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The Kimball Group Reader, Remastered Collection is the essential reference for data warehouse and business intelligence design, packed with best practices, design tips, and valuable insight from industry pioneer Ralph Kimball and the Kimball Group. This Remastered Collection represents decades of expert advice and mentoring in data warehousing and business intelligence, and is the final work to be published by the Kimball Group. Organized for quick navigation and easy reference, this book contains nearly 20 years of experience on more than 300 topics, all fully up-to-date and expanded with 65 new articles. The discussion covers the complete data warehouse/business intelligence lifecycle, including project planning, requirements gathering, system architecture, dimensional modeling, ETL, and business intelligence analytics, with each group of articles prefaced by original commentaries explaining their role in the overall Kimball Group methodology.
Data warehousing/business intelligence industry's current multi-billion dollar value is due in no small part to the contributions of Ralph Kimball and the Kimball Group. Their publications are the standards on which the industry is built, and nearly all data warehouse hardware and software vendors have adopted their methods in one form or another. This book is a compendium of Kimball Group expertise, and an essential reference for anyone in the field.
* Learn data warehousing and business intelligence from the field's pioneers
* Get up to date on best practices and essential design tips
* Gain valuable knowledge on every stage of the project lifecycle
* Dig into the Kimball Group methodology with hands-on guidance
Ralph Kimball and the Kimball Group have continued to refine their methods and techniques based on thousands of hours of consulting and training. This Remastered Collection of The Kimball Group Reader represents their final body of knowledge, and is nothing less than a vital reference for anyone involved in the field.
The Kimball Group Reader, Remastered Collection is the essential reference for data warehouse and business intelligence design, packed with best practices, design tips, and valuable insight from industry pioneer Ralph Kimball and the Kimball Group. This Remastered Collection represents decades of expert advice and mentoring in data warehousing and business intelligence, and is the final work to be published by the Kimball Group. Organized for quick navigation and easy reference, this book contains nearly 20 years of experience on more than 300 topics, all fully up-to-date and expanded with 65 new articles. The discussion covers the complete data warehouse/business intelligence lifecycle, including project planning, requirements gathering, system architecture, dimensional modeling, ETL, and business intelligence analytics, with each group of articles prefaced by original commentaries explaining their role in the overall Kimball Group methodology.
Data warehousing/business intelligence industry's current multi-billion dollar value is due in no small part to the contributions of Ralph Kimball and the Kimball Group. Their publications are the standards on which the industry is built, and nearly all data warehouse hardware and software vendors have adopted their methods in one form or another. This book is a compendium of Kimball Group expertise, and an essential reference for anyone in the field.
* Learn data warehousing and business intelligence from the field's pioneers
* Get up to date on best practices and essential design tips
* Gain valuable knowledge on every stage of the project lifecycle
* Dig into the Kimball Group methodology with hands-on guidance
Ralph Kimball and the Kimball Group have continued to refine their methods and techniques based on thousands of hours of consulting and training. This Remastered Collection of The Kimball Group Reader represents their final body of knowledge, and is nothing less than a vital reference for anyone involved in the field.
Ralph Kimball, PhD, founded the Kimball Group and is a leading visionary in the data warehousing industry.
Margy Ross, President of the Kimball Group and DecisionWorks Consulting, has focused on DW/BI solutions since 1982.
