Fundamentals of Data Warehousing
Course Specifications
Course number: 085715
Course length: 0.5 day(s)
Course Description
Course Objective: You will describe the fundamentals of data warehousing.

Target Student: This course is intended for people involved in the data quality or data governance process, application development, project teams, IT managers, and project managers needing knowledge of various aspects or the basic fundamentals of data warehousing.

Prerequisites: There are no specific prerequisites for this course other than basic familiarity with databases. 
Hardware Requirements
The instructor’s computer should have the following hardware configured: 
•	Intel® Pentium® IV 300 MHz processor 
•	64 MB of RAM with 1.0 GB of free hard disk space 
•	CD-ROM drive 
•	Super VGA recommended (set at least to 800 x 600 screen resolution) with 256 colors 
•	Internet connection 
Software Requirements
Each computer requires the following software:
•	Microsoft® PowerPoint® 2000 or later. 
Course Objectives
Upon successful completion of this course, students will be able to: 
•	identify the need for data warehouses as well as their general components, structure, and functions. 
•	describe enterprise data. 
•	describe data warehouse design and modeling. 
•	describe the data warehousing architecture. 
•	describe the business value of data warehousing. 
Course Content
Lesson 1: Understanding a Data Warehouse
Topic 1A: Introduction to Data Warehousing 
Topic 1B: Types of Databases 
Topic 1C: OLTP and OLAP 
Lesson 2: Understanding Enterprise Data
Topic 2A: Data Sources of a Data Warehouse 
Topic 2B: Characteristics of Data in a Data Warehouse 
Lesson 3: Data Warehouse Design and Modeling
Topic 3A: Data Warehouse Design 
Topic 3B: Dimensional Model Schemas 
Lesson 4: Understanding the Data Warehousing Architecture
Topic 4A: Extract, Transform and Load Data 
Topic 4B: Data Storage and Presentation 
Lesson 5: Identifying the Business Value of Data Warehousing
Topic 5A: Understand Applications of Data Warehousing 
Topic 5B: Understand Data Mining