DATA WAREHOUSING
by Pamela Martínez
1. Types
1.1. Data marts
1.1.1. Dependent
1.1.1.1. Subset created directly from DW
1.1.2. Independent
1.1.2.1. Small WH designed for SBU
1.2. Operational Data Stores (ODS)
1.3. Enterprise Data Warehouses (EDW)
2. Components of DW process
2.1. Data extraction and transformation
2.1.1. ETL
2.1.1.1. Extract
2.1.1.2. Transform
2.1.1.3. Load
2.2. Data sources
2.3. Data loading
2.4. Comprehensive database
2.5. Metadata
2.5.1. SW programs
2.6. Middleware tools
2.6.1. SQL queries
3. Data Warehousing Architecture
3.1. Tier 1
3.1.1. Client workstation
3.2. Tier 2
3.2.1. Application and DB server
3.3. Alternative DW Architectures
3.3.1. Independent Datamart
3.3.2. Data mart bus architecture linked w/dimensional data marts
3.3.3. Hub and Spoke
3.3.4. Centralized data warehouse
3.3.5. Federated Architecture
4. Data integration
4.1. Data Access
4.2. Data Federation
4.3. Change capture
5. Representation of data
5.1. Dimensional Modeling
5.2. Star Schema
5.3. Snowflake Schema
6. Metadata
6.1. EAI
6.1.1. EII
6.1.1.1. SOA
7. Totally integrated
8. Decision support
9. Improved analytical capabilities
10. Characteristics
10.1. Subject oriented
10.2. Integrated
10.3. Time variant (time series)
10.4. Nonvolatile
11. Allows ready access to business information
11.1. Creates business insights
11.1.1. Scalability
12. Analysis
12.1. OLTP
12.1.1. OLAP
12.1.1.1. ROLAP
12.1.1.2. MOLAP
12.1.1.3. HOLAP
13. Real-time data warehousing (RDW)
13.1. Active data warehousing (ADW)
13.2. Loading data
13.2.1. Providing data