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

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L'autore:

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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Linstedt, Daniel
Editore: Elsevier Science & Technology
ISBN 10: 012802044X ISBN 13: 9780128020449
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Descrizione libro Elsevier Science & Technology. Condizione libro: Brand New. Ships SAME or NEXT business day. We Ship to APO/FPO addr. Choose EXPEDITED shipping and receive in 2-5 business days within the United States. See our member profile for customer support contact info. We have an easy return policy. Codice libro della libreria 42020553

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William H. Inmon, Dan Linstedt
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Descrizione libro Morgan Kaufmann Publishers In 2014-12-15, 2014. Paperback. Condizione libro: New. Codice libro della libreria NU-ELS-00007194

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Descrizione libro ELSEVIER SCIENCE TECHNOLOGY, United States, 2015. Paperback. Condizione libro: New. 235 x 190 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. Codice libro della libreria AAZ9780128020449

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William H. Inmon, Dan Linstedt
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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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William H. Inmon, Dan Linstedt
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ISBN 10: 012802044X ISBN 13: 9780128020449
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Descrizione libro Elsevier Science & Technology 2014-11-26, San Francisco, 2014. paperback. Condizione libro: New. Codice libro della libreria 9780128020449

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William H. Inmon, Dan Linstedt
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ISBN 10: 012802044X ISBN 13: 9780128020449
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Descrizione libro ELSEVIER SCIENCE TECHNOLOGY, United States, 2015. Paperback. Condizione libro: New. 235 x 190 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. Codice libro della libreria LIB9780128020449

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William Inmon
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Descrizione libro Paperback. Condizione libro: New. Not Signed; 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). book. Codice libro della libreria ria9780128020449_rkm

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Inmon, William H./ Linstedt, Dan
Editore: Morgan Kaufmann Pub (2014)
ISBN 10: 012802044X ISBN 13: 9780128020449
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Descrizione libro Morgan Kaufmann Pub, 2014. Paperback. Condizione libro: Brand New. 1st edition. 378 pages. 9.50x7.50x0.75 inches. In Stock. Codice libro della libreria __012802044X

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Linstedt, Daniel
Editore: Elsevier Science & Technology
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Descrizione libro Elsevier Science & Technology. Condizione libro: Brand New. Dispatch Same Working Day, (Delivery 2-4 business days, Courier For Heavy/Expensive Items) Money Back Guarantee, 99.3% Customer Satisfaction, Prompt Customer Service. Codice libro della libreria 41000174

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