Building a Scalable Data Warehouse with Data Vault 2.0

Building a Scalable Data Warehouse with Data Vault 2.0

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Description

Building a Scalable Data Warehouse with Data Vault 2.0 covers everything users need to create a scalable data warehouse from scratch, including a presentation of the Data Vault modeling technique which provides the foundations to create a technical data warehouse layer. In addition, the book presents tactics on how to create the input layer (the stage layer) and the presentation layer (data mart) of the Data Vault 2.0 standard. Drawing upon years of practical experience and using numerous examples and an easy to understand framework, authors Dan Listedt and Michael Olschimke discuss tactics on 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, and data quality (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 warehouseDemystifies data vault modeling with beginning, intermediate, and advanced techniquesDiscusses 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
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Product details

  • Electronic book text | 724 pages
  • 191 x 235mm
  • Morgan Kaufmann Publishers In
  • San Francisco, United States
  • English
  • 0128026480
  • 9780128026489

Table of contents

Chapter 1: Introduction to Data Warehousing
Chapter 2: Scalable Data Warehouse Architecture
Chapter 3: Agile Data Warehouse Development Methodology
Chapter 4: The Data Vault 2.0 Methodology
Chapter 5: Data Vault Modeling
Chapter 6: Intermediate Data Vault Modeling
Chapter 7: Advanced Data Vault Modeling
Chapter 8: Dimensional Modeling
Chapter 9: Physical Database Design
Chapter 10: Master Data Management
Chapter 11: Metadata Management
Chapter 12: Data Extraction
Chapter 13: Loading the Data Vault
Chapter 14: Implementing Data Quality in the Data Vault
Chapter 15: Loading the Dimensional Data Mart
Chapter 16: Relational Reports (Optional)
Chapter 17: Multidimensional Database
Chapter 18: Refactoring the Data Warehouse
Chapter 19: Data Mining (Optional)
Chapter 20: Data Warehouse Administration (Optional)
Chapter 21: Best Practices (Optional)
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Rating details

6 ratings
4.16 out of 5 stars
5 33% (2)
4 50% (3)
3 17% (1)
2 0% (0)
1 0% (0)
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