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.

Requirements and Design

  • Bottom Up vs Top Down approach.
  • Data Driven Design.
  • Data Warehouse Matrix.
  • Agile Data Warehouse Design – Laurence Corr.
  • Writing an Ontology.
  • Agile Project Management. Strategic and Tactical approaches.