Idioma: Inglés
Publicado por Morgan Kaufmann Publishers, 2014
ISBN 10: 012802044X ISBN 13: 9780128020449
Librería: ThriftBooks-Dallas, Dallas, TX, Estados Unidos de America
EUR 6,28
Cantidad disponible: 1 disponibles
Añadir al carritoPaperback. Condición: Fair. No Jacket. Readable copy. Pages may have considerable notes/highlighting. ~ ThriftBooks: Read More, Spend Less.
Librería: MusicMagpie, Stockport, Reino Unido
EUR 4,94
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Añadir al carritoCondición: Very Good. 1749436112. 6/9/2025 2:28:32 AM.
Librería: Textbooks_Source, Columbia, MO, Estados Unidos de America
Original o primera edición
EUR 9,71
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Añadir al carritopaperback. Condición: Good. 1st Edition. Ships in a BOX from Central Missouri! May not include working access code. Will not include dust jacket. Has used sticker(s) and some writing or highlighting. UPS shipping for most packages, (Priority Mail for AK/HI/APO/PO Boxes).
Librería: Wm Burgett Bks and Collectibles, San diego, CA, Estados Unidos de America
Original o primera edición
EUR 20,80
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Añadir al carritoPaperback. Condición: Near Fine. First Edition. CLEAN near fine 2015 paperback.
Librería: GoldBooks, Denver, CO, Estados Unidos de America
EUR 34,89
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Añadir al carritoPaperback. Condición: new. New Copy. Customer Service Guaranteed.
Librería: Shakespeare Book House, Rockford, IL, Estados Unidos de America
EUR 37,86
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Librería: Majestic Books, Hounslow, Reino Unido
EUR 56,31
Cantidad disponible: 3 disponibles
Añadir al carritoCondición: New.
Idioma: Inglés
Publicado por Morgan Kaufmann 2014-11-26, 2014
ISBN 10: 012802044X ISBN 13: 9780128020449
Librería: Chiron Media, Wallingford, Reino Unido
EUR 45,72
Cantidad disponible: Más de 20 disponibles
Añadir al carritoPaperback. Condición: New.
Librería: Revaluation Books, Exeter, Reino Unido
EUR 51,68
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Añadir al carritoPaperback. Condición: Brand New. 1st edition. 378 pages. 9.50x7.50x0.75 inches. In Stock.
Librería: Books Puddle, New York, NY, Estados Unidos de America
EUR 74,77
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Añadir al carritoCondición: New.
Librería: Biblios, Frankfurt am main, HESSE, Alemania
EUR 68,85
Cantidad disponible: 3 disponibles
Añadir al carritoCondición: New.
Idioma: Inglés
Publicado por Elsevier Science & Technology, 2014
ISBN 10: 012802044X ISBN 13: 9780128020449
Librería: THE SAINT BOOKSTORE, Southport, Reino Unido
EUR 59,12
Cantidad disponible: Más de 20 disponibles
Añadir al carritoPaperback / softback. Condición: New. New copy - Usually dispatched within 4 working days.
Librería: preigu, Osnabrück, Alemania
EUR 129,55
Cantidad disponible: 5 disponibles
Añadir al carritoTaschenbuch. Condición: Neu. Data Architecture: A Primer for the Data Scientist | Big Data, Data Warehouse and Data Vault | W. H. Inmon (u. a.) | Taschenbuch | Englisch | 2014 | Elsevier LTD | EAN 9780128020449 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu.
Librería: Brook Bookstore On Demand, Napoli, NA, Italia
EUR 48,91
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Añadir al carritoCondición: new. Questo è un articolo print on demand.
Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Alemania
EUR 150,00
Cantidad disponible: 2 disponibles
Añadir al carritoTaschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Today, the world is trying to create and educate data scientists because of the phenomenon of Big Data. And everyone is looking deeply into this technology. But no one is looking at the larger architectural picture of how Big Data needs to fit within the existing systems (data warehousing systems). Taking a look at the larger picture into which Big Data fits gives the data scientist the necessary context for how pieces of the puzzle should fit together. Most references on Big Data look at only one tiny part of a much larger whole. Until data gathered can be put into an existing framework or architecture it can't be used to its full potential. Data Architecture a Primer for the Data Scientist addresses the larger architectural picture of how Big Data fits with the existing information infrastructure, an essential topic for the data scientist. Drawing upon years of practical experience and using numerous examples and an easy to understand framework. W.H. Inmon, and Daniel Linstedt define the importance of data architecture and how it can be used effectively to harness big data within existing systems. You'll be able to: Turn textual information into a form that can be analyzed by standard tools. Make the connection between analytics and Big Data Understand how Big Data fits within an existing systems environment Conduct analytics on repetitive and non-repetitive data 378 pp. Englisch.
Librería: AHA-BUCH GmbH, Einbeck, Alemania
EUR 156,09
Cantidad disponible: 2 disponibles
Añadir al carritoTaschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Today, the world is trying to create and educate data scientists because of the phenomenon of Big Data. And everyone is looking deeply into this technology. But no one is looking at the larger architectural picture of how Big Data needs to fit within the existing systems (data warehousing systems). Taking a look at the larger picture into which Big Data fits gives the data scientist the necessary context for how pieces of the puzzle should fit together. Most references on Big Data look at only one tiny part of a much larger whole. Until data gathered can be put into an existing framework or architecture it can't be used to its full potential. Data Architecture a Primer for the Data Scientist addresses the larger architectural picture of how Big Data fits with the existing information infrastructure, an essential topic for the data scientist. Drawing upon years of practical experience and using numerous examples and an easy to understand framework. W.H. Inmon, and Daniel Linstedt define the importance of data architecture and how it can be used effectively to harness big data within existing systems. You'll be able to: Turn textual information into a form that can be analyzed by standard tools. Make the connection between analytics and Big Data Understand how Big Data fits within an existing systems environment Conduct analytics on repetitive and non-repetitive data.