Data Warehouse Design Workshop - DW10 ( 2 Days )
Price: $1200.00
Course Outline
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Abstract/Overview
This course presents powerful, practical, and repeatable techniques for the analysis and design of real-life Data Warehouse solutions. Numerous exercises and an extensive final case study help students to gain a deeper understanding of how to analyze and model data for building warehouse databases that satisfy practical business needs.
Audience - Who Should Attend?
This course is intended for business and systems analysts who will be involved in the analysis and design of Data Warehouses.
Prerequisite
Students should have a basic understanding of Data Warehouse and Data Modelling concepts. Ideally, participants have completed Information Balance's "Introduction to Data Warehouses" seminar and "Data Modelling Workshop" course or equivalent.
Objective
- Be able to apply a set of techniques for gathering user warehouse requirements.
- Be able to transform an operational data model into a warehouse data model.
- Be able to apply design techniques to optimize the physical warehouse model.
- Understand the star schema technique and its strengths and weaknesses.
- Learn steps to ensure that your first data warehouse is a success.
Content
Introduction
- Review Fundamental Data Warehousing Concepts
- Review Concepts of Conceptual, Logical, and Physical Models
- Review Analysis and Design Framework
Gathering User Requirements
- Objectives and Prerequisites
- How Approach Differs from Operational?
- How Focus Differs from Operational Methods?
- Different Techniques for Different Types of Users
- Farmers
- Tourists
- Explorers
Creating the Data Warehouse Logical Data Model
- Eight Step Data Warehouse Modelling Process
- Determining Data Warehouse Contents
- Extracting
- Conditioning
- Scrubbing
- Merging
- Householding
- Enrichment
- Loading
Designing the Data Warehouse Physical Data Model
- Logical to Physical Transformation
- Techniques for Optimizing Design
- Denormalization
- Aggregation
- Partitioning
- Indexing
- Surrogate Keys
- Clustering
- Compaction
- Other Design Considerations
- Data Integrity Mechanisms
- Dealing with Time
- Dealing with Changing Data Values
- Aging and Archiving Data
- Accommodating Growth
Dimensional Modelling Technique
- When to Use Dimensional Modelling
- Data Mart vs. Data Warehouse
- Star Schema
- Fact Table
- Dimension Table
- Snowflake Schema
- Best Practices
Keys to Success
- Important Scoping Decisions
- Critical Success factors
- Supporting the Users
Course Schedule
| Start Date | Location | Class Code | Duration (days) |
| Thu, Sep 23 2010 | Ottawa | P36173 | 2 |
| | | | |
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