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
Staging
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.
DATABASES WE HAVE WORKED ON INCLUDE:
- 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.