| Introduction |
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xxv | |
| 1 The Reader at a Glance |
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1 | (34) |
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1 | (5) |
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1.1 Resist the Urge to Start Coding |
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1 | (3) |
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4 | (2) |
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Tackling DW/BI Design and Development |
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6 | (29) |
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6 | (3) |
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9 | (1) |
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10 | (3) |
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1.6 Essential Steps for the Integrated Enterprise Data Warehouse |
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13 | (9) |
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1.7 Drill Down to Ask Why |
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22 | (3) |
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1.8 Slowly Changing Dimensions |
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25 | (3) |
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1.9 Judge Your BI Tool through Your Dimensions |
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28 | (3) |
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31 | (2) |
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1.11 Exploit Your Fact Tables |
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33 | (2) |
| 2 Before You Dive In |
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35 | (40) |
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35 | (2) |
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2.1 History Lesson on Ralph Kimball and Xerox PARC |
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36 | (1) |
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37 | (10) |
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2.2 The Database Market Splits |
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37 | (3) |
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2.3 Bringing Up Supermarts |
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40 | (7) |
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Dealing with Demanding Realities |
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47 | (28) |
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2.4 Brave New Requirements for Data Warehousing |
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47 | (5) |
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2.5 Coping with the Brave New Requirements |
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52 | (5) |
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57 | (3) |
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2.7 Design Constraints and Unavoidable Realities |
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60 | (4) |
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64 | (3) |
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2.9 Data Warehouse Dining Experience |
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67 | (3) |
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2.10 Easier Approaches for Harder Problems |
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70 | (2) |
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2.11 Expanding Boundaries of the Data Warehouse |
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72 | (3) |
| 3 Project/Program Planning |
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75 | (48) |
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Professional Responsibilities |
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75 | (25) |
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3.1 Professional Boundaries |
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75 | (3) |
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78 | (4) |
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3.3 Beware the Objection Removers |
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82 | (4) |
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3.4 What Does the Central Team Do? |
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86 | (4) |
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3.5 Avoid Isolating DW and BI Teams |
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90 | (1) |
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3.6 Better Business Skills for BI and Data Warehouse Professionals |
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91 | (2) |
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3.7 Risky Project Resources Are Risky Business |
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93 | (2) |
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3.8 Implementation Analysis Paralysis |
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95 | (1) |
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3.9 Contain DW/BI Scope Creep and Avoid Scope Theft |
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96 | (2) |
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3.10 Are IT Procedures Beneficial to DW/BI Projects? |
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98 | (2) |
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Justification and Sponsorship |
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100 | (8) |
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3.11 Habits of Effective Sponsors |
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100 | (3) |
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3.12 TCO Starts with the End User |
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103 | (5) |
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108 | (15) |
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3.13 Kimball Lifecycle in a Nutshell |
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108 | (3) |
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111 | (1) |
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112 | (3) |
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3.16 Think Critically When Applying Best Practices |
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115 | (3) |
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3.17 Eight Guidelines for Low Risk Enterprise Data Warehousing |
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118 | (5) |
| 4 Requirements Definition |
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123 | (24) |
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123 | (11) |
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4.1 Alan Alda's Interviewing Tips for Uncovering Business Requirements |
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123 | (4) |
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4.2 More Business Requirements Gathering Dos and Don'ts |
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127 | (2) |
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4.3 Balancing Requirements and Realities |
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129 | (1) |
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4.4 Overcoming Obstacles When Gathering Business Requirements |
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130 | (3) |
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4.5 Surprising Value of Data Profiling |
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133 | (1) |
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Organizing around Business Processes |
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134 | (5) |
