Isbn: 9780128169162 - data architecture: a primer for the data scientist (25 resultados)

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  • Idioma: Inglés

    Editorial: Elsevier Science Publishing Co Inc, 2019

    0128169168 / 9780128169162

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    Editorial: Academic Press 2019-06-01, 2019

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    Editorial: Elsevier Science Publishing Co Inc, US, 2019

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    Paperback. Condición: New. Over the past 5 years, the concept of big data has matured, data science has grown exponentially, and data architecture has become a standard part of organizational decision-making. Throughout all this change, the basic principles that shape the architecture of data have remained the same. There remains a need for people to take a look at the "bigger picture" and to understand where their data fit into the grand scheme of things. Data Architecture: A Primer for the Data Scientist, Second Edition addresses the larger architectural picture of how big data fits within the existing information infrastructure or data warehousing systems. This is an essential topic not only for data scientists, analysts, and managers but also for researchers and engineers who increasingly need to deal with large and complex sets of data. Until data are gathered and can be placed into an existing framework or architecture, they cannot be used to their full potential. Drawing upon years of practical experience and using numerous examples and case studies from across various industries, the authors seek to explain this larger picture into which big data fits, giving data scientists the necessary context for how pieces of the puzzle should fit together.…

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    Paperback. Condición: new. Paperback. Data Architecture: A Primer for the Data Scientist: Big Data, Data Warehouse and Data Vault Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

  • Idioma: Inglés

    Editorial: Elsevier Science Publishing Co Inc, US, 2019

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    Paperback. Condición: New. Over the past 5 years, the concept of big data has matured, data science has grown exponentially, and data architecture has become a standard part of organizational decision-making. Throughout all this change, the basic principles that shape the architecture of data have remained the same. There remains a need for people to take a look at the "bigger picture" and to understand where their data fit into the grand scheme of things. Data Architecture: A Primer for the Data Scientist, Second Edition addresses the larger architectural picture of how big data fits within the existing information infrastructure or data warehousing systems. This is an essential topic not only for data scientists, analysts, and managers but also for researchers and engineers who increasingly need to deal with large and complex sets of data. Until data are gathered and can be placed into an existing framework or architecture, they cannot be used to their full potential. Drawing upon years of practical experience and using numerous examples and case studies from across various industries, the authors seek to explain this larger picture into which big data fits, giving data scientists the necessary context for how pieces of the puzzle should fit together.…

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    Editorial: Elsevier Science Publishing Co Inc, 2019

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    Condición: New. 2019. 2nd Edition. Paperback. . . . . .

  • Idioma: Inglés

    Editorial: Academic Press, 2019

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    Paperback. Condición: Brand New. 2nd edition. 450 pages. 9.25x7.50x0.83 inches. In Stock.

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    Condición: New. In English.

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    Editorial: Elsevier Science Publishing Co Inc, 2019

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  • Idioma: Inglés

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    Condición: New. 2019. 2nd Edition. Paperback. . . . . . Books ship from the US and Ireland.

  • Idioma: Inglés

    Editorial: Elsevier, 2019

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    Condición: New. pp. 450.

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    Kartoniert / Broschiert. Condición: New. New case studies include expanded coverage of textual management and analytics New chapters on visualization and big data Discussion of new visualizations of the end-state architectureAutor/Autorin: W.H. Inmon.

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    Editorial: Elsevier Science Publishing Co Inc, US, 2019

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    Paperback. Condición: New. Over the past 5 years, the concept of big data has matured, data science has grown exponentially, and data architecture has become a standard part of organizational decision-making. Throughout all this change, the basic principles that shape the architecture of data have remained the same. There remains a need for people to take a look at the "bigger picture" and to understand where their data fit into the grand scheme of things. Data Architecture: A Primer for the Data Scientist, Second Edition addresses the larger architectural picture of how big data fits within the existing information infrastructure or data warehousing systems. This is an essential topic not only for data scientists, analysts, and managers but also for researchers and engineers who increasingly need to deal with large and complex sets of data. Until data are gathered and can be placed into an existing framework or architecture, they cannot be used to their full potential. Drawing upon years of practical experience and using numerous examples and case studies from across various industries, the authors seek to explain this larger picture into which big data fits, giving data scientists the necessary context for how pieces of the puzzle should fit together.…

