Data Analysis and Data Modeling
for the Data Warehouse

~ Seminar Outline

  1. Introduction to Data Warehouse
    • Problems that lead to data warehouse
    • Goals and ojectives of data warehouse
  2. Data Warehouse Architecture
    • OLTP versus data warehouse
    • ODS vs. data warehouse vs. data mart
    • Data Mining
  3. Data Architecture
    • Logical data model
    • Physical data model
    • Distributed data model
    • Top-down vs. bottom-up vs. middle-out modeling
    • Meta data components
  4. Data Analysis
    • Analyzing the source data
    • Dirty data categories
    • Data cleansing issues
    • Types of transformations
  5. Basic Logical Data Model
    • Entities
    • Dependent vs. independent entities
    • Supertypes and subtypes
    • Relationships
    • Relationship matrix
    • High-level Entity-Relationship diagram
  6. Refinement of Logical Data Model
    • Logical data model
    • Cardinality
    • Associative entities (intersection data)
    • Optionality (existence criteria)
    • Primary keys
    • Foreign keys
    • Refined Entity-Relationship diagram
  7. Fully Attributed Logical Data Model
    • Descriptive attributes
    • Normalization
      (1NF, 2NF, 3NF, BCNF, 4NF, 5NF)
    • Final modeling considerations
  8. Data Warehouse Extensions
    • Derived attributes
    • Date/Time Period
    • Summarization and aggregation
    • Physical attributes
    • Access patterns and frequency
    • Performance considerations
  9. DW Database Design Alternatives
    • Overnormalization
    • Denormalization
    • Multi-dimensional design
    • Denormalized relational model
    • Denormalized star-schema model