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Building a Scalable Data Warehouse with Data Vault 2.0

Paperback Engels 2015 9780128025109
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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. View more >

Key Features
- 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

- Data Analysts, Business Intelligence and Data Warehousing Professionals, and Business Analysts


Trefwoorden:Data Warehousing
Aantal pagina's:684
Hoofdrubriek:IT-management / ICT


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Authors Biography

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

2: Scalable Data Warehouse Architecture
2.1. Dimensions of Scalable Data Warehouse Architectures
2.2. Data Vault 2.0 Architecture

3: The Data Vault 2.0 Methodology
3.1. Project Planning
3.2. Project Execution
3.3. Review and Improvement

4: Data Vault 2.0 Modeling
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

5: Intermediate Data Vault Modeling
5.1. Hub Applications
5.2. Link Applications
5.3. Satellite Applications

6: Advanced Data Vault Modeling
6.1. Point-in-Time Tables
6.2. Bridge Tables
6.3. Reference Tables

7: Dimensional Modeling
7.1. Introduction
7.2. Star Schemas
7.3. Multiple Stars
7.4. Dimension Design

8: Physical Data Warehouse Design
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

9: Master Data Management
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

10: Metadata Management
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

11: Data Extraction
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

12: Loading the Data Vault
12.1. Loading Raw Data Vault Entities
12.2. Loading Reference Tables
12.3. Truncating the Staging Area

13: Implementing Data Quality
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

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

15: Multidimensional Database
15.1. Accessing the Information Mart
15.2. Creating Dimensions
15.3. Creating Cubes
15.4. Accessing the Cube

Subject Index

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