Książki

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

Building a Scalable Data Warehouse with Data Vault 2.0

Chapter 1. Introduction to Data Warehousing Chapter 2. Scalable Data Warehouse Architecture Chapter 3. The Data Vault 2.0 Methodology Chapter 4. Data Vault 2.0 Modeling Chapter 5. Intermediate Data Vault Modeling Chapter 6. Advanced Data Vault Modeling Chapter 7. Dimensional Modeling Chapter 8. Physical Data Warehouse Design Chapter 9. Master Data Managment Chapter 10. Metadata Managment Chapter 11. Data Extraction Chapter 12. Loading the Data Vault Chapter 13. Implementing Data Quality Chapter 14. Loading the Dimensional Information Mart Chapter 15. Multidemensional Database