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)....

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Bibliographic Details
Main Authors: Inmon, William H., (Author), Linstedt, Dan, (Author)
Format: eBook
Language: English
Published: Amsterdam : Elsevier, [2014]
Subjects:
skladování dat
big data
data warehousing
elektronické knihy
monografie
electronic books
monographs
ISBN: 9780128020449
9780128020913
Physical Description: 1 online resource

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100 1 |a Inmon, William H.,  |e author. 
245 1 0 |a Data architecture :  |b a primer for the data scientist : big data, data warehouse and data vault /  |c William H. Inmon, Dan Linstedt. 
264 1 |a Amsterdam :  |b Elsevier,  |c [2014] 
264 4 |c ©2015 
300 |a 1 online resource 
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506 |a Plný text je dostupný pouze z IP adres počítačů Univerzity Tomáše Bati ve Zlíně nebo vzdáleným přístupem pro zaměstnance a studenty 
520 |a 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.- Resumé vydavatel 
590 |a Elsevier  |b ScienceDirect All Books 
650 0 7 |a skladování dat  |7 ph139310  |2 czenas 
650 0 7 |a big data  |7 ph836790  |2 czenas 
650 0 9 |a data warehousing  |2 eczenas 
650 0 9 |a big data  |2 eczenas 
655 7 |a elektronické knihy  |7 fd186907  |2 czenas 
655 7 |a monografie  |7 fd132842  |2 czenas 
655 9 |a electronic books  |2 eczenas 
655 9 |a monographs  |2 eczenas 
700 1 |a Linstedt, Dan,  |e author. 
856 4 0 |u https://proxy.k.utb.cz/login?url=https://www.sciencedirect.com/science/book/9780128020449  |y Plný text 
942 |2 udc 
992 |c NTK-Elsevier 
993 |x NEPOSILAT  |y EIZ 

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