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The Kimball Group Reader: Relentlessly Practical Tools for Data Warehousing and Business Intelligence

Book Description

An unparalleled collection of recommended guidelines for data warehousing and business intelligence pioneered by Ralph Kimball and his team of colleagues from the Kimball Group.

Recognized and respected throughout the world as the most influential leaders in the data warehousing industry, Ralph Kimball and the Kimball Group have written articles covering more than 250 topics that define the field of data warehousing. For the first time, the Kimball Group's incomparable advice, design tips, and best practices have been gathered in this remarkable collection of articles, which spans a decade of data warehousing innovation.

Each group of articles is introduced with original commentaries that explain their role in the overall lifecycle methodology developed by the Kimball Group. These practical, hands-on articles are fully updated to reflect current practices and terminology and cover the complete lifecycle—including project planning, requirements gathering, dimensional modeling, ETL, and business intelligence and analytics.

This easily referenced collection is nothing less than vital if you are involved with data warehousing or business intelligence in any capacity.

Table of Contents

  1. Copyright
  2. About the Authors
  3. Credits
  4. Acknowledgments
  5. Introduction
    1. Intended Audience and Goals
    2. Preview of Contents
    3. Navigation Aids
    4. Terminology Notes
  6. 1. The Reader at a Glance
    1. 1.1. Setting Up for Success
      1. 1.1.1. 1.1 Resist the Urge to Start Coding
      2. 1.1.2. 1.2 Set Your Boundaries
    2. 1.2. Tackling DW/BI Design and Development
      1. 1.2.1. 1.3 Data Wrangling
      2. 1.2.2. 1.4 Myth Busters
      3. 1.2.3. 1.5 Dividing the World
      4. 1.2.4. 1.6 Essential Steps for the Integrated Enterprise Data Warehouse
        1. 1.2.4.1. What Does an Integrated EDW Deliver?
        2. 1.2.4.2. Ultimate Litmus Test for Integration
        3. 1.2.4.3. Organizational Challenges
        4. 1.2.4.4. Conformed Dimensions and Facts
        5. 1.2.4.5. Using the Bus Matrix to Communicate with Executives
        6. 1.2.4.6. Managing the Integrated EDW Backbone
        7. 1.2.4.7. The Dimension Manager
        8. 1.2.4.8. The Fact Provider
        9. 1.2.4.9. Configuring Business Intelligence (BI) Tools
        10. 1.2.4.10. Joint Responsibilities
      5. 1.2.5. 1.7 Drill Down to Ask Why
      6. 1.2.6. 1.8 Slowly Changing Dimensions
        1. 1.2.6.1. Three Types of Slowly Changing Dimensions
          1. 1.2.6.1.1. Type 1: Overwrite
          2. 1.2.6.1.2. Type 2: Add a New Dimension Record
          3. 1.2.6.1.3. Type 3: Add a New Field
      7. 1.2.7. 1.9 Judge Your BI Tool through Your Dimensions
      8. 1.2.8. 1.10 Fact Tables
        1. 1.2.8.1. Stay True to the Grain
        2. 1.2.8.2. Build Up from the Lowest Possible Grain
        3. 1.2.8.3. Three Kinds of Fact Tables
      9. 1.2.9. 1.11 Exploit Your Fact Tables
        1. 1.2.9.1. Front Room: Aggregate Navigation
        2. 1.2.9.2. Front Room: Drilling Across Different Grains
        3. 1.2.9.3. Front Room: Exporting Constraints to Different Business Processes
        4. 1.2.9.4. Back Room: Fact Table Surrogate Keys
  7. 2. Before You Dive In
    1. 2.1. Historical Perspective
      1. 2.1.1. 2.1 The Database Market Splits
      2. 2.1.2. 2.2 Bringing Up Supermarts
        1. 2.1.2.1. The Planning Crisis
        2. 2.1.2.2. Data Marts with an Architecture
        3. 2.1.2.3. Importance of Conformed Dimensions
        4. 2.1.2.4. Designing Conformed Dimensions
        5. 2.1.2.5. Taking the Pledge
        6. 2.1.2.6. Permissible Variations of Conformed Dimensions
        7. 2.1.2.7. Establishing Standard Fact Definitions
        8. 2.1.2.8. Importance of Granularity
        9. 2.1.2.9. Higher Level Data Marts
        10. 2.1.2.10. Rescuing Stovepipes
        11. 2.1.2.11. When You Don't Need Conformed Dimensions
        12. 2.1.2.12. Clear Vision
    2. 2.2. Dealing with Demanding Realities
      1. 2.2.1. 2.3 Brave New Requirements for Data Warehousing
      2. 2.2.2. 2.4 Coping with the Brave New Requirements
        1. 2.2.2.1. Data Marts and Dimensional Modeling
        2. 2.2.2.2. Plugging Data Marts into the Data Warehouse Bus Architecture
      3. 2.2.3. 2.5 Stirring Things Up
      4. 2.2.4. 2.6 Design Constraints and Unavoidable Realities
        1. 2.2.4.1. Design Constraints
        2. 2.2.4.2. Unavoidable Realities
        3. 2.2.4.3. Picking Ourselves Up Off the Floor
      5. 2.2.5. 2.7 Two Powerful Ideas
        1. 2.2.5.1. Separate Your Systems
        2. 2.2.5.2. Symmetrical Stars and Cubes
        3. 2.2.5.3. The Big Payoff
        4. 2.2.5.4. What Have We Accomplished?
      6. 2.2.6. 2.8 Data Warehouse Dining Experience
        1. 2.2.6.1. The Kitchen
        2. 2.2.6.2. The Dining Room
  8. 3. Project/Program Planning
    1. 3.1. Professional Responsibilities
      1. 3.1.1. 3.1 Professional Boundaries
      2. 3.1.2. 3.2 An Engineer's View
        1. 3.1.2.1. The Data Warehouse Mission
        2. 3.1.2.2. Design Drivers
        3. 3.1.2.3. Design Constraints
        4. 3.1.2.4. The Engineer's Response
      3. 3.1.3. 3.3 Beware the Objection Removers
      4. 3.1.4. 3.4 What Does the Central Team Do?
