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DW 304 – Advanced Dimensional Modeling

Duration: 3 – 5 days

Advanced Dimensional Modeling is an in-depth, technical course that teaches the advanced concepts for designing a dimensional Business Intelligence (BI) model for high-performance reporting and analytics solutions such as Business Performance Management (BPM), Customer Analytics, Supply Chain Analytics, etc.

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Description

Advanced Dimensional Modeling is an in-depth, technical course that teaches the advanced concepts for designing a dimensional Business Intelligence (BI) model for high-performance reporting and analytics solutions such as Business Performance Management (BPM), Customer Analytics, Supply Chain Analytics, etc.

This course will review data warehouse architecture and basic dimensional modeling concepts, but the primary focus will be on advanced dimensional modeling techniques and helping participants learn when to use which techniques. With hands-on workshops, attendees will transform end users’ reporting and analytics requirements into Dimensional Models (using a phased modeling approach) that are understandable to the business and will perform well in the face of unpredictable query patterns.

By learning the best way to design dimensional models for Business Intelligence or Business Performance systems, your designers and analysts will better capture business requirements and relate them to the supporting data structures. Hands-on workshops throughout the course will reinforce the learning experience and provide the attendees with concrete results that can be utilized in their organizations.

Objectives

  • Build dimensional models to address BI reporting and analytics requirements
  • Demonstrate how dimensional models address business requirements
  • Plan for capacity, security, and performance in your data warehouse

Seminar Content

  • Data Warehouse Systems Architecture
  • Building Dimensional Models
    • When to build Dimensional models … and when not to
    • Phased modeling approach (Conceptual, Logical, Dimensional, Physical)
    • Using requirements for informative communication throughout the project’s lifecycle
  • Advanced Dimensional Modeling
    • Dimensional Model Navigation
      • Case for Surrogate Keys
      • Additive, Semi-additive, and Non-additive Facts
      • Relationships between Fact Tables
      • Factless Fact Tables
    • DW Architectures (Inmon, Kimball, CIF, Hybrids)
      • Conformed Dimensions
      • Dimension Hierarchy Approaches
    • Temporal Perspectives in Dimensional Models
      • Fact Tables (Current vs. Historical View Approaches)
      • Slowly Changing Dimensions – Types 1, 2, 3 & 6
      • Date and Time Dimensions
    • Capacity Planning
    • Metadata Tags
    • Designing Security into the Dimensional Model (role & row)
    • Fact Qualifier Matrices
    • 8 Steps for Dimensional Models
    • When to Snowflake, and when not to
    • Managing Data Granularity in the Dimensional Model
    • Dimensional Challenges
      • Rapidly Changing Dimensions
      • Large Dimensions
      • Accommodating multiple views of data (Enterprise, Business Unit)
      • Ragged Hierarchies
      • Degenerate Dimensions
      • Junk Dimensions
      • Bridge Tables
      • Role Playing Dimensions
      • Audit Dimensions
      • Demographic Dimensions
      • Data Validity Dimensions
      • Value Band Reporting
  • Performance Tuning Approaches
    • Aggregation Methods
    • Indexing (b-tree, bitmap, join indexes, etc)
    • Partitioning
    • RDBMS Tuning (e.g. buffer size, sort heap size)
    • Massively Parallel Processing (MPP) platforms vs. Symmetrical Multiprocessing (SMP) platforms
    • Query Optimization (e.g. Star Transformation, Star Join)
  • Workshop Conclusion
    • Summary, additional exercises, resources for further reading, etc.

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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