Overview

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 QADMBIS - Developing A Modern Business Intelligence System should not attend this course.

Prerequisites

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.

Learning Outcomes

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

Course Outline

  • 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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