語系:
繁體中文
English
說明(常見問題)
圖資館首頁
登入
回首頁
切換:
標籤
|
MARC模式
|
ISBD
Data architecturea primer for the da...
~
Inmon, W. H.,
Data architecturea primer for the data scientist /
紀錄類型:
書目-電子資源 : Monograph/item
正題名/作者:
Data architectureW.H. Inmon, Daniel Linstedt, Mary Levins.
其他題名:
a primer for the data scientist /
作者:
Inmon, W. H.,
其他作者:
Linst, Daniel,
出版者:
London :Academic Press,2019.
面頁冊數:
1 online resource (xv, 416 p.) :ill.
附註:
Includes index.
標題:
Data warehousing.
電子資源:
https://www.sciencedirect.com/science/book/9780128169162
ISBN:
9780128169179 (electronic bk.)
Data architecturea primer for the data scientist /
Inmon, W. H.,
Data architecture
a primer for the data scientist /[electronic resource] :W.H. Inmon, Daniel Linstedt, Mary Levins. - Second Edition. - London :Academic Press,2019. - 1 online resource (xv, 416 p.) :ill.
Includes index.
1. Introduction to architecture<br>2. "Diagram of the world;, end state architecture<br>3. Transformation and redundancy<br>4. Big Data<br>5. Siloed applications<br>6. Data vault<br>7. Data lake, ponds, landing zone<br>8. IoT, Edge computing <br>9. Operational environment<br>10. The evolution of data architecture <br>11. Repetitive data, the sandbox <br>12. Non-repetitive data, contextualization <br>13. Operational performance <br>14. Integration of data <br>15. Personal computing <br>16. Managing text, taxonomies <br>17. System of record <br>18. The intellectual roadmap -- data modelling, taxonomies, etc. <br>19. Business value across the architecture <br>20. Virtualization, streaming <br>21. The end of evolution
Data Architecture: A Primer for the Data Scientist: Big Data, Data Warehouse and Data Vault, Second Edition, addresses how Big Data fits within the existing information infrastructure and data warehousing systems. This is an essential topic as researchers and engineers increasingly need to deal with large and complex sets of data. Until data is gathered and placed into an existing framework or architecture, it cannot be used to its full potential. Drawing upon years of practical experience and using numerous examples and case studies from across industries, the authors explain where Big Data fits, giving data scientists the necessary context for how pieces of the puzzle should fit together.
ISBN: 9780128169179 (electronic bk.)Subjects--Topical Terms:
199894
Data warehousing.
Index Terms--Genre/Form:
214472
Electronic books.
LC Class. No.: QA76.9.D37 / .I56 2019eb
Dewey Class. No.: 005.745
Data architecturea primer for the data scientist /
LDR
:02395cmm a2200277 a 4500
001
582202
006
o d
007
cnu|unuuu||
008
210121s2019 enk o 001 0 eng d
020
$a
9780128169179 (electronic bk.)
020
$a
0128169176 (electronic bk.)
020
$a
9780128169162
035
$a
(OCoLC)1099675091
035
$a
EL2020122
040
$a
N
$b
eng
$c
N
$d
N
$d
OPELS
$d
UKMGB
$d
OCLCF
$d
YDX
$d
CNO
$d
OCLCO
$d
OTZ
$d
OCL
$d
UKAHL
041
0
$a
eng
050
4
$a
QA76.9.D37
$b
.I56 2019eb
082
0 4
$a
005.745
$2
23
100
1
$a
Inmon, W. H.,
$e
author.
$3
872288
245
1 0
$a
Data architecture
$h
[electronic resource] :
$b
a primer for the data scientist /
$c
W.H. Inmon, Daniel Linstedt, Mary Levins.
250
$a
Second Edition.
260
$a
London :
$b
Academic Press,
$c
2019.
300
$a
1 online resource (xv, 416 p.) :
$b
ill.
500
$a
Includes index.
505
0
$a
1. Introduction to architecture<br>2. "Diagram of the world;, end state architecture<br>3. Transformation and redundancy<br>4. Big Data<br>5. Siloed applications<br>6. Data vault<br>7. Data lake, ponds, landing zone<br>8. IoT, Edge computing <br>9. Operational environment<br>10. The evolution of data architecture <br>11. Repetitive data, the sandbox <br>12. Non-repetitive data, contextualization <br>13. Operational performance <br>14. Integration of data <br>15. Personal computing <br>16. Managing text, taxonomies <br>17. System of record <br>18. The intellectual roadmap -- data modelling, taxonomies, etc. <br>19. Business value across the architecture <br>20. Virtualization, streaming <br>21. The end of evolution
520
$a
Data Architecture: A Primer for the Data Scientist: Big Data, Data Warehouse and Data Vault, Second Edition, addresses how Big Data fits within the existing information infrastructure and data warehousing systems. This is an essential topic as researchers and engineers increasingly need to deal with large and complex sets of data. Until data is gathered and placed into an existing framework or architecture, it cannot be used to its full potential. Drawing upon years of practical experience and using numerous examples and case studies from across industries, the authors explain where Big Data fits, giving data scientists the necessary context for how pieces of the puzzle should fit together.
588
0
$a
Online resource; title from PDF title page (EBSCO, viewed May 6, 2019).
650
0
$a
Data warehousing.
$3
199894
650
0
$a
Big data.
$3
609582
650
0
$a
Electronic data processing.
$3
201945
650
0
$a
Information retrieval.
$3
215224
655
4
$a
Electronic books.
$2
local.
$3
214472
700
1
$a
Linst, Daniel,
$e
author.
$3
872289
700
1
$a
Levins, Mary,
$e
author.
$3
872290
856
4 0
$u
https://www.sciencedirect.com/science/book/9780128169162
筆 0 讀者評論
全部
電子館藏
館藏
1 筆 • 頁數 1 •
1
條碼號
館藏地
館藏流通類別
資料類型
索書號
使用類型
借閱狀態
預約狀態
備註欄
附件
000000187045
電子館藏
1圖書
電子書
EB QA76.9.D37 .I56 2019eb 2019
一般使用(Normal)
在架
0
1 筆 • 頁數 1 •
1
多媒體
多媒體檔案
https://www.sciencedirect.com/science/book/9780128169162
評論
新增評論
分享你的心得
Export
取書館別
處理中
...
變更密碼
登入