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Dimensional Modelling

Dimensional modelling is an integral part of any BI (Business Intelligence) system and can be used within the data warehouse and/or the data marts. This 3 day course assumes no prior knowledge of dimensional modelling. It starts by discussing what a data warehouse is, how they are designed and the part that dimensional modelling plays.

The vitally important process of requirement gathering is covered and delegates are shown how to:

  •   Collect the analytical requirements of the business users
  •   Create a logical model of these requirements
  •   Create a star schema from those requirements

The relational and dimensional models are compared and contrasted, with particular reference to the current Kimball/Inmon debate.

The course then looks in great detail at dimensional modelling itself and finally ends with a summary of possible BI architectures.

Target Audience:

This course is aimed at people who work in the BI area. It is suitable for business analysts who need to understand the analytical requirements and turn those requirements into a model. It is also suitable for the IT professional who will turn those models into working OLAP (On-Line Analytical Processing) structures. Please note, delegates who have previously attended Developing A Modern Business Intelligence System should not attend this course.


Learning Objectives

At the end of this course you will be able to:

  •   Understand the pros and cons of relational and dimensional modelling
  •   Design dimensional models from analytical business requirements
  •   Produce effective star schemas that deliver the analytical capabilities that the business requires


There are no specific pre-requisites for this course but delegates who have previously attended QADMBIS - 'Developing A Modern Business Intelligence System' should not attend this course due to the duplication of content.

Course Content

  •   Introduction to designing dimensional data warehouses
  •   Gathering analytical requirements
  •   Measures and dimensions
  •   Logical (Sun) modelling
  •   Physical modelling - the star schema
  •   Facts and dimensions
  •   Attributes and hierarchies
  •   Time dimensions
  •   Synonym dimensions
  •   Surrogate keys
  •   Additive, semi-additive and non-additive measures
  •   Degenerate dimensions
  •   Slowly changing dimensions
  •   Bridge tables
  •   Mini dimensions
  •   Hot-swappable dimensions
  •   Multi-valued dimensions
  •   Parent child dimensions
  •   Bitmap dimensions
  •   Ragged hierarchies
  •   Unbalanced hierarchies
  •   Step dimensions
  •   First and last analysis
  •   Optimizing fact table performance
  •   Indexing in star schema
  •   Aggregation
  •   MOLAP
  •   HOLAP
  •   ROLAP
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