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Linstedt Daniel (52 results)
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Condition: good. Book is in good condition and may include underlining highlighting and minimal wear. The book can also include "From the library of" labels. May not contain miscellaneous items toys, dvds, etc. . We offer 100% money back guarantee and 24 7 customer service.
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Data Architecture: A Primer for the Data Scientist: Big Data, Data Warehouse and Data Vault
Published by Morgan Kaufmann Publishers, 2014
ISBN 10: 012802044X ISBN 13: 9780128020449
Language: English
Paperback. Condition: Good. No Jacket. Pages can have notes/highlighting. Spine may show signs of wear. ~ ThriftBooks: Read More, Spend Less.
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Condition: Very Good. 1749436112. 6/9/2025 2:28:32 AM.
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Building a Scalable Data Warehouse with Data Vault 2.0
Published by Morgan Kaufmann Publishers, 2015
ISBN 10: 0128025107 ISBN 13: 9780128025109
Language: English
Paperback. Condition: Fair. No Jacket. Readable copy. Pages may have considerable notes/highlighting. ~ ThriftBooks: Read More, Spend Less.
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Condition: good. Spine is twisted bowed. Cover Case has some rubbing and edgewear. Access codes, CD's, slipcovers and other accessories may not be included.
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Condition: very_good. Gently read. May have name of previous ownership, or ex-library edition. Binding tight; spine straight and smooth, with no creasing; covers clean and crisp. Minimal signs of handling or shelving. 100% GUARANTEE! Shipped with delivery confirmation, if you're not satisfied with purchase please return item for full refund. Ships USPS Media Mail.
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Data Architecture: A Primer for the Data Scientist: Big Data, Data Warehouse and Data Vault
Seller: Wm Burgett Bks and Collectibles, San diego, CA, U.S.A.
Seller rating 4 out of 5 stars
First Edition
Paperback. Condition: Near Fine. First Edition. CLEAN near fine 2015 paperback.
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Condition: New.
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Data Architecture: A Primer for the Data Scientist: Big Data, Data Warehouse and Data Vault
Published by Morgan Kaufmann 2014-11-26, 2014
ISBN 10: 012802044X ISBN 13: 9780128020449
Language: English
Paperback. Condition: New.
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Data Architecture: A Primer for the Data Scientist
Seller: PBShop.store UK, Fairford, GLOS, United Kingdom
Seller rating 5 out of 5 stars
PAP. Condition: New. New Book. Shipped from UK. Established seller since 2000.
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Condition: New.
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PAP. Condition: New. New Book. Shipped from UK. Established seller since 2000.
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Building a Scalable Data Warehouse with Data Vault 2.0
Seller: PBShop.store UK, Fairford, GLOS, United Kingdom
Seller rating 5 out of 5 stars
PAP. Condition: New. New Book. Shipped from UK. Established seller since 2000.
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Condition: As New. Unread book in perfect condition.
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Condition: New.
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Condition: New. pp. 640.
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Condition: New. pp. 450.
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Condition: New.
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Paperback. Condition: Brand New. 2nd edition. 450 pages. 9.25x7.50x0.83 inches. In Stock.
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Data Architecture: A Primer for the Data Scientist: A Primer for the Data Scientist
Seller: Ria Christie Collections, Uxbridge, United Kingdom
Seller rating 5 out of 5 stars
Condition: New. In.
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Building a Scalable Data Warehouse with Data Vault 2.0
Seller: Ria Christie Collections, Uxbridge, United Kingdom
Seller rating 5 out of 5 stars
Condition: New. In.
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Data Architecture : A Primer for the Data Scientist
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
Seller rating 5 out of 5 stars
Condition: New.
