1. The Compelling Need for Data WarehousingLearning ObjectiveCase Study1.1 A Short Historical Note1.2 Need for Data Warehousing1.2.1 Increasing Demand for Strategic Information1.2.2 The Information Crisis1.
2.3 Inability of Past Decision Support System1.2.4 Presence of Better Technology1.2.5 Expectations from the New Kind of Decision Support System1.2.6 Operational Vs Decisional Support System1.
3 Data Warehouse Defined1.3.1 What can a Data Warehouse Do?1.3.2 What Data Warehouse cannot do?1.3.3 What is a Data Warehouse- an Environment or a Product?1.3.
4 A Blend of Many Technologies1.4 Data Warehouse Users1.4.1 Why do they want Information?1.5 Benefits of Data Warehousing1.5.1 Tangible Benefits1.6 Concerns in Data Warehousing1.
6.1 Nothing is for freeSummaryReview Questions2. Data Warehouse: Defining FeaturesLearning ObjectivesCase Study2.1 Introduction2.2 Features of a Data Warehouse2.2.1 Subject Oriented Data2.2.
2 Integrated Data2.2.2.1 Data Cleansing2.2.2.2 Data Transformation2.2.
2.3 Non-Volatile Data2.2.2.4 Time Variant Data2.3 Data Granularity2.3.1 Benefits of Data Granularity2.
3.2 Data granularity - Pros and Cons2.3.3 Dual Levels of Data Granularity2.4 The Information Flow Mechanism2.5 Metadata2.5.1 Role of Metadata2.
5.2 Classification of Metadata2.5.3 Metadata is the Nerve Centre of the Data Warehouse2.5.4 Metadata Management2.6 Two Classes of Data2.7 Life Cycle of Data2.
7.1 What is Data Velocity?2.7.2 Moving Data from One Medium to Another2.7.3 Inverted Data Warehouse2.8 Can Data Move from Data Warehouse to the Operational Systems?2.8.
1 Direct Access Mode2.8.2 Indirect Access ModeSummaryReview Questions3. Physical Architecture of a Data Warehouse and Data Mart IssuesLearning ObjectivesCase Study3.1 Introduction3.2 Distinguishing Characteristics of Data Warehouse Architecture3.3 Data Warehouse Architectural Goals3.4 Data Warehouse Architecture3.
4.1 Pros and Cons of Data Warehouse Architecture3.4.2 The Two Tier Architecture3.4.3 The Three Tier Architecture3.4.4 The Four Tier Architecture3.
4.5 Three Tier Versus Two Tier Architecture3.4.6 Architecture Considerations and Challenges3.4.7 Interfacing3.5 Data Warehouse and Data Marts3.6 Issues in Building Data Marts3.
6.1 A Change of Approaches3.6.2 How Are Data Warehouse Different From Data Marts3.6.3 Reasons for Creating Data Marts3.6.4 Advantages of Building a Data Mart3.
6.5 Limitations of Building a Data Mart3.7 Building Data Marts3.8 Other Data Mart Issues3.8.1 Types of Data Marts Based on Underlying DBMS3.8.2 Loading of Data Marts3.
8.2.1 The Types of Data Marts to Load3.8.2.2 Loading Temporal Data Marts3.8.2.
3 Loading of Non- Temporal Data Marts3.8.3 Metadata for a Data Mart3.8.4 Maintenance of a Data mart3.8.5 Nature of data in a Data Mart3.8.
6 Software Components of a Data Mart3.8.7 Performance Issues3.8.8 Monitoring Requirements for a Data Mart3.8.9 Security In A Data Mart3.8.
10 Structure of a Data Mart3.9 Reasons for Increased Popularity of Data Marts3.10 Can We Have the Data Warehouse and Data Marts on the Same Processor?3.11 Pushing and Pulling DataSummaryReview Questions4. Gathering the Business RequirementsLearning ObjectiveCase Study4.1 Introduction4.2 Determining the End User Requirements4.2.
1 Business Objectives4.2.2 Business Queries4.2.3 Determining the Functional Requirements4.2.4 Information Infrastructure Environment4.2.
5 The Data Quality Levels4.3 Requirements Gathering Methods4.3.1 Interviews4.3.2 JAD Methodology4.3.3 Review of Existing Documentation4.
