Building a Scalable Data Warehouse with Data Vault 2.0

Authors Biography
Foreword
Preface
Acknowledgments
Chapter 1: Introduction to Data Warehousing
Abstract
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
Abstract
2.1. Dimensions of Scalable Data Warehouse Architectures
2.2. Data Vault 2.0 Architecture
Chapter 3: The Data Vault 2.0 Methodology
Abstract
3.1. Project Planning
3.2. Project Execution
3.3. Review and Improvement
Chapter 4: Data Vault 2.0 Modeling
Abstract
4.1. Introduction to Data Vault Modeling
4.2. Data Vault Modeling Vocabulary
4.3. Hub Definition
4.4. Link Definition
4.5. Satellite Definition
Chapter 5: Intermediate Data Vault Modeling
Abstract
5.1. Hub Applications
5.2. Link Applications
5.3. Satellite Applications
Chapter 6: Advanced Data Vault Modeling
Abstract
6.1. Point-in-Time Tables
6.2. Bridge Tables
6.3. Reference Tables
Chapter 7: Dimensional Modeling
Abstract
7.1. Introduction
7.2. Star Schemas
7.3. Multiple Stars
7.4. Dimension Design
Chapter 8: Physical Data Warehouse Design
Abstract
8.1. Database Workloads
8.2. Separate Environments for Development, Testing, and Production
8.3. Microsoft Azure Cloud Computing Platform
8.4. Physical Data Warehouse Architecture on Premise
8.5. Database Options
8.6. Setting up the Data Warehouse
Chapter 9: Master Data Management
Abstract
9.1. Definitions
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
9.7. Creating a Model
9.8. Importing a Model
9.9. Integrating MDS with the Data Vault and Operational Systems
Chapter 10: Metadata Management
Abstract
10.1. What is Metadata?
10.2. Implementing the Meta Mart
10.3. Implementing the Metrics Vault
10.4. Implementing the Metrics Mart
10.5. Implementing the Error Mart
Chapter 11: Data Extraction
Abstract
11.1. Purpose of Staging Area
11.2. Hashing in the Data Warehouse
11.3. Purpose of the Load Date
11.4. Purpose of the Record Source
11.5. Types of Data Sources
11.6. Sourcing Flat Files
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
Abstract
12.1. Loading Raw Data Vault Entities
12.2. Loading Reference Tables
12.3. Truncating the Staging Area
Chapter 13: Implementing Data Quality
Abstract
13.1. Business Expectations Regarding Data Quality
13.2. The Costs of Low Data Quality
13.3. The Value of Bad Data
13.4. Data Quality in the Architecture
13.5. Correcting Errors in the Data Warehouse
13.6. Transform, Enhance and Calculate Derived Data
13.7. Standardization of 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
Abstract
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
Abstract
15.1. Accessing the Information Mart
15.2. Creating Dimensions
15.3. Creating Cubes
15.4. Accessing the Cube
Subject Index