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Data architecture : a primer for the data scientist : big data, data warehouse and data vault / (Record no. 247012)

000 -LEADER
fixed length control field 03014cam a2200457Ii 4500
001 - CONTROL NUMBER
control field ocn896901332
003 - CONTROL NUMBER IDENTIFIER
control field OCoLC
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20190328114809.0
006 - FIXED-LENGTH DATA ELEMENTS--ADDITIONAL MATERIAL CHARACTERISTICS
fixed length control field m o d
007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION
fixed length control field cr |||||||||||
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 141105t20142015ne o 000 0 eng d
040 ## - CATALOGING SOURCE
Original cataloging agency UKMGB
Language of cataloging eng
Description conventions rda
-- pn
Transcribing agency UKMGB
Modifying agency N$T
-- VRC
-- OPELS
-- OCLCF
-- TEFOD
-- OCLCQ
-- TMC
-- MERER
-- OCLCQ
-- OCLCO
-- OCLCA
-- GILDS
-- U3W
-- D6H
016 7# - NATIONAL BIBLIOGRAPHIC AGENCY CONTROL NUMBER
Record control number 016955847
Source Uk
016 7# - NATIONAL BIBLIOGRAPHIC AGENCY CONTROL NUMBER
Record control number 016945767
Source Uk
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9780128020449
Qualifying information (electronic bk.)
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 012802044X
Qualifying information (electronic bk.)
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9780128020913
Qualifying information (electronic bk.)
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 0128020911
Qualifying information (electronic bk.)
035 ## - SYSTEM CONTROL NUMBER
System control number (OCoLC)896901332
050 #4 - LIBRARY OF CONGRESS CALL NUMBER
Classification number QA76.9.D37
072 #7 - SUBJECT CATEGORY CODE
Subject category code COM
Subject category code subdivision 062000
Source bisacsh
082 04 - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 005.745
Edition number 23
100 1# - MAIN ENTRY--PERSONAL NAME
Personal name Inmon, William H.,
Relator term author.
245 10 - TITLE STATEMENT
Title Data architecture : a primer for the data scientist : big data, data warehouse and data vault /
Medium [electronic resource]
Statement of responsibility, etc. William H. Inmon, Dan Linstedt.
264 #1 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE
Place of production, publication, distribution, manufacture Amsterdam :
Name of producer, publisher, distributor, manufacturer Elsevier,
Date of production, publication, distribution, manufacture, or copyright notice [2014]
264 #4 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE
Date of production, publication, distribution, manufacture, or copyright notice �2015
300 ## - PHYSICAL DESCRIPTION
Extent 1 online resource
336 ## - CONTENT TYPE
Content type term text
Content type code txt
Source rdacontent
337 ## - MEDIA TYPE
Media type term computer
Media type code c
Source rdamedia
338 ## - CARRIER TYPE
Carrier type term online resource
Carrier type code cr
Source rdacarrier
520 ## - SUMMARY, ETC.
Summary, etc. 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.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Data warehousing.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Big data.
650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element COMPUTERS
General subdivision Data Modeling & Design.
Source of heading or term bisacsh
650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Big data.
Source of heading or term fast
Authority record control number (OCoLC)fst01892965
650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Data warehousing.
Source of heading or term fast
Authority record control number (OCoLC)fst00888026
650 12 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Data Collection.
655 #4 - INDEX TERM--GENRE/FORM
Genre/form data or focus term Electronic books.
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Linstedt, Dan,
Relator term author.
856 40 - ELECTRONIC LOCATION AND ACCESS
Materials specified ScienceDirect
Uniform Resource Identifier http://www.sciencedirect.com/science/book/9780128020449

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Last Updated on September 15, 2019
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