2.Definition, Architecture And Concepts

Enterprise Data Model
Operational vs. historical data
Extract Transform Load (ETL)
Metadata
Data warehouse vs. data mart
Data mining
OLAP vs. OLTP
Massive size implementation
Logical design vs. physical design
Normalization vs. denormalization
Referential constraints


3.Data Modelling Options

Entity model
Star schema
Snowflake schema

4.Dimensional Modelling Development Life Cycle

Requirements analysis
Requirements gathering
Requirements validation
Requirements modelling
Schema design
Project definition
Warehouse design
Implementation
Follow-up and review

5. Dimensional Modelling Design

Overview
Metadata properties
Star schema
Snowflake schema
Cubes
Measures and facts
Attributes and relationships
Dimension
Hierarchies
Role-playing dimensions
Joins
Summary tables and aggregation
Exercises

6. Implementation Options

Overview
Top down
Bottom up
Sizing
Cleaning
Populating the data warehouse

7. Extract, Transform, Load (Etl) Terms And Concepts

Options
Extraction options
Transformation options
Loading options
Change Data Capture and publishing
Staging areas

8. Extracting

Logical-to-physical data mapping
Disparate (heterogeneous) data sources
Extracting changes data ?delta or other

9. Data Cleaning And Conforming

Data quality criteria
Design methods and alternatives
Cleaning deliverables
Conforming dimension tables
Conforming fact tables

10. Dimension Table Delivery

Dimension table structure
Surrogate key generation
Dimension table grain
Flat (denormalized) or snowflake?
Data and time dimensions
態ig?vs. 憇mall?dimensions
Dimensional roles
Dimensions as subdimensions
Degenerate dimensions

11. Slowly Changing Dimensions And Multivalued Dimensions

Type 1
Type 2
Type 3
Hybrid
Late arrivals
Definition
Bridge tables

12. Fact Table Delivery

Fact table structure
Referential integrity (RI)
Surrogate key derivation and flow
Fundamental grain
Transaction fact tables
Factless fact tables
Periodic snapshots
Accumulating snapshots

13. fact table load considerations

Index management
Partition management
Updates, deletes and inserts
Recovery
Summary tables
Parallelism

14. Data Warehouse Performance Design

Materialized views
Large concurrent reports
Short running queries
Long running queries
Random queries
Occasional updates
On-line utilities
Index options
Partitioning and parallelism (e.g., LOADs)

15. Introduction To Statistics, Analytic And Olap Sql Queries

AVG
CORRELATION
COUNT
COUNT_BIG
CONVARIANCE
MAX
MIN
RAND
STDDEV
SUM
VARIANCE
Regression function
GROUPING, ROLLUP AND CUBE

16. Physical Design Considerations

Denormalization
Index choices
Data placement
Free space
Summary tables
Data compression