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Data architecture = a primer for the...

Inmon, W. H.,

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Data architecture = a primer for the data scientist /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Data architecture/ W.H. Inmon, Daniel Linstedt, Mary Levins.
Reminder of title:	a primer for the data scientist /
Author:	Inmon, W. H.,
other author:	Linst, Daniel,
Published:	London :Academic Press, : 2019.,
Description:	1 online resource (xv, 416 p.) :ill.
Notes:	Includes index.
[NT 15003449]:	1. Introduction to architecture 2. "Diagram of the world;, end state architecture 3. Transformation and redundancy 4. Big Data 5. Siloed applications 6. Data vault 7. Data lake, ponds, landing zone 8. IoT, Edge computing 9. Operational environment 10. The evolution of data architecture 11. Repetitive data, the sandbox 12. Non-repetitive data, contextualization 13. Operational performance 14. Integration of data 15. Personal computing 16. Managing text, taxonomies 17. System of record 18. The intellectual roadmap -- data modelling, taxonomies, etc. 19. Business value across the architecture 20. Virtualization, streaming 21. The end of evolution
Subject:	Data warehousing. -
Online resource:	https://www.sciencedirect.com/science/book/9780128169162
ISBN:	9780128169179 (electronic bk.)

Data architecture = a primer for the data scientist /
Inmon, W. H.,

Data architecturea 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 2. "Diagram of the world;, end state architecture 3. Transformation and redundancy 4. Big Data 5. Siloed applications 6. Data vault 7. Data lake, ponds, landing zone 8. IoT, Edge computing 9. Operational environment 10. The evolution of data architecture 11. Repetitive data, the sandbox 12. Non-repetitive data, contextualization 13. Operational performance 14. Integration of data 15. Personal computing 16. Managing text, taxonomies 17. System of record 18. The intellectual roadmap -- data modelling, taxonomies, etc. 19. Business value across the architecture 20. Virtualization, streaming 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:

606996
Data warehousing.
Index Terms--Genre/Form:

542853
Electronic books.

LC Class. No.: QA76.9.D37 / .I56 2019eb

Dewey Class. No.: 005.745

Data architecture = a primer for the data scientist /
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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 2. "Diagram of the world;, end state architecture 3. Transformation and redundancy 4. Big Data 5. Siloed applications 6. Data vault 7. Data lake, ponds, landing zone 8. IoT, Edge computing 9. Operational environment 10. The evolution of data architecture 11. Repetitive data, the sandbox 12. Non-repetitive data, contextualization 13. Operational performance 14. Integration of data 15. Personal computing 16. Managing text, taxonomies 17. System of record 18. The intellectual roadmap -- data modelling, taxonomies, etc. 19. Business value across the architecture 20. Virtualization, streaming 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).
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700 1 $a Levins, Mary, $e author. $3 3462788
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