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    Taschenbuch. Condición: Neu. Data Architecture: A Primer for the Data Scientist | A Primer for the Data Scientist | Daniel Linstedt (u. a.) | Taschenbuch | Einband - fest (Hardcover) | Englisch | 2019 | Elsevier Science Publishing Co Inc | EAN 9780128169162 | Verantwortliche Person für die EU: Kolibri 360 GmbH, Ettore-Bugatti-Str. 6-14, 51149 Köln, produktsicherheit[at]kolibri360[dot]de | Anbieter: preigu. …

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    Editorial: Elsevier Science Publishing Co Inc, US, 2019

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    Paperback. Condición: New. Over the past 5 years, the concept of big data has matured, data science has grown exponentially, and data architecture has become a standard part of organizational decision-making. Throughout all this change, the basic principles that shape the architecture of data have remained the same. There remains a need for people to take a look at the "bigger picture" and to understand where their data fit into the grand scheme of things. Data Architecture: A Primer for the Data Scientist, Second Edition addresses the larger architectural picture of how big data fits within the existing information infrastructure or data warehousing systems. This is an essential topic not only for data scientists, analysts, and managers but also for researchers and engineers who increasingly need to deal with large and complex sets of data. Until data are gathered and can be placed into an existing framework or architecture, they cannot be used to their full potential. Drawing upon years of practical experience and using numerous examples and case studies from across various industries, the authors seek to explain this larger picture into which big data fits, giving data scientists the necessary context for how pieces of the puzzle should fit together.…

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    Editorial: Elsevier Science Publishing Co Inc, San Diego, 2019

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    Paperback. Condición: new. Paperback. Data Architecture: A Primer for the Data Scientist: Big Data, Data Warehouse and Data Vault Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.

  • Idioma: Inglés

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    Condición: Gut. Zustand: Gut | Seiten: 431 | Sprache: Englisch | Produktart: Bücher | New case studies include expanded coverage of textual management and analytics New chapters on visualization and big data Discussion of new visualizations of the end-state architecture.

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    Editorial: Elsevier Science Publishing Co Inc Mai 2019, 2019

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    Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Over the past 5 years, the concept of big data has matured, data science has grown exponentially, and data architecture has become a standard part of organizational decision-making. Throughout all this change, the basic principles that shape the architecture of data have remained the same. There remains a need for people to take a look at the 'bigger picture' and to understand where their data fit into the grand scheme of things. Data Architecture: A Primer for the Data Scientist, Second Edition addresses the larger architectural picture of how big data fits within the existing information infrastructure or data warehousing systems. This is an essential topic not only for data scientists, analysts, and managers but also for researchers and engineers who increasingly need to deal with large and complex sets of data. Until data are gathered and can be placed into an existing framework or architecture, they cannot be used to their full potential. Drawing upon years of practical experience and using numerous examples and case studies from across various industries, the authors seek to explain this larger picture into which big data fits, giving data scientists the necessary context for how pieces of the puzzle should fit together. 431 pp. Englisch.…

  • Idioma: Inglés

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    Taschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Over the past 5 years, the concept of big data has matured, data science has grown exponentially, and data architecture has become a standard part of organizational decision-making. Throughout all this change, the basic principles that shape the architecture of data have remained the same. There remains a need for people to take a look at the 'bigger picture' and to understand where their data fit into the grand scheme of things. Data Architecture: A Primer for the Data Scientist, Second Edition addresses the larger architectural picture of how big data fits within the existing information infrastructure or data warehousing systems. This is an essential topic not only for data scientists, analysts, and managers but also for researchers and engineers who increasingly need to deal with large and complex sets of data. Until data are gathered and can be placed into an existing framework or architecture, they cannot be used to their full potential. Drawing upon years of practical experience and using numerous examples and case studies from across various industries, the authors seek to explain this larger picture into which big data fits, giving data scientists the necessary context for how pieces of the puzzle should fit together.…