        1. 3.1.4.1. Defining and Publishing Corporate Dimensions
        2. 3.1.4.2. Providing Cross-Divisional Applications
        3. 3.1.4.3. Defining a Consistent Data Warehouse Security Architecture
      5. 3.1.5. 3.5 Avoid DW/BI Isolation
      6. 3.1.6. 3.6 Implementation Analysis Paralysis
    2. 3.2. Justification and Sponsorship
      1. 3.2.1. 3.7 Habits of Effective Sponsors
        1. 3.2.1.1. Setting Up for Success
        2. 3.2.1.2. Resist the Path of Least Resistance
        3. 3.2.1.3. Rally Those Around You
        4. 3.2.1.4. Patience Is a Virtue
        5. 3.2.1.5. Remain Focused on the Goal
      2. 3.2.2. 3.8 TCO Starts with the End User
        1. 3.2.2.1. Bad Decisions Are Costs
        2. 3.2.2.2. A Closer Look at the Costs
      3. 3.2.3. 3.9 Better Business Skills for BI and Data Warehouse Professionals
        1. 3.2.3.1. Building Business Understanding
        2. 3.2.3.2. Building Interpersonal Skills
        3. 3.2.3.3. Building Public Speaking Skills
        4. 3.2.3.4. Building Written Communication Skills
        5. 3.2.3.5. Practice, Practice, Practice
    3. 3.3. Kimball Methodology
      1. 3.3.1. 3.10 Kimball Lifecycle in a Nutshell
        1. 3.3.1.1. Program/Project Planning and Management
        2. 3.3.1.2. Business Requirements
        3. 3.3.1.3. Technology Track
        4. 3.3.1.4. Data Track
        5. 3.3.1.5. Business Intelligence Track
        6. 3.3.1.6. Deployment, Maintenance, and Growth
      2. 3.3.2. 3.11 Off the Bench
      3. 3.3.3. 3.12 The Anti-Architect
      4. 3.3.4. 3.13 Think Critically When Applying Best Practices
        1. 3.3.4.1. Take an Enterprise Approach
        2. 3.3.4.2. Embrace Business Intelligence
        3. 3.3.4.3. Design Dimensional Schemas
        4. 3.3.4.4. Use Conformed Dimensions for Integration
        5. 3.3.4.5. Carefully Plan the ETL Architecture
      5. 3.3.5. 3.14 Eight Guidelines for Low Risk Enterprise Data Warehousing
        1. 3.3.5.1. Work on the Right Thing
        2. 3.3.5.2. Give Business Users Control
        3. 3.3.5.3. Proceed Incrementally
        4. 3.3.5.4. Start with Lightweight, Focused Governance
        5. 3.3.5.5. Build a Simple, Universal Platform
        6. 3.3.5.6. Integrate Using Conformed Dimensions
        7. 3.3.5.7. Manage Quality a Few Screens at a Time
        8. 3.3.5.8. Use Surrogate Keys Throughout
      6. 3.3.6. 3.15 Relating to Agile Methodologies
      7. 3.3.7. 3.16 Is Agile Enterprise Data Warehousing an Oxymoron?
  9. 4. Requirements Definition
    1. 4.1. Gathering Requirements
      1. 4.1.1. 4.1 Alan Alda's Interviewing Tips for Uncovering Business Requirements
        1. 4.1.1.1. Be Curious, but Not Too Smart
        2. 4.1.1.2. Be Conversational
        3. 4.1.1.3. Listen and Expect to Be Changed
      2. 4.1.2. 4.2 More Business Requirements Gathering Dos and Don'ts
      3. 4.1.3. 4.3 Overcoming Obstacles When Gathering Business Requirements
      4. 4.1.4. 4.4 Surprising Value of Data Profiling
    2. 4.2. Organizing around Business Processes
      1. 4.2.1. 4.5 Focus on Business Processes, Not Business Departments!
      2. 4.2.2. 4.6 Identifying Business Processes
      3. 4.2.3. 4.7 Business Process Decoder Ring
      4. 4.2.4. 4.8 Relationship between Strategic Business Initiatives and Business Processes
    3. 4.3. Wrapping Up the Requirements
      1. 4.3.1. 4.9 The Bottom-Up Misnomer
        1. 4.3.1.1. Focus on the Enterprise, Not Departments
        2. 4.3.1.2. Draft the Enterprise Data Warehouse Bus Matrix
        3. 4.3.1.3. Prioritize for an Orderly Conclusion
        4. 4.3.1.4. Achieve an Enterprise Roadmap
  10. 5. Data Architecture
    1. 5.1. Making the Case for Dimensional Modeling
      1. 5.1.1. 5.1 Is ER Modeling Hazardous to DSS?
        1. 5.1.1.1. Dimensional Model versus Normalized Model
      2. 5.1.2. 5.2 A Dimensional Modeling Manifesto
        1. 5.1.2.1. What Is 3NF Normalized Modeling?
        2. 5.1.2.2. What Is DM?
        3. 5.1.2.3. DM versus 3NF
        4. 5.1.2.4. The Strengths of DM
        5. 5.1.2.5. Myths About DM
        6. 5.1.2.6. In Defense of DM
      3. 5.1.3. 5.3 There Are No Guarantees
        1. 5.1.3.1. Does 3NF Modeling Handle Business Rules?
        2. 5.1.3.2. Early Dimensional Modeling
    2. 5.2. Enterprise Data Warehouse Bus Architecture
      1. 5.2.1. 5.4 Divide and Conquer
        1. 5.2.1.1. Common Labels Wanted?
        2. 5.2.1.2. Business Process Subject Areas Are Not Departmental
        3. 5.2.1.3. Conformed Dimensions and Facts
        4. 5.2.1.4. The Data Warehouse Bus Architecture
        5. 5.2.1.5. Only for Highly Distributed Systems?
