000			03014cam a2200457Ii 4500
001			ocn896901332
003			OCoLC
005			20190328114809.0
006			m o d
007			cr |||||||||||
008			141105t20142015ne o 000 0 eng d
040			_aUKMGB _beng _erda _epn _cUKMGB _dN$T _dVRC _dOPELS _dOCLCF _dTEFOD _dOCLCQ _dTMC _dMERER _dOCLCQ _dOCLCO _dOCLCA _dGILDS _dU3W _dD6H
016	7		_a016955847 _2Uk
016	7		_a016945767 _2Uk
020			_a9780128020449 _q(electronic bk.)
020			_a012802044X _q(electronic bk.)
020			_a9780128020913 _q(electronic bk.)
020			_a0128020911 _q(electronic bk.)
035			_a(OCoLC)896901332
050		4	_aQA76.9.D37
072		7	_aCOM _x062000 _2bisacsh
082	0	4	_a005.745 _223
100	1		_aInmon, William H., _eauthor.
245	1	0	_aData architecture : a primer for the data scientist : big data, data warehouse and data vault / _h[electronic resource] _cWilliam H. Inmon, Dan Linstedt.
264		1	_aAmsterdam : _bElsevier, _c[2014]
264		4	_c�2015
300			_a1 online resource
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
520			_aToday, 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.
650		0	_aData warehousing.
650		0	_aBig data.
650		7	_aCOMPUTERS _xData Modeling & Design. _2bisacsh
650		7	_aBig data. _2fast _0(OCoLC)fst01892965
650		7	_aData warehousing. _2fast _0(OCoLC)fst00888026
650	1	2	_aData Collection.
655		4	_aElectronic books.
700	1		_aLinstedt, Dan, _eauthor.
856	4	0	_3ScienceDirect _uhttp://www.sciencedirect.com/science/book/9780128020449
999			_c247012 _d247012