Data Warehouse Intensive Education

One full day.

This pragmatic seminar provides the student with a comprehensive understanding of data warehousing fundamentals and principles. You will learn the best practices and processes necessary to architect, design and build a successful data warehouse – or resurrect one.

Practical field examples of data warehousing implementations permeate the presentation. The class covers justification and requirements activities, project planning, data modeling and data access as well as topics like data quality, maturing warehouses and market landscape issues that are needed for success.

This class is for anyone involved in a data warehouse/business intelligence initiative. The class establishes the terminology foundation and exposes each participant to the larger picture and roles of the other team members, thereby creating a more efficient and successful team.

Intended Audience:

Business Analysts
Chief Information Officers
Data Administrators
Data Architects
Data Modelers
Data Stewards
Database Administrators
Database Designers
Enterprise Architects
Information Technology Leadership
Project and Program Managers
Systems Analysts

Course Outline

Definitions and Justification

Data Warehouse
Data Mart
Data Warehouse Program
Data Warehouse Justification

Data Warehouse Architecture

Inmon-oriented Enterprise Data Warehouse Approaches
Kimball-oriented Data Mart Oriented Approaches
Federating the Data Warehouse across the Enterprise
Data Access Tool Categories

Project Planning

The Agile Approach
Requirements Gathering
Roles and Responsibilities – Technical
Roles and Responsibilities – Business (including data stewards)
Design and Implementation Deliverables
Aspects of Architecture and Methodology that each organization needs a decisiveness direction on

Data Warehouse Modeling

Conceptual and Logical
Steps to create a dimensional data model
Dimensional – Star
Dimensional – Snowflake

The Data Warehouse Landscape – What’s Going on?

Market Landscape
Architecture Landscape
Application Landscape
Data Landscape
BI Program Environment Landscape
Technology Landscape

Case Studies

Contact us for availability.


  • Actian Avalanche
  • AWS Redshift
  • Azure Synapse
  • DB2 Warehouse on Cloud/DB2
  • Cloudera
  • Google BigQuery
  • Microfocus Vertica
  • Oracle Autonomous Database/Oracle
  • SAP HANA Cloud Platform
  • Snowflake
  • Teradata
  • Yellowbrick

We have a methodological approach to build the POC which tremendously speeds up the value provided.

In our POC, we will work with you to define the MVP, build the MVP and empower you with next steps – technical and organizational – for continued quick and frequent iteration to increasing cloud analytic database, and business, success.