        6. 5.2.1.6. Netting Out the Benefits
      2. 5.2.2. 5.5 The Matrix
        1. 5.2.2.1. Inviting Subject Area Groups to the Conforming Meeting
        2. 5.2.2.2. Communicating with the Boss
        3. 5.2.2.3. Second-Level Subject Areas
      3. 5.2.3. 5.6 The Matrix: Revisited
        1. 5.2.3.1. Matrix Columns for Reference Data
        2. 5.2.3.2. Data Stewardship
        3. 5.2.3.3. Process-Centric Rows
        4. 5.2.3.4. Associate Columns and Rows
        5. 5.2.3.5. Common Matrix Mishaps
        6. 5.2.3.6. Matrix Extensions
      4. 5.2.4. 5.7 Drill Down into a Detailed Bus Matrix
    3. 5.3. Integration Instead of Centralization
      1. 5.3.1. 5.8 Integration for Real People
        1. 5.3.1.1. Defining Integration
        2. 5.3.1.2. Integrating Labels
        3. 5.3.1.3. Integrating Measures
        4. 5.3.1.4. Responsibilities of the Dimension Manager
        5. 5.3.1.5. Responsibilities of the Fact Provider
      2. 5.3.2. 5.9 Data Stewardship 101: The First Step to Quality and Consistency
        1. 5.3.2.1. Why Stewardship Is Essential
        2. 5.3.2.2. Stewardship Responsibilities
        3. 5.3.2.3. Right Stuff for Stewardship
        4. 5.3.2.4. Communication Tools and Techniques
        5. 5.3.2.5. How to Get Started
      3. 5.3.3. 5.10 To Be or Not To Be Centralized
        1. 5.3.3.1. All That Glitters Is Not Gold
        2. 5.3.3.2. Be Not Afraid of Greatness
        3. 5.3.3.3. All's Well That Ends Well
    4. 5.4. Contrast with the Corporate Information Factory
      1. 5.4.1. 5.11 Differences of Opinion
        1. 5.4.1.1. Common Ground
        2. 5.4.1.2. Kimball Bus Architecture
        3. 5.4.1.3. Corporate Information Factory
        4. 5.4.1.4. Fundamental Differences
        5. 5.4.1.5. Hybrid Approach?
        6. 5.4.1.6. Success Criteria
      2. 5.4.2. 5.12 Don't Support Business Intelligence with a Normalized EDW
  11. 6. Dimensional Modeling Fundamentals
    1. 6.1. Basics of Dimensional Modeling
      1. 6.1.1. 6.1 Fact Tables and Dimension Tables
        1. 6.1.1.1. Measurements and Context
        2. 6.1.1.2. Dimensional Keys
        3. 6.1.1.3. Relating the Two Modeling Worlds
        4. 6.1.1.4. Declaring the Grain
        5. 6.1.1.5. Additive Facts
        6. 6.1.1.6. Degenerate Dimensions
      2. 6.1.2. 6.2 Drilling Down, Up, and Across
        1. 6.1.2.1. Drilling Down
        2. 6.1.2.2. Drilling Up
        3. 6.1.2.3. Drilling Across
      3. 6.1.3. 6.3 The Soul of the Data Warehouse, Part One: Drilling Down
      4. 6.1.4. 6.4 The Soul of the Data Warehouse, Part Two: Drilling Across
        1. 6.1.4.1. Implementing Drill Across
        2. 6.1.4.2. Amazing Magic
      5. 6.1.5. 6.5 The Soul of the Data Warehouse, Part Three: Handling Time
        1. 6.1.5.1. Time Validity
        2. 6.1.5.2. Correct Association
        3. 6.1.5.3. Natural Grains
        4. 6.1.5.4. Have You Lived Up to Your Pledges?
      6. 6.1.6. 6.6 Graceful Modifications to Existing Fact and Dimension Tables
    2. 6.2. Dos and Don'ts
      1. 6.2.1. 6.7 Kimball's Ten Essential Rules of Dimensional Modeling
      2. 6.2.2. 6.8 What Not to Do
    3. 6.3. Myths about Dimensional Modeling
      1. 6.3.1. 6.9 Dangerous Preconceptions
      2. 6.3.2. 6.10 Fables and Facts
        1. 6.3.2.1. Not All Dimensional Models Are Created Equal
        2. 6.3.2.2. Focus on Measurement Processes, Not Departmental Reports
        3. 6.3.2.3. Begin with Atomic Details, Not Summarized Data
        4. 6.3.2.4. Integration Is the Goal, Not Normalization
  12. 7. Dimensional Modeling Tasks and Responsibilities
    1. 7.1. Design Activities
      1. 7.1.1. 7.1 Letting the Users Sleep
      2. 7.1.2. 7.2 Staffing the Dimensional Modeling Team
      3. 7.1.3. 7.3 Practical Steps for Designing a Dimensional Model
        1. 7.1.3.1. Join the Party
        2. 7.1.3.2. Dive into the Details
        3. 7.1.3.3. Review the Results
      4. 7.1.4. 7.4 The Naming Game
        1. 7.1.4.1. Step 1: Preparation
        2. 7.1.4.2. Step 2: Creating an Initial Name Set
        3. 7.1.4.3. Step 3: Building Consensus
      5. 7.1.5. 7.5 When Is the Dimensional Design Done?
    2. 7.2. Design Review Activities
      1. 7.2.1. 7.6 Fistful of Flaws
        1. 7.2.1.1. What's the Grain?
        2. 7.2.1.2. Mixed Grain or Textual Facts?
        3. 7.2.1.3. Dimension Descriptors and Decodes?
        4. 7.2.1.4. Handling of Hierarchies?
        5. 7.2.1.5. Explicit Date Dimension?
        6. 7.2.1.6. Control Numbers as Degenerate Dimensions?
        7. 7.2.1.7. Surrogate Keys?
        8. 7.2.1.8. Slowly Changing Dimension Strategies?
        9. 7.2.1.9. Well Understood Business Requirements?
      2. 7.2.2. 7.7 Rating Your Dimensional Data Warehouse
        1. 7.2.2.1. Architecture Criteria
        2. 7.2.2.2. Administration Criteria
        3. 7.2.2.3. Expression Criteria
        4. 7.2.2.4. Are You Dimensional?
  13. 8. Fact Table Core Concepts
    1. 8.1. Granularity
      1. 8.1.1. 8.1 Declaring the Grain
        1. 8.1.1.1. In Business Terms
        2. 8.1.1.2. Powerful Effects
        3. 8.1.1.3. Keep Facts True to the Grain
      2. 8.1.2. 8.2 Keep to the Grain in Dimensional Modeling
      3. 8.1.3. 8.3 Warning: Summary Data May Be Hazardous to Your Health
      4. 8.1.4. 8.4 No Detail Too Small
        1. 8.1.4.1. Accumulating the Atoms
        2. 8.1.4.2. Consolidating across Processes
        3. 8.1.4.3. More Performance, Less Dimensionality
        4. 8.1.4.4. Consolidated Fact Table Example
        5. 8.1.4.5. Accumulating Snapshot Example
        6. 8.1.4.6. Details Always Come First
    2. 8.2. Types of Fact Tables
      1. 8.2.1. 8.5 Fundamental Grains
        1. 8.2.1.1. Fundamental Grains
        2. 8.2.1.2. How Do We Use Each Fact Table Type?