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4.6 Focus on Business Processes, Not Business Departments! |
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134 | (1) |
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4.7 Identifying Business Processes |
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135 | (2) |
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4.8 Business Process Decoder Ring |
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137 | (1) |
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4.9 Relationship between Strategic Business Initiatives and Business Processes |
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138 | (1) |
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Wrapping Up the Requirements |
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139 | (8) |
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4.10 The Bottom-Up Misnomer |
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140 | (4) |
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4.11 Think Dimensionally (Beyond Data Modeling) |
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144 | (1) |
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4.12 Using the Dimensional Model to Validate Business Requirements |
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145 | (2) |
| 5 Data Architecture |
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147 | (56) |
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Making the Case for Dimensional Modeling |
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147 | (16) |
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5.1 Is ER Modeling Hazardous to DSS? |
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147 | (4) |
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5.2 A Dimensional Modeling Manifesto |
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151 | (8) |
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5.3 There Are No Guarantees |
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159 | (4) |
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Enterprise Data Warehouse Bus Architecture |
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163 | (13) |
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163 | (3) |
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166 | (4) |
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5.6 The Matrix: Revisited |
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170 | (4) |
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5.7 Drill Down into a Detailed Bus Matrix |
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174 | (2) |
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Agile Project Considerations |
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176 | (5) |
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5.8 Relating to Agile Methodologies |
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176 | (1) |
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5.9 Is Agile Enterprise Data Warehousing an Oxymoron? |
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177 | (2) |
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5.10 Going Agile? Start with the Bus Matrix |
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179 | (1) |
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5.11 Conformed Dimensions as the Foundation for Agile Data Warehousing |
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180 | (1) |
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Integration Instead of Centralization |
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181 | (11) |
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5.12 Integration for Real People |
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181 | (4) |
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5.13 Build a Ready-to-Go Resource for Enterprise Dimensions |
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185 | (1) |
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5.14 Data Stewardship 101: The First Step to Quality and Consistency |
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186 | (3) |
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5.15 To Be or Not To Be Centralized |
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189 | (3) |
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Contrast with the Corporate Information Factory |
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192 | (11) |
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5.16 Differences of Opinion |
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193 | (5) |
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5.17 Much Ado about Nothing |
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198 | (1) |
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5.18 Don't Support Business Intelligence with a Normalized EDW |
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199 | (2) |
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5.19 Complementing 3NF EDWs with Dimensional Presentation Areas |
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201 | (2) |
| 6 Dimensional Modeling Fundamentals |
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203 | (30) |
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Basics of Dimensional Modeling |
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203 | (17) |
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6.1 Fact Tables and Dimension Tables |
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203 | (4) |
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6.2 Drilling Down, Up, and Across |
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207 | (3) |
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6.3 The Soul of the Data Warehouse, Part One: Drilling Down |
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210 | (3) |
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6.4 The Soul of the Data Warehouse, Part Two: Drilling Across |
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213 | (3) |
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6.5 The Soul of the Data Warehouse, Part Three: Handling Time |
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216 | (3) |
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6.6 Graceful Modifications to Existing Fact and Dimension Tables |
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219 | (1) |
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220 | (6) |
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6.7 Kimball's Ten Essential Rules of Dimensional Modeling |
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221 | (2) |
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223 | (3) |
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Myths about Dimensional Modeling |
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226 | (7) |
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6.9 Dangerous Preconceptions |
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226 | (2) |
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228 | (5) |
| 7 Dimensional Modeling Tasks and Responsibilities |
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233 | (34) |
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233 | (21) |
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7.1 Letting the Users Sleep |
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233 | (7) |
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7.2 Practical Steps for Designing a Dimensional Model |
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240 | (3) |
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7.3 Staffing the Dimensional Modeling Team |
