Data Architecture: A Primer for the Data Scientist: Big Data, Data Warehouse and Data Vault

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9780128020449: Data Architecture: A Primer for the Data Scientist: Big Data, Data Warehouse and Data Vault
From the Publisher:

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. William Inmon, Jay Brophy 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 * Discusses the value in Big Data that is often overlooked, non-repetitive data, and why there is significant business value in using it* Shows how to turn textual information into a form that can be analyzed by standard tools.* Explains how Big Data fits within an existing systems environment * Presents new opportunities that are afforded by the advent of Big Data * Demystifies the murky waters of repetitive and non-repetitive data in Big Data

About the Author:

Dan Linstedt is founder and principle of Empowered Holdings, LLC - a holding company for LearnDataVault.com, and RapidGenDS.com. LearnDataVault.com and a is a world-renowned expert in Data Warehousing and Business Intelligence. He has 20+ years of experience in the IT industry, and has worked with companies like Nike, PepsiCo, Amex, and Visa. His experience extends through data modeling, process design to ETL/ELT performance and tuning. He has a background in SEI/CMMI Level 5, and has contributed architecture efforts to petabyte scale data warehouses offers high quality on-line training and consulting services for Data Vault. He is the inventor and founder of the Data Vault modeling and methodology.

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Inmon, W. H.
Editorial: Elsevier Science & Technology
ISBN 10: 012802044X ISBN 13: 9780128020449
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Descripción ELSEVIER SCIENCE TECHNOLOGY, United States, 2015. Paperback. Estado de conservación: New. 235 x 191 mm. Language: English . Brand New Book. 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 * Discusses the value in Big Data that is often overlooked, non-repetitive data, and why there is significant business value in using it* Shows how to turn textual information into a form that can be analyzed by standard tools.* Explains how Big Data fits within an existing systems environment * Presents new opportunities that are afforded by the advent of Big Data * Demystifies the murky waters of repetitive and non-repetitive data in Big Data. Nº de ref. de la librería AAZ9780128020449

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Inmon, W. H.
Editorial: ELSEVIER SCIENCE TECHNOLOGY, United States (2015)
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Descripción ELSEVIER SCIENCE TECHNOLOGY, United States, 2015. Paperback. Estado de conservación: New. 235 x 191 mm. Language: English . Brand New Book. 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 * Discusses the value in Big Data that is often overlooked, non-repetitive data, and why there is significant business value in using it* Shows how to turn textual information into a form that can be analyzed by standard tools.* Explains how Big Data fits within an existing systems environment * Presents new opportunities that are afforded by the advent of Big Data * Demystifies the murky waters of repetitive and non-repetitive data in Big Data. Nº de ref. de la librería AAZ9780128020449

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Descripción Morgan Kaufmann Publishers In 2014-12-15, 2014. Estado de conservación: New. Brand new book, sourced directly from publisher. Dispatch time is 24-48 hours from our warehouse. Book will be sent in robust, secure packaging to ensure it reaches you securely. Nº de ref. de la librería NU-GRD-05216788

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Inmon, W. H.
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Descripción Elsevier Science & Technology. Paperback. Estado de conservación: new. BRAND NEW, Data Architecture: A Primer for the Data Scientist: Big Data, Data Warehouse and Data Vault, William H. Inmon, Dan Linstedt, 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 * Discusses the value in Big Data that is often overlooked, non-repetitive data, and why there is significant business value in using it* Shows how to turn textual information into a form that can be analyzed by standard tools.* Explains how Big Data fits within an existing systems environment * Presents new opportunities that are afforded by the advent of Big Data * Demystifies the murky waters of repetitive and non-repetitive data in Big Data. Nº de ref. de la librería B9780128020449

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Descripción Morgan Kaufmann, 2014. Estado de conservación: New. Num Pages: 378 pages. BIC Classification: UND. Category: (P) Professional & Vocational. Dimension: 235 x 190 x 13. Weight in Grams: 762. . 2014. 1st Edition. Paperback. . . . . . Nº de ref. de la librería V9780128020449

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Descripción Elsevier Science & Technology 2014-11-26, San Francisco, 2014. paperback. Estado de conservación: New. Nº de ref. de la librería 9780128020449

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Descripción Morgan Kaufmann. Estado de conservación: New. Num Pages: 378 pages. BIC Classification: UND. Category: (P) Professional & Vocational. Dimension: 235 x 190 x 13. Weight in Grams: 762. . 2014. 1st Edition. Paperback. . . . . Books ship from the US and Ireland. Nº de ref. de la librería V9780128020449

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