      2. 8.2.2. 8.6 Modeling a Pipeline with an Accumulating Snapshot
      3. 8.2.3. 8.7 Combining Periodic and Accumulating Snapshots
      4. 8.2.4. 8.8 Modeling Time Spans
      5. 8.2.5. 8.9 A Rolling Prediction of the Future, Now and in the Past
      6. 8.2.6. 8.10 Factless Fact Tables
      7. 8.2.7. 8.11 Factless Fact Tables? Sound Like Jumbo Shrimp?
      8. 8.2.8. 8.12 What Didn't Happen
        1. 8.2.8.1. Coverage Tables
        2. 8.2.8.2. Explicit Records for Non-Behavior
        3. 8.2.8.3. Searching for Nonexistent Facts with NOT EXISTS
        4. 8.2.8.4. Using NOT EXISTS to Find Non Attributes
    3. 8.3. Parent-Child Fact Tables
      1. 8.3.1. 8.13 Managing Your Parents
        1. 8.3.1.1. Conflicting Allocation Theories
        2. 8.3.1.2. Tough Allocation Environments
      2. 8.3.2. 8.14 Patterns to Avoid When Modeling Header/Line Item Transactions
        1. 8.3.2.1. Bad Idea #1: Retain the Header as a Dimension
        2. 8.3.2.2. Bad Idea #2: Line Items Don't Inherit Header Dimensionality
        3. 8.3.2.3. Recommended Structure for Header/Line Item Transactions
    4. 8.4. Fact Table Keys and Degenerates
      1. 8.4.1. 8.15 Fact Table Surrogate Keys
      2. 8.4.2. 8.16 Reader Suggestions on Fact Table Surrogate Keys
      3. 8.4.3. 8.17 Another Look at Degenerate Dimensions
      4. 8.4.4. 8.18 Creating a Reference Dimension for Infrequently Accessed Degenerates
    5. 8.5. Miscellaneous Fact Table Design Patterns
      1. 8.5.1. 8.19 Put Your Fact Tables on a Diet
      2. 8.5.2. 8.20 Keeping Text Out of the Fact Table
      3. 8.5.3. 8.21 Dealing with Nulls in a Dimensional Model
        1. 8.5.3.1. Nulls as Fact Table Foreign Keys
        2. 8.5.3.2. Nulls as Facts
        3. 8.5.3.3. Nulls as Dimension Attributes
      4. 8.5.4. 8.22 Modeling Data as Both a Fact and Dimension Attribute
      5. 8.5.5. 8.23 When a Fact Table Can Be Used as a Dimension Table
      6. 8.5.6. 8.24 Sparse Facts and Facts with Short Lifetimes
      7. 8.5.7. 8.25 Pivoting the Fact Table with a Fact Dimension
  14. 9. Dimension Table Core Concepts
    1. 9.1. Dimension Table Keys
      1. 9.1.1. 9.1 Surrogate Keys
      2. 9.1.2. 9.2 Keep Your Keys Simple
    2. 9.2. Date and Time Dimension Considerations
      1. 9.2.1. 9.3 It's Time for Time
        1. 9.2.1.1. Basic Time Issues
        2. 9.2.1.2. Intermediate Time Issues
      2. 9.2.2. 9.4 Surrogate Keys for the Time Dimension
      3. 9.2.3. 9.5 Latest Thinking on Time Dimension Tables
      4. 9.2.4. 9.6 Smart Date Keys to Partition Fact Tables
      5. 9.2.5. 9.7 Handling All the Dates
    3. 9.3. Miscellaneous Dimension Patterns
      1. 9.3.1. 9.8 Data Warehouse Role Models
      2. 9.3.2. 9.9 Mystery Dimensions
        1. 9.3.2.1. Find the Obvious Dimension-Related Fields
        2. 9.3.2.2. Find the Fact Related Fields
        3. 9.3.2.3. Decide What to Do with the Rest
        4. 9.3.2.4. Transform Mystery Fields Into Mystery Dimensions
      3. 9.3.3. 9.10 De-Clutter with Junk Dimensions
      4. 9.3.4. 9.11 Showing the Correlation Between Dimensions
      5. 9.3.5. 9.12 Causal (Not Casual) Dimensions
      6. 9.3.6. 9.13 Resist Abstract Generic Dimensions
      7. 9.3.7. 9.14 Hot-Swappable Dimensions
      8. 9.3.8. 9.15 Accurate Counting with a Dimensional Supplement
    4. 9.4. Slowly Changing Dimensions
      1. 9.4.1. 9.16 Perfectly Partitioning History with Type 2 SCD
      2. 9.4.2. 9.17 Many Alternate Realities
        1. 9.4.2.1. Predictable Multiple Realities
        2. 9.4.2.2. Unpredictable Multiple Realities
      3. 9.4.3. 9.18 Monster Dimensions
      4. 9.4.4. 9.19 When a Slowly Changing Dimension Speeds Up
        1. 9.4.4.1. Date Stamps in Slowly Changing Dimensions
        2. 9.4.4.2. SCDs That Aren't Slowly Changing
      5. 9.4.5. 9.20 When Do Dimensions Become Dangerous?