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243 | (1) |
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7.4 Involve Business Representatives in Dimensional Modeling |
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244 | (2) |
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7.5 Managing Large Dimensional Design Teams |
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246 | (2) |
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7.6 Use a Design Charter to Keep Dimensional Modeling Activities on Track |
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248 | (1) |
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249 | (1) |
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250 | (3) |
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7.9 When Is the Dimensional Design Done |
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253 | (1) |
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254 | (13) |
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7.10 Design Review Dos and Don'ts |
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255 | (2) |
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257 | (3) |
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7.12 Rating Your Dimensional Data Warehouse |
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260 | (7) |
| 8 Fact Table Core Concepts |
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267 | (60) |
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267 | (9) |
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267 | (3) |
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8.2 Keep to the Grain in Dimensional Modeling |
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270 | (2) |
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8.3 Warning: Summary Data May Be Hazardous to Your Health |
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272 | (1) |
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273 | (3) |
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276 | (28) |
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277 | (3) |
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8.6 Modeling a Pipeline with an Accumulating Snapshot |
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280 | (2) |
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8.7 Combining Periodic and Accumulating Snapshots |
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282 | (2) |
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8.8 Complementary Fact Table Types |
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284 | (2) |
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286 | (3) |
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8.10 A Rolling Prediction of the Future, Now and in the Past |
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289 | (4) |
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8.11 Timespan Accumulating Snapshot Fact Tables |
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293 | (1) |
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8.12 Is it a Dimension, a Fact, or Both? |
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294 | (1) |
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8.13 Factless Fact Tables |
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295 | (3) |
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8.14 Factless Fact Tables? Sounds Like Jumbo Shrimp? |
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298 | (1) |
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299 | (3) |
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8.16 Factless Fact Tables for Simplification |
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302 | (2) |
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304 | (5) |
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8.17 Managing Your Parents |
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304 | (3) |
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8.18 Patterns to Avoid When Modeling Header/Line Item Transactions |
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307 | (2) |
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Fact Table Keys and Degenerate Dimensions |
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309 | (5) |
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8.19 Fact Table Surrogate Keys |
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309 | (1) |
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8.20 Reader Suggestions on Fact Table Surrogate Keys |
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310 | (2) |
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8.21 Another Look at Degenerate Dimensions |
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312 | (1) |
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8.22 Creating a Reference Dimension for Infrequently Accessed Degenerates |
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313 | (1) |
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Miscellaneous Fact Table Design Patterns |
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314 | (13) |
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8.23 Put Your Fact Tables on a Diet |
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314 | (2) |
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8.24 Keeping Text Out of the Fact Table |
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316 | (1) |
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8.25 Dealing with Nulls in a Dimensional Model |
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317 | (1) |
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8.26 Modeling Data as Both a Fact and Dimension Attribute |
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318 | (1) |
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8.27 When a Fact Table Can Be Used as a Dimension Table |
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319 | (2) |
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8.28 Sparse Facts and Facts with Short Lifetimes |
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321 | (2) |
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8.29 Pivoting the Fact Table with a Fact Dimension |
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323 | (1) |
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8.30 Accumulating Snapshots for Complex Workflows |
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324 | (3) |
| 9 Dimension Table Core Concepts |
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327 | (58) |
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327 | (7) |
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327 | (4) |
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9.2 Keep Your Keys Simple |
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331 | (2) |
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9.3 Durable "Super-Natural" Keys |
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333 | (1) |
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Date and Time Dimension Considerations |
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334 | (11) |
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335 | (2) |
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9.5 Surrogate Keys for the Time Dimension |
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337 | (2) |
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9.6 Latest Thinking on Time Dimension Tables |
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339 | (2) |
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9.7 Smart Date Keys to Partition Fact Tables |
