Kimball Dimensional Data Warehouse Course

This free course is an introduction to Kimball Data Warehouse concepts. It consist of a number of Data Warehouse topics, explanation of key concepts, insight into experience based best practice, some real-life examples, and practical hands on exercises where applicable.

To start the course, use the menu on the right side of this page ->. Or scroll to the bottom and use the Course Content menu.

What is a Data Warehouse?

The definition of a Kimball Data Warehouse for the purposes of this course.

Why do you need a data warehouse?

The problems that a data warehouse solves.
- A summary of the various issues a data warehouses solves with examples.
- Simplification through Star schema.

Understanding Star Schemas

At the core of a Data warehouse is a star schema. It’s essential to understand Star schema concepts, including the filter theory. Hands on comparison with querying an operational schema, and what to watch out for.
Fact concepts, including
- Types of facts, how to recognize them and when to use them.
  - Transaction Fact.
  - Accumulating Snapshot.
  - Periodic Snapshot.
- Dimension relationships (Role Plays etc).
- ETL Patterns.
Dimension concepts, including:
- Surrogate Keys.
- Hierarchies, including Parent-Child.
- History in Dimensions – Type 1 and 2 attributes.
- Many to many relationships and how to model them.
- ETL Patterns
- Calendar Dimension
Data Warehouse vs Data Mart vs Conformed Dimensions
The Data Warehouse Matrix

ETL design

An explanation of the Data stores, Data Sets, and Entity types in a Data Warehouse solution, their structure and purpose. Related back to Dimodelo Data Warehouse Studio.
An overview of the ETL patterns utilized to move data from one data set to another. Related back to Dimodelo Data Warehouse Studio.

OLAP

What is an OLAP Cube/Tabular model. Why you need one and the benefits it brings.
Understanding calculated measures, when and how they are applied.
Security and Perspectives in OLAP.