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Building a Scalable Data Warehouse with Data Vault 2.0
Published by MORGAN KAUFMANN PUBL INC, 2015
ISBN 10: 0128025107 ISBN 13: 9780128025109
Language: English
Condition: New. Idioma/Language: Inglés. The Data Vault was invented by Dan Linstedt at the U. S. Department of Defense, and the standard has been successfully applied to data warehousing projects at organizations of different sizes, from small to large-size corporations. Due to its simplified design, which is adapted from nature, the Data Vault 2. 0 standard helps prevent typical data warehousing failures. " Building a Scalable Data Warehouse " covers everything one needs to know to create a scalable data warehouse end to end, including a presentation of the Data Vault modeling technique, which provides the foundations to create a technical data warehouse layer. The book discusses how to build the data warehouse incrementally using the agile Data Vault 2. 0 methodology. In addition, readers will learn how to create the input layer (the stage layer) and the presentation layer (data mart) of the Data Vault 2. 0 architecture including implementation best practices. Drawing upon years of practical experience and using numerous examples and an easy to understand framework, Dan Linstedt and Michael Olschimke discuss: How to load each layer using SQL Server Integration Services (SSIS), including automation of the Data Vault loading processes. Important data warehouse technologies and practices. Data Quality Services (DQS) and Master Data Services (MDS) in the context of the Data Vault architecture. Provides a complete introduction to data warehousing, applications, and the business context so readers can get-up and running fast Explains theoretical concepts and provides hands-on instruction on how to build and implement a data warehouse Demystifies data vault modeling with beginning, intermediate, and advanced techniques Discusses the advantages of the data vault approach over other techniques, also including the latest updates to Data Vault 2. 0 and multiple improvements to Data Vault 1. 0 *** Nota: Los envíos a España peninsular, Baleares y Canarias se realizan a través de mensajería urgente. No aceptamos pedidos con destino a Ceuta y Melilla.
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Condition: New. pp. 450.
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Condition: Nuevo. - Tecnología.
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Data Architecture : A Primer for the Data Scientist
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
Seller rating 5 out of 5 stars
Condition: As New. Unread book in perfect condition.
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Condition: New. pp. 640.
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Condition: Nuevo. Building a scalable data warehouse with data vault 2.0 editado por Elsevier uk.
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Data Architecture: A Primer for the Data Scientist
Published by Elsevier Science Publishing Co Inc, US, 2019
ISBN 10: 0128169168 ISBN 13: 9780128169162
Language: English
Seller: Rarewaves.com USA, London, LONDO, United Kingdom
Seller rating 5 out of 5 stars
Paperback. Condition: New. Over the past 5 years, the concept of big data has matured, data science has grown exponentially, and data architecture has become a standard part of organizational decision-making. Throughout all this change, the basic principles that shape the architecture of data have remained the same. There remains a need for people to take a look at the "bigger picture" and to understand where their data fit into the grand scheme of things. Data Architecture: A Primer for the Data Scientist, Second Edition addresses the larger architectural picture of how big data fits within the existing information infrastructure or data warehousing systems. This is an essential topic not only for data scientists, analysts, and managers but also for researchers and engineers who increasingly need to deal with large and complex sets of data. Until data are gathered and can be placed into an existing framework or architecture, they cannot be used to their full potential. Drawing upon years of practical experience and using numerous examples and case studies from across various industries, the authors seek to explain this larger picture into which big data fits, giving data scientists the necessary context for how pieces of the puzzle should fit together.
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Data Architecture: A Primer for the Data Scientist
Published by Elsevier Science Publishing Co Inc, US, 2019
ISBN 10: 0128169168 ISBN 13: 9780128169162
Language: English
Paperback. Condition: New. Over the past 5 years, the concept of big data has matured, data science has grown exponentially, and data architecture has become a standard part of organizational decision-making. Throughout all this change, the basic principles that shape the architecture of data have remained the same. There remains a need for people to take a look at the "bigger picture" and to understand where their data fit into the grand scheme of things. Data Architecture: A Primer for the Data Scientist, Second Edition addresses the larger architectural picture of how big data fits within the existing information infrastructure or data warehousing systems. This is an essential topic not only for data scientists, analysts, and managers but also for researchers and engineers who increasingly need to deal with large and complex sets of data. Until data are gathered and can be placed into an existing framework or architecture, they cannot be used to their full potential. Drawing upon years of practical experience and using numerous examples and case studies from across various industries, the authors seek to explain this larger picture into which big data fits, giving data scientists the necessary context for how pieces of the puzzle should fit together.