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341 | (1) |
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9.8 Updating the Date Dimension |
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342 | (1) |
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9.9 Handling All the Dates |
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343 | (2) |
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Miscellaneous Dimension Patterns |
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345 | (18) |
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9.10 Selecting Default Values for Nulls |
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345 | (2) |
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9.11 Data Warehouse Role Models |
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347 | (3) |
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350 | (3) |
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9.13 De-Clutter with Junk Dimensions |
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353 | (1) |
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9.14 Showing the Correlation between Dimensions |
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354 | (2) |
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9.15 Causal (Not Casual) Dimensions |
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356 | (3) |
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9.16 Resist Abstract Generic Dimensions |
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359 | (1) |
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9.17 Hot-Swappable Dimensions |
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360 | (1) |
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9.18 Accurate Counting with a Dimensional Supplement |
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361 | (2) |
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Slowly Changing Dimensions |
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363 | (22) |
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9.19 Perfectly Partitioning History with Type 2 SCD |
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363 | (1) |
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9.20 Many Alternate Realities |
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364 | (3) |
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367 | (3) |
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9.22 When a Slowly Changing Dimension Speeds Up |
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370 | (2) |
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9.23 When Do Dimensions Become Dangerous? |
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372 | (1) |
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9.24 Slowly Changing Dimensions Are Not Always as Easy as 1, 2, and 3 |
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373 | (5) |
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9.25 Slowly Changing Dimension Types 0, 4, 5, 6 and 7 |
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378 | (4) |
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9.26 Dimension Row Change Reason Attributes |
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382 | (3) |
| 10 More Dimension Patterns and Considerations |
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385 | (110) |
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Snowflakes, Outriggers, and Bridges |
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385 | (24) |
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10.1 Snowflakes, Outriggers, and Bridges |
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385 | (3) |
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10.2 A Trio of Interesting Snowflakes |
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388 | (4) |
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10.3 Help for Dimensional Modeling |
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392 | (3) |
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10.4 Managing Bridge Tables |
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395 | (4) |
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10.5 The Keyword Dimension |
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399 | (4) |
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10.6 Potential Bridge (Table) Detours |
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403 | (2) |
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10.7 Alternatives for Multi-Valued Dimensions |
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405 | (2) |
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10.8 Adding a Mini-Dimension to a Bridge Table |
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407 | (2) |
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409 | (18) |
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10.9 Maintaining Dimension Hierarchies |
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409 | (5) |
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10.10 Help for Hierarchies |
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414 | (3) |
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10.11 Five Alternatives for Better Employee Dimensional Modeling |
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417 | (8) |
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10.12 Avoiding Alternate Organization Hierarchies |
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425 | (1) |
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10.13 Alternate Hierarchies |
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426 | (1) |
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427 | (12) |
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10.14 Dimension Embellishments |
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427 | (2) |
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10.15 Wrangling Behavior Tags |
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429 | (2) |
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10.16 Three Ways to Capture Customer Satisfaction |
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431 | (4) |
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10.17 Extreme Status Tracking for Real-Time Customer Analysis |
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435 | (4) |
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Addresses and International Issues |
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439 | (14) |
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10.18 Think Globally, Act Locally |
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439 | (4) |
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10.19 Warehousing without Borders |
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443 | (5) |
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10.20 Spatially Enabling Your Data Warehouse |
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448 | (4) |
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10.21 Multinational Dimensional Data Warehouse Considerations |
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452 | (1) |
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Industry Scenarios and Idiosyncrasies |
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453 | (42) |
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10.22 Industry Standard Data Models Fall Short |
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453 | (2) |
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10.23 An Insurance Data Warehouse Case Study |
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455 | (5) |
