Escalating Need for Strategic Information, Failures Of Past Decision-Support Systems, Operational Versus Decision-Support Systems, Data warehousing—The Only Viable Solution, Data Warehouse Defined. The Data warehousing Movement, Evolution of Business Intelligence.

Data Warehouse-The Building Blocks: Defining Features, Data Warehouses and Data Marts, Architectural Types, Components: Source Data Component, Data Staging Component, Data Storage Component, Information Delivery Component, Metadata Component, Management and Control Component, Metadata In The Data Warehouse.

Planning And Project Management: Planning Your Data Warehouse, The Data Warehouse Project, The Development Phases, The Project Team, Project Management Considerations.

Defining The Business Requirements: Dimensional Analysis, Information Packages: Requirements Not Fully Determinate, Business Dimensions, Dimension Hierarchies and Categories, Key Business Metrics Or Facts, Requirements Gathering Methods, Data Sources, Data Transformation, Data Storage, Information Delivery, Information Package Diagrams.

Requirements As The Driving Force For Data warehousing: Data Design , The Architectural Plan , Data Storage Specifications , Information Delivery Strategy.

Architectural Components: Understanding Data Warehouse Architecture , Distinguishing Characteristics , Architectural Framework , Technical Architecture , Architectural Types.

Infrastructure As The Foundation For Data warehousing: Infrastructure Supporting Architecture , Hardware And Operating Systems , Database Software , Collection Of Tools, Data Warehouse Appliances.

The Significant Role Of Metadata: Why Metadata Is Important , Metadata Types By Functional Areas , Business Metadata , Technical Metadata , How To Provide Metadata.

Principles Of Dimensional Modelling: From Requirements To Data Design , The Star Schema , Star Schema Keys , Advantages Of The Star Schema , Star Schema: Examples , Dimensional Modelling: Advanced Topics : Updates To The Dimension Tables , Miscellaneous Dimensions ,The Snowflake Schema , Aggregate Fact Tables ,Families Of Stars.

Data Extraction, Transformation, And Loading: ETL Overview, ETL Requirements And Steps, Data Extraction, Data Transformation, Data Loading, ETL Tool Options Reemphasizing ETL Metadata, ETL Summary And Approach.

Data Quality-A Key To Success: Why Is Data Quality Critical? Data Quality Challenges, Data Quality Tools, Data Quality Initiative, Master Data Management (Mdm) . Matching Information To The Classes Of Users: Information From The Data Warehouse, Who Will Use The Information? Information Delivery.

Information Delivery: Business Activity Monitoring (Bam) , Dashboards And Scorecards OLAP In the Data Warehouse: Demand for Online Analytical Processing, Major Features And Functions, OLAP Models, OLAP Implementation Considerations.

Data Warehousing And the Web: Web-Enabled Data Warehouse, Web-Based Information Delivery, OLAP And The Web, Building A Web-Enabled Data Warehouse.