Introduction xxv
1 The Reader at a Glance 1
Setting Up for Success 1
1.1 Resist the Urge to Start Coding 1
1.2 Set Your Boundaries 4
Tackling DW/BI Design and Development 6
1.3 Data Wrangling 6
1.4 Myth Busters 9
1.5 Dividing the World 10
1.6 Essential Steps for the Integrated Enterprise Data Warehouse 13
1.7 Drill Down to Ask Why 22
1.8 Slowly Changing Dimensions 25
1.9 Judge Your BI Tool through Your Dimensions 28
1.10 Fact Tables 31
1.11 Exploit Your Fact Tables 33
2 Before You Dive In 35
Before Data Warehousing 35
2.1 History Lesson on Ralph Kimball and Xerox PARC 36
Historical Perspective 37
2.2 The Database Market Splits 37
2.3 Bringing Up Supermarts 40
Dealing with Demanding Realities 47
2.4 Brave New Requirements for Data Warehousing 47
2.5 Coping with the Brave New Requirements 52
2.6 Stirring Things Up 57
2.7 Design Constraints and Unavoidable Realities 60
2.8 Two Powerful Ideas 64
2.9 Data Warehouse Dining Experience 67
2.10 Easier Approaches for Harder Problems 70
2.11 Expanding Boundaries of the Data Warehouse 72
3 Project/Program Planning 75
Professional Responsibilities 75
3.1 Professional Boundaries 75
3.2 An Engineer's View 78
3.3 Beware the Objection Removers 82
3.4 What Does the Central Team Do? 86
3.5 Avoid Isolating DW and BI Teams 90
3.6 Better Business Skills for BI and Data Warehouse Professionals 91
3.7 Risky Project Resources are Risky Business 93
3.8 Implementation Analysis Paralysis 95
3.9 Contain DW/BI Scope Creep and Avoid Scope Theft 96
3.10 Are IT Procedures Beneficial to DW/BI Projects? 98
Justification and Sponsorship 100
3.11 Habits of Effective Sponsors 100
3.12 TCO Starts with the End User 103
Kimball Methodology 108
3.13 Kimball Lifecycle in a Nutshell 108
3.14 Off the Bench111
3.15 The Anti-Architect112
3.16 Think Critically When Applying Best Practices 115
3.17 Eight Guidelines for Low Risk Enterprise Data Warehousing 118
4 Requirements Definition 123
Gathering Requirements 123
4.1 Alan Alda's Interviewing Tips for Uncovering Business Requirements 123
4.2 More Business Requirements Gathering Dos and Don'ts 127
4.3 Balancing Requirements and Realities 129
4.4 Overcoming Obstacles When Gathering Business Requirements 130
4.5 Surprising Value of Data Profiling 133
Organizing around Business Processes 134
4.6 Focus on Business Processes, Not Business Departments! 134
4.7 Identifying Business Processes 135
4.8 Business Process Decoder Ring 137
4.9 Relationship between Strategic Business Initiatives and Business Processes 138
Wrapping Up the Requirements 139
4.10 The Bottom-Up Misnomer 140
4.11 Think Dimensionally (Beyond Data Modeling) 144
4.12 Using the Dimensional Model to Validate Business Requirements 145
5 Data Architecture 147
Making the Case for Dimensional Modeling 147
5.1 Is ER Modeling Hazardous to DSS? 147
5.2 A Dimensional Modeling Manifesto 151
5.3 There are No Guarantees 159
Enterprise Data Warehouse Bus Architecture 163
5.4 Divide and Conquer 163
5.5 The Matrix 166
5.6 The Matrix: Revisited 170
5.7 Drill Down into a Detailed Bus Matrix 174
Agile Project Considerations 176
5.8 Relating to Agile Methodologies 176
5.9 Is Agile Enterprise Data Warehousing an Oxymoron? 177
5.10 Going Agile? Start with the Bus Matrix 179
5.11 Conformed Dimensions as the Foundation for Agile Data Warehousing 180
Integration Instead of Centralization 181
5.12 Integration for Real People 181
5.13 Build a Ready-to-Go Resource for Enterprise Dimensions 185
5.14 Data Stewardship 101: The First Step to Quality and Consistency 186
5.15 To Be or Not To Be Centralized 189
Contrast with the Corporate Information Factory 192
5.16 Differences of Opinion 193
5.17 Much Ado about Nothing 198
5.18 Don't Support Business Intelligence with a Normalized EDW 199
5.19 Complementing 3NF EDWs with Dimensional Presentation Areas 201
6 Dimensional Modeling Fundamentals 203
Basics of Dimensional Modeling 203