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10.24 Traveling through Databases |
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460 | (3) |
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10.25 Human Resources Dimensional Models |
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463 | (4) |
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10.26 Managing Backlogs Dimensionally |
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467 | (1) |
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468 | (3) |
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10.28 The Budgeting Chain |
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471 | (4) |
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10.29 Compliance-Enabled Data Warehouses |
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475 | (2) |
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10.30 Clicking with Your Customer |
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477 | (5) |
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10.31 The Special Dimensions of the Clickstream |
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482 | (3) |
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10.32 Fact Tables for Text Document Searching |
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485 | (4) |
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10.33 Enabling Market Basket Analysis |
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489 | (6) |
| 11 Back Room ETL and Data Quality |
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495 | (122) |
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495 | (40) |
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11.1 Surrounding the ETL Requirements |
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495 | (5) |
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11.2 The 34 Subsystems of ETL |
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500 | (4) |
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11.3 Six Key Decisions for ETL Architectures |
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504 | (4) |
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11.4 Three ETL Compromises to Avoid |
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508 | (2) |
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11.5 Doing the Work at Extract Time |
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510 | (3) |
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11.6 Is Data Staging Relational? |
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513 | (4) |
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11.7 Staging Areas and ETL Tools |
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517 | (1) |
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11.8 Should You Use an ETL Tool? |
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518 | (3) |
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11.9 Call to Action for ETL Tool Providers |
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521 | (1) |
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11.10 Document the ETL System |
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522 | (1) |
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11.11 Measure Twice, Cut Once |
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523 | (4) |
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527 | (3) |
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11.13 Building a Change Data Capture System |
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530 | (1) |
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11.14 Disruptive ETL Changes |
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531 | (2) |
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11.15 New Directions for ETL |
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533 | (2) |
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Data Quality Considerations |
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535 | (37) |
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11.16 Dealing With Data Quality: Don't Just Sit There, Do Something! |
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535 | (2) |
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11.17 Data Warehouse Testing Recommendations |
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537 | (2) |
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11.18 Dealing with Dirty Data |
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539 | (6) |
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11.19 An Architecture for Data Quality |
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545 | (8) |
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11.20 Indicators of Quality: The Audit Dimension |
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553 | (3) |
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11.21 Adding an Audit Dimension to Track Lineage and Confidence |
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556 | (3) |
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11.22 Add Uncertainty to Your Fact Table |
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559 | (1) |
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11.23 Have You Built Your Audit Dimension Yet? |
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560 | (2) |
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11.24 Is Your Data Correct? |
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562 | (3) |
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11.25 Eight Recommendations for International Data Quality |
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565 | (3) |
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11.26 Using Regular Expressions for Data Cleaning |
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568 | (4) |
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Populating Fact and Dimension Tables |
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572 | (34) |
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11.27 Pipelining Your Surrogates |
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572 | (4) |
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11.28 Unclogging the Fact Table Surrogate Key Pipeline |
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576 | (3) |
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11.29 Replicating Dimensions Correctly |
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579 | (1) |
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11.30 Identify Dimension Changes Using Cyclic Redundancy Checksums |
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580 | (1) |
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11.31 Maintaining Back Pointers to Operational Sources |
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581 | (1) |
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11.32 Creating Historical Dimension Rows |
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582 | (3) |
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11.33 Facing the Re-Keying Crisis |
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585 | (2) |
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587 | (3) |
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11.35 Early-Arriving Facts |
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590 | (1) |
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11.36 Slowly Changing Entities |
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591 | (2) |
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11.37 Using the SQL MERGE Statement for Slowly Changing Dimensions |
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593 | (2) |
