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

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

Le informazioni nella sezione "Riassunto" possono far riferimento a edizioni diverse di questo titolo.

About the Author:

Dan has more than 25 years of experience in the Data Warehousing and Business Intelligence field and is internationally known for inventing the Data Vault 1.0 model and the Data Vault 2.0 System of Business Intelligence. He helps business and government organizations around the world to achieve BI excellence by applying his proven knowledge in Big Data, unstructured information management, agile methodologies and product development. He has held training classes and presented at TDWI, Teradata Partners, DAMA, Informatica, Oracle user groups and Data Modeling Zone conference. He has a background in SEI/CMMI Level 5, and has contributed architecture efforts to petabyte scale data warehouses and offers high quality on-line training and consulting services for Data Vault.

Le informazioni nella sezione "Su questo libro" possono far riferimento a edizioni diverse di questo titolo.

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W.H. Inmon; Dan Linstedt
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William H. Inmon, Dan Linstedt
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Descrizione libro ELSEVIER SCIENCE TECHNOLOGY, United States, 2015. Paperback. Condizione libro: New. 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. Codice libro della libreria AAZ9780128020449

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William H. Inmon, Dan Linstedt
Editore: ELSEVIER SCIENCE TECHNOLOGY, United States (2015)
ISBN 10: 012802044X ISBN 13: 9780128020449
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Descrizione libro ELSEVIER SCIENCE TECHNOLOGY, United States, 2015. Paperback. Condizione libro: New. 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 DataUnderstand how Big Data fits within an existing systems environment Conduct analytics on repetitive and non-repetitive data. Codice libro della libreria AAZ9780128020449

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W.H. Inmon, Dan Linstedt
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Descrizione libro Morgan Kaufmann, 2014. PAP. Condizione libro: New. New Book. Shipped from UK in 4 to 14 days. Established seller since 2000. Codice libro della libreria GB-9780128020449

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Descrizione libro Elsevier Science & Technology. Paperback. Condizione libro: 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. Codice libro della libreria B9780128020449

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