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DW 302 – Data Modeling For Data Warehousing / Business Intelligence

Duration: 3 – 4 days

Data Modeling for Data Warehousing / Business Intelligence is an in-depth, technical course that teaches the fundamental concepts for modeling a data warehouse, operational data store, and a data mart or other decision support application.

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Description

Data Modeling for Data Warehousing / Business Intelligence is an in-depth, technical course that teaches the fundamental concepts for modeling a data warehouse, operational data store, and a data mart or other decision support application.

Using 3rd normal form, attendees will learn how to represent the data requirements for an atomic data warehouse or an operational data store so that the resulting database is designed appropriately. Attendees will also learn the basics of how to design data marts for business intelligence applications using multi-dimensional techniques (star and snowflake schema). With hands-on workshops, attendees will transform end users’ requirements into data warehouse/data mart models.

Objectives

  • Understand the differences between data models and data structures for transactional systems versus decision support systems
  • Learn how to construct a conceptual data model for decision support
  • Learn how to construct normalized logical and physical data models for an atomic data warehouse and/or operational data store
  • Understand how to build multi-dimensional models (star and snowflake) for a data mart
  • Construct data mode

Seminar Content

  • Data Warehousing Fundamentals
    • Differences between transactional and data warehouse systems
    • Characteristics of data warehouses
    • Elements of a data warehouse architecture
      • Atomic data warehouse
      • Data mart
      • Operational data store
  • What is a Data Model and Why Model Data?
    • Role of modeling in architectural definition
  • Types of Data Models
    • Conceptual Data Model (CDM)
    • Logical Data Model (LDM)
    • Physical Data Model (PDM)
  • Data Models and Quality
    • What is information?
    • What is quality?
    • How do data models support information quality?
  • Data Modeling Terminology and Notation
    • Definitions of key terms used in data models
    • Information Engineering notation used in data models
  • Managing Redundancy and Efficiency
    • Normalization
    • De-normalization
    • Views
    • Indexes
  • Phased Modeling Approach
    • Why develop models in the order CDM -> LDM -> PDM?
  • Conceptual Data Modeling (CDM)
    • Purposes of CDM
    • How to develop CDM
    • Issues if CDM is ignored
  • Logical Data Modeling (LDM)
    • Purposes of LDM
    • How to develop LDM
    • Resolving M:M relationships
    • Resolving super-type / subtype relationships
    • Abstraction
    • Normalization
  • Physical Data Modeling
    • Purposes of PDM
    • How to develop PDM
  • Dimensional Schemas and Architectures
    • Why dimensional?
    • Facts and dimensions
    • Stars, snowflakes and galaxies
    • Fact qualifier matrices
  • Metadata and Modeling
    • Types of metadata
  • Standards
  • Technical metadata tags
  • Handling Historical Data in the Data Warehouse
    • Effective dated tables
    • Slowly changing dimensions
  • Data Model Presentation
    • Making the models understandable
  • Optimizing the Physical Model for Performance
    • Types of indexes and their uses
    • Types of partitioning and their uses
  • Conclusion

About the Course Designer

This training was designed by David Marco, PhD, an internationally recognized authority on data and AI governance, to help teams succeed in real organizational conditions. The curriculum equips participants with practical judgment, shared language, and decision clarity that hold under scale, risk, and executive accountability.

David Marco PHD EWSolutions

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