6.1 Fact Tables and Dimension Tables 203
6.2 Drilling Down, Up, and Across 207
6.3 The Soul of the Data Warehouse, Part One: Drilling Down 210
6.4 The Soul of the Data Warehouse, Part Two: Drilling Across 213
6.5 The Soul of the Data Warehouse, Part Three: Handling Time 216
6.6 Graceful Modifications to Existing Fact and Dimension Tables 219
Dos and Don'ts 220
6.7 Kimball's Ten Essential Rules of Dimensional Modeling 221
6.8 What Not to Do 223
Myths about Dimensional Modeling 226
6.9 Dangerous Preconceptions 226
6.10 Fables and Facts 228
7 Dimensional Modeling Tasks and Responsibilities 233
Design Activities 233
7.1 Letting the Users Sleep 233
7.2 Practical Steps for Designing a Dimensional Model 240
7.3 Staffing the Dimensional Modeling Team 243
7.4 Involve Business Representatives in Dimensional Modeling 244
7.5 Managing Large Dimensional Design Teams 246
7.6 Use a Design Charter to Keep Dimensional Modeling Activities on Track 248
7.7 The Naming Game 249
7.8 What's in a Name? 250
7.9 When is the Dimensional Design Done? 253
Design Review Activities 254
7.10 Design Review Dos and Don'ts 255
7.11 Fistful of Flaws 257
7.12 Rating Your Dimensional Data Warehouse 260
8 Fact Table Core Concepts 267
Granularity 267
8.1 Declaring the Grain 267
8.2 Keep to the Grain in Dimensional Modeling 270
8.3 Warning: Summary Data May Be Hazardous to Your Health 272
8.4 No Detail Too Small 273
Types of Fact Tables 276
8.5 Fundamental Grains 277
8.6 Modeling a Pipeline with an Accumulating Snapshot 280
8.7 Combining Periodic and Accumulating Snapshots 282
8.8 Complementary Fact Table Types 284
8.9 Modeling Time Spans 286
8.10 A Rolling Prediction of the Future, Now and in the Past 289
8.11 Timespan Accumulating Snapshot Fact Tables 293
8.12 Is it a Dimension, a Fact, or Both? 294
8.13 Factless Fact Tables 295
8.14 Factless Fact Tables? Sounds Like Jumbo Shrimp? 298
8.15 What Didn't Happen 299
8.16 Factless Fact Tables for Simplification 302
Parent-Child Fact Tables 304
8.17 Managing Your Parents 304
8.18 Patterns to Avoid When Modeling Header/Line Item Transactions 307
Fact Table Keys and Degenerate Dimensions 309
8.19 Fact Table Surrogate Keys 309
8.20 Reader Suggestions on Fact Table Surrogate Keys 310
8.21 Another Look at Degenerate Dimensions 312
8.22 Creating a Reference Dimension for Infrequently Accessed Degenerates 313
Miscellaneous Fact Table Design Patterns 314
8.23 Put Your Fact Tables on a Diet 314
8.24 Keeping Text Out of the Fact Table 316
8.25 Dealing with Nulls in a Dimensional Model 317
8.26 Modeling Data as Both a Fact and Dimension Attribute 318
8.27 When a Fact Table Can Be Used as a Dimension Table 319
8.28 Sparse Facts and Facts with Short Lifetimes 321
8.29 Pivoting the Fact Table with a Fact Dimension 323
8.30 Accumulating Snapshots for Complex Workflows 324
9 Dimension Table Core Concepts 327
Dimension Table Keys 327
9.1 Surrogate Keys 327
9.2 Keep Your Keys Simple 331
9.3 Durable "Super-Natural" Keys 333
Date and Time Dimension Considerations 334
9.4 It's Time for Time 335
9.5 Surrogate Keys for the Time Dimension 337
9.6 Latest Thinking on Time Dimension Tables 339
9.7 Smart Date Keys to Partition Fact Tables 341
9.8 Updating the Date Dimension 342
9.9 Handling All the Dates 343
Miscellaneous Dimension Patterns 345
9.10 Selecting Default Values for Nulls 345
9.11 Data Warehouse Role Models 347
9.12 Mystery Dimensions 350
9.13 De-Clutter with Junk Dimensions 353
9.14 Showing the Correlation between Dimensions 354
9.15 Causal (Not Casual) Dimensions 356
9.16 Resist Abstract Generic Dimensions 359
9.17 Hot-Swappable Dimensions 360
9.18 Accurate Counting with a Dimensional Supplement 361
Slowly Changing Dimensions 363
9.19 Perfectly Partitioning History with Type 2 SCD 363
9.20 Many Alternate Realities 364
9.21 Monster Dimensions 367
9.22 When a Slowly Changing Dimension Speeds Up 370
9.23 When Do Dimensions Become Dangerous? 372