      6. 9.4.6. 9.21 Slowly Changing Dimensions Are Not Always as Easy as 1, 2, and 3
        1. 9.4.6.1. Mini Dimension with Current Overwrite
        2. 9.4.6.2. Type 2 with Current Overwrite
        3. 9.4.6.3. Type 2 with Durable Keys in the Fact Table
        4. 9.4.6.4. Series of Type 3 Attributes
        5. 9.4.6.5. Balance Power against Ease of Use
      7. 9.4.7. 9.22 Dimension Row Change Reason Attributes
  15. 10. More Dimension Patterns and Case Studies
    1. 10.1. Snowflakes, Outriggers, and Bridges
      1. 10.1.1. 10.1 Snowflakes, Outriggers, and Bridges
      2. 10.1.2. 10.2 A Trio of Interesting Snowflakes
        1. 10.1.2.1. Classic Snowflake
        2. 10.1.2.2. Large Customer Dimensions
        3. 10.1.2.3. Financial Product Dimensions
        4. 10.1.2.4. Multi-Enterprise Calendar Dimensions
        5. 10.1.2.5. Permissible Snowflakes
      3. 10.1.3. 10.3 Help for Dimensional Modeling
      4. 10.1.4. 10.4 Managing Bridge Tables
        1. 10.1.4.1. Using Surrogate Keys
        2. 10.1.4.2. Using Twin Time Stamps
        3. 10.1.4.3. Updating the Bridge Table
      5. 10.1.5. 10.5 The Keyword Dimension
        1. 10.1.5.1. Designing the Keyword Dimension
        2. 10.1.5.2. The AND/OR Dilemma
        3. 10.1.5.3. Searching for Substrings
        4. 10.1.5.4. High Performance Substring Indexes
    2. 10.2. Dealing with Hierarchies
      1. 10.2.1. 10.6 Maintaining Dimension Hierarchies
        1. 10.2.1.1. Start with the Design
        2. 10.2.1.2. Load Normalized Data
        3. 10.2.1.3. Maintain True Hierarchies
        4. 10.2.1.4. Address Dirty Sources
        5. 10.2.1.5. Make It Perform
      2. 10.2.2. 10.7 Help for Hierarchies
      3. 10.2.3. 10.8 Five Alternatives for Better Employee Dimensional Modeling
        1. 10.2.3.1. Alternative 1: Bridge Table Using Surrogate Keys
        2. 10.2.3.2. Alternative 2: Bridge Table with Separate Reports-To Dimension
        3. 10.2.3.3. Alternative 3: Bridge Table with Natural Keys
        4. 10.2.3.4. Alternative 4: Forced Fixed-Depth Hierarchy Technique
        5. 10.2.3.5. Alternative 5: The PathString Attribute
        6. 10.2.3.6. Recommendation
      4. 10.2.4. 10.9 Alternate Hierarchies
    3. 10.3. Customer Issues
      1. 10.3.1. 10.10 Dimension Embellishments
      2. 10.3.2. 10.11 Wrangling Behavior Tags
      3. 10.3.3. 10.12 Three Ways to Capture Customer Satisfaction
        1. 10.3.3.1. Standard Fixed List
        2. 10.3.3.2. Simultaneous Dimension Attributes and Facts
        3. 10.3.3.3. The Unpredictable, Chaotic List
    4. 10.4. Addresses and International Issues
      1. 10.4.1. 10.13 Think Globally, Act Locally
        1. 10.4.1.1. Synchronizing Multiple Time Zones
        2. 10.4.1.2. Supporting Multiple National Calendars
        3. 10.4.1.3. Collecting Revenue in Multiple Currencies
        4. 10.4.1.4. Dealing with the Euro
      2. 10.4.2. 10.14 Warehousing without Borders
        1. 10.4.2.1. Designing for an International Name and Address Environment
      3. 10.4.3. 10.15 Spatially Enabling Your Data Warehouse
        1. 10.4.3.1. Investigating a GIS Vendor
        2. 10.4.3.2. Going to Boot Camp
        3. 10.4.3.3. Automatic Address Standardizing
        4. 10.4.3.4. Geographic Query on Standard Databases
        5. 10.4.3.5. The Right Fit?
      4. 10.4.4. 10.16 Multinational Dimensional Data Warehouse Considerations
    5. 10.5. Industry Scenarios and Idiosyncrasies
      1. 10.5.1. 10.17 An Insurance Data Warehouse Case Study
      2. 10.5.2. 10.18 Traveling through Databases
        1. 10.5.2.1. Walking through the Design
        2. 10.5.2.2. Adding Dimensions
        3. 10.5.2.3. Images and Maps
      3. 10.5.3. 10.19 Human Resources Dimensional Models
      4. 10.5.4. 10.20 Not So Fast
        1. 10.5.4.1. Find the Components of Profitability
        2. 10.5.4.2. Marketing and Finance Need to Help
        3. 10.5.4.3. Allocations: The Heart of the Profitability Challenge
        4. 10.5.4.4. If You're in a Hurry
      5. 10.5.5. 10.21 The Budgeting Chain
        1. 10.5.5.1. The Grain of the Budgeting Chain Fact Tables
        2. 10.5.5.2. The Budgeting Chain Dimensions and Facts
        3. 10.5.5.3. Applications across the Budget Chain
      6. 10.5.6. 10.22 Compliance-Enabled Data Warehouses
      7. 10.5.7. 10.23 Clicking with Your Customer
        1. 10.5.7.1. The Goals of the Clickstream Dimensional Model
        2. 10.5.7.2. The Clickstream Data Source
        3. 10.5.7.3. The Fundamental Grain of Clickstream Data
        4. 10.5.7.4. Identifying the Clickstream's Dimensions and Facts
        5. 10.5.7.5. Analyzing Clickstream Events
      8. 10.5.8. 10.24 The Special Dimensions of the Clickstream
        1. 10.5.8.1. Visitor Dimension
        2. 10.5.8.2. Page Object Dimension
        3. 10.5.8.3. Session Type
        4. 10.5.8.4. Focus on Page Object and Session Dimensions
      9. 10.5.9. 10.25 Fact Tables for Text Document Searching
        1. 10.5.9.1. Similarity Metrics
        2. 10.5.9.2. Fact Tables for Similarity Measures
        3. 10.5.9.3. Powerful Applications
      10. 10.5.10. 10.26 Enabling Market Basket Analysis
        1. 10.5.10.1. Progressive Pruning Algorithm
  16. 11. Back Room ETL and Data Quality
    1. 11.1. Planning the ETL System
      1. 11.1.1. 11.1 Surrounding the ETL Requirements
        1. 11.1.1.1. Business Needs
        2. 11.1.1.2. Compliance
        3. 11.1.1.3. Data Quality via Data Profiling
        4. 11.1.1.4. Security
        5. 11.1.1.5. Data Integration and the 360 Degree View
        6. 11.1.1.6. Data Latency
        7. 11.1.1.7. Archiving and Lineage
        8. 11.1.1.8. BI User Delivery Interfaces
        9. 11.1.1.9. Available Skills
        10. 11.1.1.10. Legacy Licenses
      2. 11.1.2. 11.2 The 34 Subsystems of ETL
        1. 11.1.2.1. Extracting: Getting Data into the Data Warehouse
        2. 11.1.2.2. Cleaning and Conforming Data
        3. 11.1.2.3. Delivering: Preparing for Presentation
        4. 11.1.2.4. Managing the ETL Environment
      3. 11.1.3. 11.3 Doing the Work at Extract Time
        1. 11.1.3.1. Modeling Events across Multiple Time Zones
        2. 11.1.3.2. Verbose Calendar Dimensions
        3. 11.1.3.3. Keeping the Books across Multiple Currencies
        4. 11.1.3.4. Product Pipeline Measurements
        5. 11.1.3.5. Physical Completeness of the Profit and Loss
        6. 11.1.3.6. Heterogeneous Products
        7. 11.1.3.7. Aggregations in General
        8. 11.1.3.8. Dimensional Modeling in General
      4. 11.1.4. 11.4 Is Data Staging Relational?
