efficient networks, understanding new technologies, and building successful careers.   Category:  Cloud Computing Covers: Virtualized Data Centers  
    Preis: 24.6 € | Versand*: 0 €

  • Welche Auswirkungen hat die fortschreitende Nutzung von Big Data auf die Privatsphäre und den Datenschutz von Verbrauchern?

    Die fortschreitende Nutzung von Big Data kann die Privatsphäre von Verbrauchern gefährden, da immer mehr persönliche Daten gesammelt und analysiert werden. Dies kann zu einer verstärkten Überwachung und Profilierung von Individuen führen. Es ist wichtig, dass strenge Datenschutzgesetze und -richtlinien implementiert werden, um die Privatsphäre der Verbraucher zu schützen.

  • Wie wird Big Data in der modernen Wirtschaft eingesetzt und welche Auswirkungen hat es auf die Unternehmensführung?

    Big Data wird in der modernen Wirtschaft verwendet, um große Mengen an Daten zu analysieren und daraus Erkenntnisse für bessere Entscheidungen zu gewinnen. Unternehmen können mithilfe von Big Data ihre Prozesse optimieren, Kundenbedürfnisse besser verstehen und personalisierte Angebote entwickeln. Die Auswirkungen auf die Unternehmensführung sind eine datengetriebene Entscheidungsfindung, eine verbesserte Effizienz und Wettbewerbsfähigkeit sowie die Notwendigkeit, Datenschutz und Sicherheit zu gewährleisten.

  • Wie beeinflusst die Verwendung von Big Data die Art und Weise, wie Unternehmen Entscheidungen treffen und ihre Geschäftsstrategien entwickeln?

    Die Verwendung von Big Data ermöglicht es Unternehmen, fundierte Entscheidungen auf Basis von umfangreichen Datenanalysen zu treffen. Durch die Analyse großer Datenmengen können Unternehmen Trends und Muster identifizieren, um ihre Geschäftsstrategien zu optimieren. Big Data hilft Unternehmen, effizienter zu arbeiten, Risiken zu minimieren und Wettbewerbsvorteile zu erlangen.

  • Was sind die Herausforderungen und Chancen, die sich durch den Einsatz von Big Data für Unternehmen und die Gesellschaft ergeben?

    Die Herausforderungen beim Einsatz von Big Data sind Datenschutzbedenken, die Komplexität der Datenanalyse und die Notwendigkeit qualifizierter Fachkräfte. Die Chancen liegen in der Möglichkeit, fundierte Entscheidungen auf Basis von Daten zu treffen, die Optimierung von Geschäftsprozessen und die Entwicklung neuer Produkte und Dienstleistungen. Big Data kann auch dazu beitragen, gesellschaftliche Probleme zu lösen und Innovationen voranzutreiben.

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