3.4 Brainstorming4.3.5 Questionnaires4.3.6 Where to Stop?4.4 Requirements Analysis4.4.
1 Requirements Definition Document4.5 Gathering Requirements for a Data Warehouse Project4.6 Dimensional Analysis4.6.1 Business Dimensions4.6.2 Dimension Hierarchies/Categories4.6.
3 Facts or Metrics4.6.4 Example4.7 Information Package Diagram4.7.1 What Information does an IPD contain?4.7.2 Example4.
7.3 Reason for Forming IPDSummaryReview questions5. Planning and Project Management In A Data WarehouseLearning ObjectiveCase Study5.1 The Project Management Principles5.1.1 Key Considerations5.1.2 The Ideal Approach5.
2 Data Warehouse Readiness Assessment5.2.1 Bad Performance Indicators5.2.2 Indications for a Successful Data Warehouse Project5.3 The Data Warehouse Project Team5.3.1 Key Roles5.
3.2 User Involvement5.4 Planning for the Data Warehouse5.4.1 Gathering the Business Requirements5.4.2 Gaining Support for the Project5.5 The Data Warehouse Project Plan5.
6 Economic Feasibility Analysis5.6.1 Costs and Benefits of the System5.6.2 Economic Feasibility Measures5.6.3 Justifying the New System5.7 Planning For a Data Warehouse Server5.
7.1 SMP5.7.2 Clusters5.7.3 MMP5.7.4 ccNUMA5.
8 Capacity Planning5.8.1 Estimating the Load5.8.2 Estimating the CPU Bandwidth5.8.3 Estimating the Memory5.8.
4 Estimating the Disk5.9 Selecting the Operating System for the Data Warehouse5.10 Selecting the Database Software5.10.1 Difference between General DBMS and Data Warehouse DBMS5.10.2 How to Choose?5.11 Selection of Tools5.
11.1 Information Delivery Tools5.11.1.1 The Tool Selection Technique5.11.1.2 Criteria for Selecting the Information Delivery Tool5.
11.2 Query Tools5.11.3 Browser Tools5.11.4 Metadata Tools5.15.5 Data Quality ToolsSummaryReview Questions6.
Data Warehouse Schema6.1 Introduction6.2 Building the Fact Tables and Dimension Tables6.2.1 The Traditional Approach6.3 Dimensional Modeling6.3.1 Data Warehouse Modeling Vs Operational Database Modeling6.
3.2 Dimensional Model Vs ER Model6.3.3 The Need for Dimension Model6.3.4 Features of a Good Dimensional Model6.4 The Star Schema6.4.
1 How Does a Query Execute?6.4.2 Example6.4.3 Pros and Cons of the Star Schema6.5 The Snowflake Schema6.5.1 The Technique6.
5.2 Example6.5.3 Is Snowflaking Really Helpful?6.5.4 Pros and Cons of the Snowflake Schema6.6 Aggregate Tables6.6.
1 Need for Building Aggregate Fact Tables6.6.2 Limitations of Aggregate Tables6.7 Fact Constellation Schema or Families of Star6.7.1 Pre-requisite for a Fact Constellation Schema6.7.2 Pros and Cons of Fact Constellation Schema6.
8 Strengths of Dimensional Modeling6.9 Data Warehouse and the Data ModelSummaryReview Questions7. Fact Tables and Dimension Tables: Miscellaneous IssuesLearning ObjectiveCase Study7.1 Characteristics of a Dimension Table7.2 Characteristics of a Fact Table7.3 The Factless Fact Table7.4 Updates To Dimension Tables7.4.
1 Slowly Changing Dimensions7.4.1.1 Type 1 Changes7.4.1.2 Type 2 Changes7.4.
1.3 Type 3 Changes7.4.1.4 Example7.5 Cyclicity of Data - Wrinkle of Time7.6 Other Types of Dimension Tables7.6.
1 Large Dimension Tables7.6.2 Rapidly Changing or Large Slowly Changing Dimensions7.6.3 Junk Dimensions7.7 Keys in the Data Warehouse Schema7.7.1 Primary Keys7.