        1. 11.1.4.1. Dimension Processing
        2. 11.1.4.2. Deciding What Has Changed
        3. 11.1.4.3. Combining from Separate Sources
        4. 11.1.4.4. Data Cleaning
        5. 11.1.4.5. Processing Names and Addresses
        6. 11.1.4.6. Validating One-to-One and One-to-Many Relationships
        7. 11.1.4.7. Fact Processing
        8. 11.1.4.8. Aggregate Processing
        9. 11.1.4.9. The Bottom Line: Is Data Staging Relational?
      5. 11.1.5. 11.5 Staging Areas and ETL Tools
      6. 11.1.6. 11.6 Should You Use an ETL Tool?
        1. 11.1.6.1. Advantages of ETL Tools
        2. 11.1.6.2. Disadvantages of ETL Tools
        3. 11.1.6.3. Build a Solid Foundation
      7. 11.1.7. 11.7 Document the ETL System
      8. 11.1.8. 11.8 Measure Twice, Cut Once
        1. 11.1.8.1. Objective: High Level ETL Plan
        2. 11.1.8.2. Inputs and Data Flows
        3. 11.1.8.3. Transformation Notes
        4. 11.1.8.4. Finish Planning Before Cutting
      9. 11.1.9. 11.9 Brace for Incoming
        1. 11.1.9.1. Typical Data Integration Process
        2. 11.1.9.2. Architecture
        3. 11.1.9.3. Setup Process
        4. 11.1.9.4. Exception Handling
        5. 11.1.9.5. Deceptive Simplicity
      10. 11.1.10. 11.10 Building a Change Data Capture System
    2. 11.2. Data Quality Considerations
      1. 11.2.1. 11.11 Dealing with Dirty Data
        1. 11.2.1.1. Applications Where Good Data Is Critical
        2. 11.2.1.2. The Science of Data Cleaning
        3. 11.2.1.3. The Marketplace Opportunity for Data Cleaning
        4. 11.2.1.4. Data Integrity Drives Business Reengineering
      2. 11.2.2. 11.12 An Architecture for Data Quality
        1. 11.2.2.1. Establish a Quality Culture and Reengineer the Processes
        2. 11.2.2.2. The Role of Data Profiling
        3. 11.2.2.3. Quality Screens
        4. 11.2.2.4. Error Event Schema
        5. 11.2.2.5. Responding to Quality Events
        6. 11.2.2.6. The Audit Dimension
        7. 11.2.2.7. Six Sigma Data Quality
      3. 11.2.3. 11.13 Indicators of Quality
        1. 11.2.3.1. Start with the Lowest Possible Grain
        2. 11.2.3.2. Reporting Aggregate Data Quality
        3. 11.2.3.3. Building the Audit Dimension
      4. 11.2.4. 11.14 Is Your Data Correct?
        1. 11.2.4.1. Judging Data Quality with No History
        2. 11.2.4.2. Compensating for Predictable Changes
      5. 11.2.5. 11.15 Eight Recommendations for International Data Quality
        1. 11.2.5.1. Languages and Character Sets
        2. 11.2.5.2. Cultures, Names, and Salutations
        3. 11.2.5.3. Geographies and Addresses
        4. 11.2.5.4. Privacy and Information Transfer
        5. 11.2.5.5. International Compliance
        6. 11.2.5.6. Currencies
        7. 11.2.5.7. Time Zones, Calendars, and Date Formats
        8. 11.2.5.8. Numbers
        9. 11.2.5.9. Architectures for International Data Quality
      6. 11.2.6. 11.16 Using Regular Expressions for Data Cleaning
        1. 11.2.6.1. Regular Expressions to the Rescue
        2. 11.2.6.2. Basic Operators
        3. 11.2.6.3. Finding the Occurrences of "Inc"
        4. 11.2.6.4. The Final Results
        5. 11.2.6.5. Where Can You Use RegExps?
    3. 11.3. Populating Fact and Dimension Tables
      1. 11.3.1. 11.17 Pipelining Your Surrogates
        1. 11.3.1.1. Keys for the Dimension Tables
        2. 11.3.1.2. Keys for the Fact Tables
      2. 11.3.2. 11.18 Replicating Dimensions Correctly
      3. 11.3.3. 11.19 Identify Dimension Changes Using Cyclic Redundancy Checksums
      4. 11.3.4. 11.20 Maintaining Back Pointers to Operational Sources
      5. 11.3.5. 11.21 Creating Historical Dimension Rows
        1. 11.3.5.1. Dig for History
        2. 11.3.5.2. Discuss the Options and Implications
        3. 11.3.5.3. Build the Dimension
        4. 11.3.5.4. Choose a Daily or Minute-Second Grain
      6. 11.3.6. 11.22 Backward in Time
        1. 11.3.6.1. Late-Arriving Fact Records
        2. 11.3.6.2. Late-Arriving Dimension Records
      7. 11.3.7. 11.23 Early-Arriving Facts
      8. 11.3.8. 11.24 Slowly Changing Entities
      9. 11.3.9. 11.25 Creating, Using, and Maintaining Junk Dimensions
        1. 11.3.9.1. Build the Initial Junk Dimension
        2. 11.3.9.2. Incorporate the Junk Dimension into the Fact Row Process
        3. 11.3.9.3. Maintain the Junk Dimension
      10. 11.3.10. 11.26 Using the SQL MERGE for Slowly Changing Dimensions
        1. 11.3.10.1. Step 1: Overwrite the Type 1 Changes
        2. 11.3.10.2. Step 2: Handle the Type 2 Changes
      11. 11.3.11. 11.27 Being Offline as Little as Possible
    4. 11.4. Supporting Real Time
      1. 11.4.1. 11.28 Working in Web Time
      2. 11.4.2. 11.29 Real-Time Partitions
        1. 11.4.2.1. Requirements for Real-Time Partitions
        2. 11.4.2.2. Transaction Grain Real-Time Partitions
        3. 11.4.2.3. Periodic Snapshot Real-Time Partition
        4. 11.4.2.4. Accumulating Snapshot Real-Time Partition
      3. 11.4.3. 11.30 The Real-Time Triage
  17. 12. Technical Architecture Considerations
    1. 12.1. Overall Technical/System Architecture
      1. 12.1.1. 12.1 Can the Data Warehouse Benefit from SOA?
