Data Warehouse Design Workshop

Why take this course?

After completing this course, you will be able to
  • apply a set of techniques for gathering user warehouse requirements.
  • transform an operational data model into a warehouse data model.
  • apply design techniques to optimize the physical warehouse model.
  • understand the star schema technique and its strengths and weaknesses.
  • ensure that your first data warehouse project is a success.
2 Days : Fee - $ 1,397.00 (CAD) : ? Multi-seat - $ 897.00 (CAD)
Sorry, no classes are currently scheduled
Contact us to arrange your preferred location and date
This course presents powerful, practical and repeatable techniques for analyzing and designing real-life Data Warehouse solutions. Through numerous exercises and an extensive final case study, you will learn how to analyze and model data for building warehouse databases that satisfy practical business needs.
Who should attend?
Business and systems analysts who will be involved in the analysis and design of Data Warehouses.
Any prerequisites?
You should have a basic understanding of Data Warehouse and Data Modelling concepts or have completed the IB-Learning courses Introduction to Data Warehouse and Data Modelling Workshop or equivalent training.

What topics are covered?

Introduction
  • Today's Business
  • IT Challenges
  • Data Warehouse
  • Data Warehouse Development
Gathering User Requirements
  • Framework
  • Gathering User Requirements
  • Modelling Concepts
  • Gathering User Requirements
  • Interviewing Users
  • Analyzing User Requirements
Creating the Data Warehouse Logicial Data Model
  • Framework
  • Creating the DW LDM
  • Framework
  • Data Warehouse Content
Data Warehouse Design
  • Framework
  • Physical Data Model
  • Denormalization Methods
  • Denormalized Models
  • Dimensional Data Modelling
  • DW Bus Architecture
Physical Data Modelling Techniques
  • Aggregation
  • Physical Data Modelling Techniques
  • Partitioning
  • Physical Data Modelling Techniques
  • Table Indexes
  • Physical Data Modelling Techniques
  • Surrogate Keys
  • Other Performance Techniques
  • Physical Data Modelling Techniques
  • Additional Design Decisions
Keys to Success
  • Important Decisions
  • Critical Success Factors
  • Supporting the Users