7.2 Surrogate Keys7.7.3 Foreign Keys7.8 Enhancing the Data Warehouse Performance7.8.1 Table Compression7.8.
2 Parallel Execution7.8.3 Table Partitioning7.8.3.1 The Partitioning Technique7.8.3.
2 Advantages of Partitioning7.8.4 Data Clustering7.8.5 Data Summarization7.8.6 Bypassing the Referential Integrity Checks7.8.
7 Indexing the Data Warehouse7.9 Data Warehousing and the TechnologySummaryReview Questions8. THE ETL PROCESSLearning ObjectiveCase Study8.1 Introduction8.1.1 Challenges in ETL Functions8.2 Data Extraction8.2.
1 Identification of Data Sources8.2.2 Extracting Data for Data Warehouse Refreshing8.2.2.1 Immediate Data Extraction Technique8.2.2.
2 Deferred Data Extraction Technique8.2.2.3 Evaluation of Extraction Techniques8.2.3 Managing Reference Tables in a Data Warehouse8.3 Data Transformation8.3.
1 Tasks Involved in Data Transformation8.3.2 Role of Data Transformation Process8.4 Data Loading8.4.1 Techniques of Data Loading8.4.2 When should we go for Data Update rather than Data Refresh?8.
4.3 Loading the Fact Tables and Dimension Tables8.5 Data Quality8.5.1 The Need for Data Quality8.5.2 Categories of Errors Which Effect data Quality8.5.
2.1 Incomplete Errors8.5.2.2 Incorrect Errors8.5.2.3 Incomprehensibility Errors8.
5.2.4 Inconsistency Errors8.5.3 Issues in Data Cleansing8.5.4 Conclusion about Data QualitySummaryReview Questions9. Testing, Growth and Maintenance Of Data WarehouseLearning ObjectiveCase Study9.
1 Data Warehouse Design Review9.1.1 Contents of a Typical Design Review9.2 Developing the Data Warehouse Iteratively9.3 Testing9.3.1 Testing the Data Warehouse9.3.
2 Developing the Test Plan9.3.3 Testing the Backup and Recovery Processes9.3.4 Testing the Data Warehouse Environment9.3.5 Testing the Database9.3.
6 Logging of Test Results9.4 Monitoring the Data Warehouse9.4.1 Why Are Statistics Monitored?9.5 Tuning the Data Warehouse9.5.1 Tuning the Data Load9.5.
2 Tuning Queries9.6 The Feedback LoopSummaryReview Questions10. OLAP in the Data WarehouseLearning ObjectiveCase Study10.1 Need for Online Analytical Processing10.1.1 Multi Dimensional Analysis10.1.2 Fast Access and Powerful Calculations10.
2 OLAP10.2.1 OLAP Defined10.2.2 OLAP is a Data Warehouse Tool10.3 OLAP and Multidimensional Analysis10.3.1 The Multi-Dimensional Logical Data Model10.
3.2 Multi Dimensional Model''s Users10.3.3 The Multi Dimensional Structure10.3.4 Multi- Dimensional Operations10.3.5 The Business Need10.
4 OLAP Functions10.4.1 Dimensional Analysis10.4.2 Hypercubes10.4.3 OLAP Operations in Multidimensional Data Model10.5 OLAP Applications10.
5.1 Integrating OLAP with GIS10.6 OLAP Models10.6.1 MOLAP10.6.2 ROLAP10.6.
3 HOLAP10.6.4 DOLAP10.6.5 OLAP Survey10.6.6 OLAP Trends10.7 OLAP Design Considerations10.
8 OLAP Tools and Products10.8.1 Report Scheduling and Sharing10.8.2 Ad hoc Reporting10.8.3 OLAP Customization10.8.
4 The Human Angle10.9 Existing OLAP Tools10.9.1 Spreadsheet OLAP Clients10.9.2 Other OLAP Clients10.9.3 Embedded OLAP10.
10 Data Design10.10 Administration and Performance10.11 OLAP PlatformsSummaryReview Questions11. Overview of Building and Maintaining A Data WarehouseLearning ObjectiveCase Study11.1 Problem Definition11.2 Critical Success Factors11.3 Requirement Analysis11.4 Planning for the Data Warehouse11.
4.1 Project Staff11.4.2 Project Plan11.4.3 Outsourcin.