      2. 12.1.2. 12.2 Picking the Right Approach to MDM
        1. 12.1.2.1. Source System Disparities
        2. 12.1.2.2. The Need for Master Data
        3. 12.1.2.3. Approach 1: Master Data in the Conformed Data Warehouse
        4. 12.1.2.4. Approach 2: The MDM Integration Hub
        5. 12.1.2.5. Approach 3: The Enterprise MDM System
        6. 12.1.2.6. Four Steps toward MDM
      3. 12.1.3. 12.3 Building Custom Tools for the DW/BI System
      4. 12.1.4. 12.4 Welcoming the Packaged App
        1. 12.1.4.1. Avoid Stovepipe Data Marts
        2. 12.1.4.2. Conforming at Query Time
        3. 12.1.4.3. Vendors Do Take Integration Seriously
      5. 12.1.5. 12.5 ERP Vendors: Bring Down Those Walls
        1. 12.1.5.1. What Are the New Rules?
        2. 12.1.5.2. The Role of ERP in the New Webhouse
      6. 12.1.6. 12.6 Building a Foundation for Smart Applications
        1. 12.1.6.1. The Quick but Risky Path
        2. 12.1.6.2. The Right Way to Smart Apps
        3. 12.1.6.3. When the Infrastructure Falls Short
        4. 12.1.6.4. Supporting Transactional Workloads
        5. 12.1.6.5. Pervasive BI: Spreading BI Everywhere
      7. 12.1.7. 12.7 RFID Tags and Smart Dust
        1. 12.1.7.1. Lifetime Employment
        2. 12.1.7.2. The Assault on Privacy
        3. 12.1.7.3. Beyond RFID to Smart Dust
    2. 12.2. Presentation Server Architecture
      1. 12.2.1. 12.8 The Aggregate Navigator
      2. 12.2.2. 12.9 Aggregate Navigation with (Almost) No Metadata
        1. 12.2.2.1. High Level Goals and Risks
        2. 12.2.2.2. Aggregate Navigation Algorithm
        3. 12.2.2.3. Aggregates for Everyone
      3. 12.2.3. 12.10 Relating to OLAP
        1. 12.2.3.1. Desktop versus Server OLAP
        2. 12.2.3.2. Dimensional Similarities
        3. 12.2.3.3. Dimensional Differences
        4. 12.2.3.4. OLAP's Strengths
      4. 12.2.4. 12.11 Dimensional Relational versus OLAP: The Final Deployment Conundrum
        1. 12.2.4.1. Dimensional Relational Advantages
        2. 12.2.4.2. Relational Disadvantages
        3. 12.2.4.3. OLAP Advantages
        4. 12.2.4.4. OLAP Disadvantages
        5. 12.2.4.5. Equally Easy in Either Approach
        6. 12.2.4.6. Making the Final Choice
      5. 12.2.5. 12.12 Dimensional Modeling for Microsoft Analysis Services
      6. 12.2.6. 12.13 Architecting Your Data for Microsoft SQL Server 2005
      7. 12.2.7. 12.14 Microsoft SQL Server Comes of Age for Data Warehousing
        1. 12.2.7.1. Speed the Queries with Database Compression
        2. 12.2.7.2. Divide and Conquer with Table Partitioning
        3. 12.2.7.3. Go Dimensional with Star Schema Optimization
        4. 12.2.7.4. Up and Coming Opportunities
    3. 12.3. Front Room Architecture
      1. 12.3.1. 12.15 The Second Revolution of User Interfaces
        1. 12.3.1.1. How the Second Revolution Differs from the First
        2. 12.3.1.2. The User Interface Is Now More Urgent
        3. 12.3.1.3. Second Generation User Interface Guidelines
      2. 12.3.2. 12.16 Designing the User Interface
    4. 12.4. Metadata
      1. 12.4.1. 12.17 Meta Meta Data Data
      2. 12.4.2. 12.18 Creating the Metadata Strategy
    5. 12.5. Infrastructure and Security Considerations
      1. 12.5.1. 12.19 Watching the Watchers
        1. 12.5.1.1. Beneficial Uses and Insidious Abuses
        2. 12.5.1.2. Who Owns Your Personal Data?
        3. 12.5.1.3. What Is Likely to Happen?
        4. 12.5.1.4. The Impact on Warehouse Architecture
      2. 12.5.2. 12.20 Catastrophic Failure
        1. 12.5.2.1. Catastrophic Failures
        2. 12.5.2.2. Countering Catastrophic Failures
      3. 12.5.3. 12.21 Digital Preservation
        1. 12.5.3.1. Does a Warehouse Even Need to Keep Old Data?
        2. 12.5.3.2. Media, Formats, Software, and Hardware
        3. 12.5.3.3. Obsolete Formats and Archaic Formats
        4. 12.5.3.4. Hard Copy, Standards, and Museums
        5. 12.5.3.5. Refreshing, Migrating, Emulating, and Encapsulating
      4. 12.5.4. 12.22 Creating the Advantages of a 64-Bit Server
      5. 12.5.5. 12.23 Server Configuration Considerations
        1. 12.5.5.1. Factors Influencing Server Configurations
        2. 12.5.5.2. Adding Capacity
        3. 12.5.5.3. Getting Help
        4. 12.5.5.4. Conclusion
      6. 12.5.6. 12.24 Adjust Your Thinking for SANs
        1. 12.5.6.1. A Postscript
  18. 13. Front Room Business Intelligence Applications
    1. 13.1. Delivering Value with Business Intelligence
      1. 13.1.1. 13.1 The Promise of Decision Support
        1. 13.1.1.1. Analytic Applications Lifecycle
        2. 13.1.1.2. Publish Reports
        3. 13.1.1.3. Identify Exceptions
        4. 13.1.1.4. Determine Causal Factors
        5. 13.1.1.5. Model Alternatives
        6. 13.1.1.6. Track Actions
        7. 13.1.1.7. Stepping Back
      2. 13.1.2. 13.2 Beyond Paving the Cow Paths
        1. 13.1.2.1. Begin with Reported Results
        2. 13.1.2.2. Identify Criteria and Threshold Tolerances
        3. 13.1.2.3. Understand Cause and Effect
        4. 13.1.2.4. Evaluate the Options
        5. 13.1.2.5. Track Actions for Future Optimization
      3. 13.1.3. 13.3 Big Shifts Happening in BI
        1. 13.1.3.1. Compliance Is a Free Pass for BI
        2. 13.1.3.2. Sequential Behavior Analysis Is BI's Mount Everest
      4. 13.1.4. 13.4 Behavior: The Next Marquee Application
        1. 13.1.4.1. CRM: The Stepping Stone to Behavior
        2. 13.1.4.2. The New Analytics of Behavior
    2. 13.2. Implementing the Business Intelligence Layer
      1. 13.2.1. 13.5 Think Like a Software Development Manager
      2. 13.2.2. 13.6 Standard Reports: Basics for Business Users
        1. 13.2.2.1. What Are BI Applications?