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11.38 Creating and Managing Shrunken Dimensions |
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595 | (2) |
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11.39 Creating and Managing Mini-Dimensions |
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597 | (2) |
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11.40 Creating, Using, and Maintaining Junk Dimensions |
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599 | (2) |
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601 | (4) |
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11.42 Being Offline as Little as Possible |
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605 | (1) |
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606 | (11) |
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11.43 Working in Web Time |
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606 | (4) |
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11.44 Real-Time Partitions |
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610 | (3) |
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11.45 The Real-Time Triage |
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613 | (4) |
| 12 Technical Architecture Considerations |
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617 | (112) |
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Overall Technical/System Architecture |
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617 | (55) |
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12.1 Can the Data Warehouse Benefit from SOA? |
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617 | (2) |
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12.2 Picking the Right Approach to MDM |
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619 | (6) |
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12.3 Building Custom Tools for the DW/BI System |
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625 | (1) |
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12.4 Welcoming the Packaged App |
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626 | (3) |
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12.5 ERP Vendors: Bring Down Those Walls |
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629 | (3) |
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12.6 Building a Foundation for Smart Applications |
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632 | (5) |
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12.7 RFID Tags and Smart Dust |
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637 | (3) |
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12.8 Is Big Data Compatible with the Data Warehouse? |
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640 | (1) |
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12.9 The Evolving Role of the Enterprise Data Warehouse in the Era of Big Data Analytics |
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641 | (18) |
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12.10 Newly Emerging Best Practices for Big Data |
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659 | (11) |
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12.11 The Hyper-Granular Active Archive |
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670 | (2) |
|
Presentation Server Architecture |
|
|
672 | (25) |
|
12.12 Columnar Databases: Game Changers for DW/BI Deployment |
|
|
672 | (1) |
|
12.13 There Is no Database Magic |
|
|
673 | (3) |
|
|
|
676 | (3) |
|
12.15 Dimensional Relational versus OLAP: The Final Deployment Conundrum |
|
|
679 | (3) |
|
12.16 Microsoft SQL Server Comes of Age for Data Warehousing |
|
|
682 | (4) |
|
12.17 The Aggregate Navigator |
|
|
686 | (4) |
|
12.18 Aggregate Navigation with (Almost) No Metadata |
|
|
690 | (7) |
|
|
|
697 | (7) |
|
12.19 The Second Revolution of User Interfaces |
|
|
697 | (3) |
|
12.20 Designing the User Interface |
|
|
700 | (4) |
|
|
|
704 | (8) |
|
12.21 Meta Meta Data Data |
|
|
704 | (4) |
|
12.22 Creating the Metadata Strategy |
|
|
708 | (1) |
|
12.23 Leverage Process Metadata for Self-Monitoring DW Operations |
|
|
709 | (3) |
|
Infrastructure and Security Considerations |
|
|
712 | (17) |
|
12.24 Watching the Watchers |
|
|
712 | (4) |
|
12.25 Catastrophic Failure |
|
|
716 | (3) |
|
12.26 Digital Preservation |
|
|
719 | (3) |
|
12.27 Creating the Advantages of a 64-Bit Server |
|
|
722 | (1) |
|
12.28 Server Configuration Considerations |
|
|
723 | (3) |
|
12.29 Adjust Your Thinking for SANs |
|
|
726 | (3) |
| 13 Front Room Business intelligence Applications |
|
729 | (76) |
|
Delivering Value with Business Intelligence |
|
|
729 | (14) |
|
13.1 The Promise of Decision Support |
|
|
730 | (3) |
|
13.2 Beyond Paving the Cow Paths |
|
|
733 | (3) |
|
13.3 BI Components for Business Value |
|
|
736 | (2) |
|
13.4 Big Shifts Happening in BI |
|
|
738 | (2) |
|
13.5 Behavior: The Next Marquee Application |
|
|
740 | (3) |
|
Implementing the Business Intelligence Layer |
|
|
743 | (21) |
|
13.6 Three Critical Components for Successful Self-Service BI |
|
|
743 | (2) |
|
13.7 Leverage Data Visualization Tools, But Avoid Anarchy |
|
|
745 | (2) |
|
13.8 Think Like a Software Development Manager |
|
|
747 | (1) |
|
13.9 Standard Reports: Basics for Business Users |
|
|
748 | (5) |
|
13.10 Building and Delivering BI Reports |
|
|
753 | (4) |
|
|
|
757 | (2) |
|
13.12 Dashboards Done Right |
|
|
759 | (1) |
|
13.13 Don't Be Overly Reliant on Your Data Access Tool's Metadata |
|
|
760 | (2) |
|
13.14 Making Sense of the Semantic Layer |
|
|
762 | (2) |
|
Mining Data to Uncover Relationships |
|
|
764 | (17) |
|
13.15 Digging into Data Mining |
|
|
764 | (2) |
|
13.16 Preparing for Data Mining |
|
|
766 | (4) |
|
13.17 The Perfect Handoff |
|
|
770 | (4) |
|
13.18 Get Started with Data Mining Now |
|
|
774 | (4) |
|
13.19 Leverage Your Dimensional Model for Predictive Analytics |
|
|
778 | (1) |
|
13.20 Does Your Organization Need an Analytic Sandbox? |
|
|
779 | (2) |
|
|
|
781 | (24) |
|
13.21 Simple Drill Across in SQL |
|
|
781 | (2) |
|
13.22 An Excel Macro for Drilling Across |
|
|
783 | (2) |
|
13.23 The Problem with Comparisons |
|
|
785 | (4) |
|
13.24 SQL Roadblocks and Pitfalls |
|
|
789 | (3) |
|
13.25 Features for Query Tools |
|
|
792 | (2) |
|
13.26 Turbocharge Your Query Tools |
|
|
794 | (4) |
|
13.27 Smarter Data Warehouses |
|
|
798 | (7) |
| 14 Maintenance and Growth Considerations |
|
805 | (36) |
|
|
|
805 | (11) |
|
14.1 Don't Forget the Owner's Manual |
|
|
805 | (4) |
|
14.2 Let's Improve Our Operating Procedures |
|
|
809 | (2) |
|
14.3 Marketing the DW/BI System |
|
|
811 | (1) |
|
14.4 Coping with Growing Pains |
|
|
812 | (4) |
|
Sustaining for Ongoing Impact |
|
|
816 | (25) |
|
14.5 Data Warehouse Checkups |
|
|
816 | (6) |
|
14.6 Boosting Business Acceptance |
|
|
822 | (3) |
|
14.7 Educate Management to Sustain DW/BI Success |
|
|
825 | (3) |
|
14.8 Getting Your Data Warehouse Back on Track |
|
|
828 | (1) |
|
14.9 Upgrading Your BI Architecture |
|
|
829 | (2) |
|
14.10 Four Fixes for Legacy Data Warehouses |
|
|
831 | (4) |
|
14.11 A Data Warehousing Fitness Program for Lean Times |
|
|
835 | (4) |
|
|
|
839 | (2) |
| 15 Final Thoughts |
|
841 | (12) |
|
Key Insights and Reminders |
|
|
841 | (6) |
|
15.1 Final Word of the Day: Collaboration |
|
|
841 | (2) |
|
15.2 Tried and True Concepts for DW/BI Success |
|
|
843 | (2) |
|
15.3 Key Tenets of the Kimball Method |
|
|
845 | (2) |
|
|
|
847 | (6) |
|
15.4 The Future Is Bright |
|
|
847 | (6) |
| Article Index |
|
853 | (8) |
| Index |
|
861 | |
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