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
"synopsis" may belong to another edition of this title.
About the Authors
Dan Linstedt has more than 25 years of experience in the Data Warehousing and Business Intelligence field and is internationally known for inventing the Data Vault 1.0 model and the Data Vault 2.0 System of Business Intelligence. He helps business and government organizations around the world to achieve BI excellence by applying his proven knowledge in Big Data, unstructured information management, agile methodologies and product development. He has held training classes and presented at TDWI, Teradata Partners, DAMA, Informatica, Oracle user groups and Data Modeling Zone conference. He has a background in SEI/CMMI Level 5, and has contributed architecture efforts to petabyte scale data warehouses and offers high quality on-line training and consulting services for Data Vault.
Michael has more than 15 years of experience in IT and has been working on business intelligence topics for the past eight years. He has consulted for a number of clients in the automotive industry, insurance industry and non-profits. In addition, he has consulted for government organizations in Germany on business intelligence topics. Michael is responsible for the Data Vault training program at Dörffler + Partner GmbH, a German consulting firm specialized in data warehousing and business intelligence. He is also a lecturer at the University of Applied Sciences and Arts in Hannover, Germany. In addition, he maintains DataVault.guru, a community site on Data Vault topics.
"About this title" may belong to another edition of this title.
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Building a Scalable Data Warehouse with Data Vault 2.0
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Softcover. Condition: Fine. Leichte Rillen / Abschürfungen / Risse / Knicke; Gebrochener Buchrücken. The Data Vault, created by Dan Linstedt at the U.S. Department of Defense, has been effectively implemented in data warehousing projects across various organizations. Its nature-inspired, simplified design helps mitigate common data warehousing failures. This resource provides comprehensive guidance on building a scalable data warehouse from start to finish, focusing on the Data Vault modeling technique that underpins a technical data warehouse layer. It emphasizes an incremental approach using the agile Data Vault 2.0 methodology. Readers will learn to construct both the input layer (stage layer) and the presentation layer (data mart) within the Data Vault 2.0 architecture, along with best practices for implementation. Drawing from extensive practical experience, the authors present numerous examples within an accessible framework. Key topics include loading each layer using SQL Server Integration Services (SSIS) and automating Data Vault loading processes. The book also covers essential data warehouse technologies, Data Quality Services (DQS), and Master Data Services (MDS) in relation to the Data Vault architecture. It offers a thorough introduction to data warehousing, blending theoretical concepts with hands-on instruction, while demystifying data vault modeling through various techniques. Additionally, it highlights the advantages of the data vault approach over other methods, including updates and enhancements to Data V. Seller Inventory # 94b2dcf8-bd3b-403c-b9d6-3b4e4197384d
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Paperback. Condition: New. 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. Seller Inventory # LU-9780128025109
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