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Data Warehouse Principles
Building Client/Server OLAP Applications

Data Warehouse Principles presents a thorough one day overview of the Data Warehouse focusing on how the architecture works, how it is different from operational data architectures, and how Data Warehouses, Data Marts and Operational Data Stores support Online Line Analytical Processing applications in a web environment.

Data Warehouse Principles answers essential questions about Data Warehouse theory, design development and implementation by explaining in non-technical language the fundamentals of the various Data Warehouse system architectures and the practical challenges surrounding its use.  The Data Warehouse architecture is explored by analyzing different types of business scenarios and OLAP applications and by examining the technical, political and methodology issues and decisions which impact the design and usage of Data Warehouses, Data Marts and Operational Data Stores.  The design and development concepts and techniques created by Data Warehouse pioneer W.H. Inmon are introduced and explained.

Data Warehouse Principles utilizes participatory exercises, Case Study examples and highly interactive discussion to introduce and describe the potential benefits of the Data Warehouse architecture and to highlight the major technical, management and organizational issues that demand special attention.

Data Warehouse Principles participants receive a special appendix containing valuable information designed to help organizations plan their Data Warehouse projects.  The appendix covers the major strategic alternatives and options decision-makers face as they plan and design their Data Warehouse architecture, including special attention on Data Transformation and Stafffing issues.

What You Will Learn

Architecture Principles: What is a Data Warehouse?

• Definition & Architecture Element Descriptions

Application Options: When and how should the Data Warehouse be used?

• Online Analytical Procession (OLAP)

Architecture Variations: Where should you build the Data Warehouse?

• Host, Central Server, Distributed Servers

Strategies and Issues: What are the design decisions and options?

• Operational vs. Archival Data

• Detail vs. Summarization

• Centralized vs. Decentralized

• Vertical vs. Integrated Data Repositories

• Logical Views vs. Physical Schema

Architecture Development: How do Data Warehouses evolve?

• Single application vertical structures

• Horizontally integrated logical and physical schema

• Data Marts, replication and distribution

• Operational Data Stores

Access and Utilization: How does the use of the Data Warehouse evolve?

• Inquiry, Reporting, Decision Support, Analysis, Modeling, EIS

• Data Harvesting vs. Data Mining

• Tactical vs. Exploration vs. OLAP Warehouses

Who Should Attend

• I/S: Managers, Project Leaders, Analysts, Application Designers and Developers

• Business Areas: Managers and staff needing data access for inquiry, reporting and analysis

• Executives: Prospective users of Decision Support and Executive Information Systems

Seminar Outline

Definitions & Characteristics

• Data Warehouse vs. Data Mart

• Data Currency: Operational Data vs. Archival Data

• Online Analytical Processing (OLAP)

• Client/Server

• Replication

• Data Integrity: The System of Record & the Truth Copy

Data Warehouse OLAP Applications

• Inquiry & Reporting

• Analysis

• Decision Support Systems (DSS)

• Executive Information Systems (EIS)

• Data Harvesting

• Data Mining

Data Organization Strategies

• Relational Structures

• Multi-dimensional (MD) Structures

• Star Schemas

• Bit-Mapped Indexing

• Symmetric Multi-Processing (SMP)

Data Warehouse Evolution

• Single-source Vertical (Stovepipe)

• Multiple-source integration

• Data Marts

Data Warehouse Load & Maintenance Strategies

• Batch Bulk Load

• ODS Replication "Trickle Load"

Special Appendix

Strategies, Issues & Decisions

• Data sources

• Level of summarization

• Refresh cycle & scheduling

• Logical organization vs. Physical organization

• Location: Centralize vs. Replicate & distribute vs. Extract & distribute

• Archive strategy

• Amount of history to maintain online

Data Transformation (Data Cleansing or Data Scrubbing)

• Data Access

• Data Mapping

• Data Characteristic Conversion

• Data Integrity Reconciliation

• When & Where to Transform

• Data Transformation Logistics

Staffing, Roles & Responsibilities

• Business Area Participation

• Data Administration & Database Administration

• Application Design and Development