Table of Contents
Chapter 1: Introduction to Data Warehousing
1.1. History of Data Warehousing
1.2. The Enterprise Data Warehouse Environment
1.3. Introduction to Data Vault 2.0
1.4. Data Warehouse Architecture
Chapter 2: Scalable Data Warehouse Architecture
2.1. Dimensions of Scalable Data Warehouse Architectures
2.2. Data Vault 2.0 Architecture
Chapter 3: The Data Vault 2.0 Methodology
Chapter 4: Data Vault 2.0 Modeling
4.1. Introduction to Data Vault Modeling
4.2. Data Vault Modeling Vocabulary
Chapter 5: Intermediate Data Vault Modeling
Chapter 6: Advanced Data Vault Modeling
Chapter 7: Dimensional Modeling
Chapter 8: Physical Data Warehouse Design
8.2. Separate Environments for Development, Testing, and Production
8.3. Microsoft Azure Cloud Computing Platform
8.4. Physical Data Warehouse Architecture on Premise
8.6. Setting up the Data Warehouse
Chapter 9: Master Data Management
9.2. Master Data Management Goals
9.3. Drivers for Managing Master Data
9.4. Operational vs. Analytical Master Data Management
9.5. Master Data Management as an Enabler for Managed Self-Service BI
9.6. Master Data Management as an Enabler for Total Quality Management
9.9. Integrating MDS with the Data Vault and Operational Systems
Chapter 10: Metadata Management
10.2. Implementing the Meta Mart
10.3. Implementing the Metrics Vault
10.4. Implementing the Metrics Mart
10.5. Implementing the Error Mart
11.2. Hashing in the Data Warehouse
11.3. Purpose of the Load Date
11.4. Purpose of the Record Source
11.7. Sourcing Historical Data
11.8. Sourcing the Sample Airline Data
11.9. Sourcing Denormalized Data Sources
11.10. Sourcing Master Data from MDS
Chapter 12: Loading the Data Vault
12.1. Loading Raw Data Vault Entities
12.2. Loading Reference Tables
12.3. Truncating the Staging Area
Chapter 13: Implementing Data Quality
13.1. Business Expectations Regarding Data Quality
13.2. The Costs of Low Data Quality
13.4. Data Quality in the Architecture
13.5. Correcting Errors in the Data Warehouse
13.6. Transform, Enhance and Calculate Derived Data
13.8. Correct and Complete Data
13.9. Match and Consolidate Data
13.10. Creating Dimensions from Same-As Links
Chapter 14: Loading the Dimensional Information Mart
14.1. Using the Business Vault as an Intermediate to the Information Mart
14.2. Materializing the Information Mart
14.3. Leveraging PIT and Bridge Tables for Virtualization
14.4. Implementing Temporal Dimensions
14.5. Implementing Data Quality Using PIT Tables
14.6. Dealing with Reference Data
14.7. About Hash Keys in the Information Mart
Chapter 15: Multidimensional Database