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Building a Scalable Data Warehouse with Data Vault 2.0
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- Sofort-KaufenBuilding a Scalable Data Warehouse with Data Vault 2.0
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eBay-Artikelnr.:286810369040
Artikelmerkmale
- Artikelzustand
- Release Year
- 2015
- Book Title
- Building a Scalable Data Warehouse with Data Vault 2.0
- ISBN
- 9780128025109
Über dieses Produkt
Product Identifiers
Publisher
Elsevier Science & Technology
ISBN-10
0128025107
ISBN-13
9780128025109
eBay Product ID (ePID)
211821030
Product Key Features
Number of Pages
684 Pages
Publication Name
Building a Scalable Data Warehouse with Data Vault 2. 0
Language
English
Publication Year
2015
Subject
Management Information Systems, Data Processing, Databases / Data Warehousing, Databases / General
Type
Textbook
Subject Area
Computers
Format
Trade Paperback
Dimensions
Item Length
9.2 in
Item Width
7.5 in
Additional Product Features
Intended Audience
College Audience
Table Of Content
Chapter 1. Introduction to Data WarehousingChapter 2. Scalable Data Warehouse ArchitectureChapter 3. The Data Vault 2.0 MethodologyChapter 4. Data Vault 2.0 ModelingChapter 5. Intermediate Data Vault ModelingChapter 6. Advanced Data Vault ModelingChapter 7. Dimensional ModelingChapter 8. Physical Data Warehouse DesignChapter 9. Master Data Managment Chapter 10. Metadata Managment Chapter 11. Data ExtractionChapter 12. Loading the Data Vault Chapter 13. Implementing Data Quality Chapter 14. Loading the Dimensional Information MartChapter 15. Multidemensional Database
Synopsis
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, 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.
LC Classification Number
QA76.9.D37
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