        2. 13.2.2.2. Build versus Buy
        3. 13.2.2.3. Designing the Reporting System
        4. 13.2.2.4. In a Nutshell
      3. 13.2.3. 13.7 Building and Delivering BI Reports
        1. 13.2.3.1. Set Up Development
        2. 13.2.3.2. Create the Reports
        3. 13.2.3.3. Test Accuracy and Performance
        4. 13.2.3.4. Deploy to Production
        5. 13.2.3.5. Manage and Maintain
        6. 13.2.3.6. Extend the Applications
      4. 13.2.4. 13.8 The BI Portal
        1. 13.2.4.1. Density
        2. 13.2.4.2. Structure
        3. 13.2.4.3. More Structure and Content
      5. 13.2.5. 13.9 Dashboards Done Right
      6. 13.2.6. 13.10 Don't Be Overly Reliant on Your Data Access Tool's Metadata
    3. 13.3. Mining Data to Uncover Relationships
      1. 13.3.1. 13.11 Digging into Data Mining
        1. 13.3.1.1. The Roots of Data Mining
        2. 13.3.1.2. The Categories of Data Mining
      2. 13.3.2. 13.12 Preparing for Data Mining
        1. 13.3.2.1. General Data Transformations
        2. 13.3.2.2. Transformations for All Forms of Data Mining
        3. 13.3.2.3. Special Tool-Dependent Transformations
      3. 13.3.3. 13.13 The Perfect Handoff
        1. 13.3.3.1. The Perfect Observation
        2. 13.3.3.2. Implications for Database Architecture
      4. 13.3.4. 13.14 Get Started with Data Mining Now
        1. 13.3.4.1. The Business Phase
        2. 13.3.4.2. The Data Mining Phase
        3. 13.3.4.3. The Operations Phase
        4. 13.3.4.4. Role of Data Mining Metadata
    4. 13.4. Dealing with SQL
      1. 13.4.1. 13.15 Simple Drill Across in SQL
      2. 13.4.2. 13.16 The Problem with Comparisons
      3. 13.4.3. 13.17 SQL Roadblocks and Pitfalls
        1. 13.4.3.1. Fixing the SQL Problem
      4. 13.4.4. 13.18 Features for Query Tools
      5. 13.4.5. 13.19 Turbocharge Your Query Tools
      6. 13.4.6. 13.20 Smarter Data Warehouses
        1. 13.4.6.1. SQL Scrutinized
        2. 13.4.6.2. SQL-99 OLAP Extensions
        3. 13.4.6.3. More Advanced Queries
        4. 13.4.6.4. The Extension Problem
        5. 13.4.6.5. OLAP Is an Answer
  19. 14. Maintenance and Growth Considerations
    1. 14.1. Deploying Successfully
      1. 14.1.1. 14.1 Don't Forget the Owner's Manual
        1. 14.1.1.1. Front Room Operations
        2. 14.1.1.2. Back Room Operations
        3. 14.1.1.3. Monitor Operations
      2. 14.1.2. 14.2 Let's Improve Our Operating Procedures
      3. 14.1.3. 14.3 Marketing the DW/BI System
        1. 14.1.3.1. Product
        2. 14.1.3.2. Price
        3. 14.1.3.3. Placement
        4. 14.1.3.4. Promotion
      4. 14.1.4. 14.4 Coping with Growing Pains
        1. 14.1.4.1. Recognize the Target
        2. 14.1.4.2. Case Study Scenario
        3. 14.1.4.3. Not So Fast
        4. 14.1.4.4. Plan Before You Build
        5. 14.1.4.5. Develop, Test, Deploy
        6. 14.1.4.6. Some Final Thoughts
    2. 14.2. Sustaining for Ongoing Impact
      1. 14.2.1. 14.5 Data Warehouse Checkups
        1. 14.2.1.1. Business Sponsor Disorder
        2. 14.2.1.2. Data Disorder
        3. 14.2.1.3. Business Acceptance Disorder
        4. 14.2.1.4. Infrastructure Disorder
        5. 14.2.1.5. Cultural/Political Disorder
        6. 14.2.1.6. Early Detection
      2. 14.2.2. 14.6 Boosting Business Acceptance
        1. 14.2.2.1. DW/BI Business Realignment
        2. 14.2.2.2. Choose the Forum
        3. 14.2.2.3. Identify and Prepare the Interview Team
        4. 14.2.2.4. Select, Schedule, and Prepare Business Representatives
        5. 14.2.2.5. Conduct the Interviews
        6. 14.2.2.6. Document, Prioritize, and Reach Consensus
      3. 14.2.3. 14.7 Educate Management to Sustain DW/BI Success
        1. 14.2.3.1. Gathering Evidence
        2. 14.2.3.2. Educating the Business: The User Forum
        3. 14.2.3.3. Educating Senior Staff
        4. 14.2.3.4. Working with Steering Committees
        5. 14.2.3.5. Conclusion
      4. 14.2.4. 14.8 Getting Your Data Warehouse Back on Track
      5. 14.2.5. 14.9 Upgrading Your BI Architecture
      6. 14.2.6. 14.10 Four Fixes for Legacy Data Warehouses
        1. 14.2.6.1. Conform the Nonconformed Dimensions
        2. 14.2.6.2. Create Surrogate Keys
        3. 14.2.6.3. Deliver the Details
        4. 14.2.6.4. Reduce Redundancies
        5. 14.2.6.5. Face the Realities
      7. 14.2.7. 14.11 A Data Warehousing Fitness Program for Lean Times
        1. 14.2.7.1. Cut the Flab
        2. 14.2.7.2. Monitor and Tune to Defer Spending
        3. 14.2.7.3. Bulk Up the Bottom Line
  20. Article Index