9.24 Slowly Changing Dimensions are Not Always as Easy as 1, 2, and 3 373
9.25 Slowly Changing Dimension Types 0, 4, 5, 6 and 7 378
9.26 Dimension Row Change Reason Attributes 382
10 More Dimension Patterns and Considerations 385
Snowflakes, Outriggers, and Bridges 385
10.1 Snowflakes, Outriggers, and Bridges 385
10.2 A Trio of Interesting Snowflakes 388
10.3 Help for Dimensional Modeling 392
10.4 Managing Bridge Tables 395
10.5 The Keyword Dimension 399
10.6 Potential Bridge (Table) Detours 403
10.7 Alternatives for Multi-Valued Dimensions 405
10.8 Adding a Mini-Dimension to a Bridge Table 407
Dealing with Hierarchies 409
10.9 Maintaining Dimension...
| Erscheinungsjahr: | 2015 |
|---|---|
| Fachbereich: | Datenkommunikation, Netze & Mailboxen |
| Genre: | Informatik |
| Rubrik: | Naturwissenschaften & Technik |
| Medium: | Taschenbuch |
| Inhalt: | 912 S. |
| ISBN-13: | 9781119216315 |
| ISBN-10: | 1119216311 |
| Sprache: | Englisch |
| Einband: | Kartoniert / Broschiert |
| Autor: |
Kimball, Ralph
Ross, Margy |
| Orchester: | Becker, Bob |
| Auflage: | 2nd edition |
| Hersteller: |
Wiley
John Wiley & Sons |
| Maße: | 235 x 191 x 48 mm |
| Von/Mit: | Ralph Kimball (u. a.) |
| Erscheinungsdatum: | 30.12.2015 |
| Gewicht: | 1,669 kg |
Ralph Kimball, PhD, founded the Kimball Group and is a leading visionary in the data warehousing industry.
Margy Ross, President of the Kimball Group and DecisionWorks Consulting, has focused on DW/BI solutions since 1982.
Introduction xxv
1 The Reader at a Glance 1
Setting Up for Success 1
1.1 Resist the Urge to Start Coding 1
1.2 Set Your Boundaries 4
Tackling DW/BI Design and Development 6
1.3 Data Wrangling 6
1.4 Myth Busters 9
1.5 Dividing the World 10
1.6 Essential Steps for the Integrated Enterprise Data Warehouse 13
1.7 Drill Down to Ask Why 22
1.8 Slowly Changing Dimensions 25
1.9 Judge Your BI Tool through Your Dimensions 28
1.10 Fact Tables 31
1.11 Exploit Your Fact Tables 33
2 Before You Dive In 35
Before Data Warehousing 35
2.1 History Lesson on Ralph Kimball and Xerox PARC 36
Historical Perspective 37
2.2 The Database Market Splits 37
2.3 Bringing Up Supermarts 40
Dealing with Demanding Realities 47
2.4 Brave New Requirements for Data Warehousing 47
2.5 Coping with the Brave New Requirements 52
2.6 Stirring Things Up 57
2.7 Design Constraints and Unavoidable Realities 60
2.8 Two Powerful Ideas 64
2.9 Data Warehouse Dining Experience 67
2.10 Easier Approaches for Harder Problems 70
2.11 Expanding Boundaries of the Data Warehouse 72
3 Project/Program Planning 75
Professional Responsibilities 75
3.1 Professional Boundaries 75
3.2 An Engineer's View 78
3.3 Beware the Objection Removers 82
3.4 What Does the Central Team Do? 86
3.5 Avoid Isolating DW and BI Teams 90
3.6 Better Business Skills for BI and Data Warehouse Professionals 91
3.7 Risky Project Resources are Risky Business 93
3.8 Implementation Analysis Paralysis 95
3.9 Contain DW/BI Scope Creep and Avoid Scope Theft 96
3.10 Are IT Procedures Beneficial to DW/BI Projects? 98
Justification and Sponsorship 100
3.11 Habits of Effective Sponsors 100
3.12 TCO Starts with the End User 103
Kimball Methodology 108
3.13 Kimball Lifecycle in a Nutshell 108
3.14 Off the Bench111
3.15 The Anti-Architect112
3.16 Think Critically When Applying Best Practices 115
3.17 Eight Guidelines for Low Risk Enterprise Data Warehousing 118
4 Requirements Definition 123
Gathering Requirements 123
4.1 Alan Alda's Interviewing Tips for Uncovering Business Requirements 123
4.2 More Business Requirements Gathering Dos and Don'ts 127
4.3 Balancing Requirements and Realities 129
4.4 Overcoming Obstacles When Gathering Business Requirements 130
4.5 Surprising Value of Data Profiling 133
Organizing around Business Processes 134
4.6 Focus on Business Processes, Not Business Departments! 134
4.7 Identifying Business Processes 135
4.8 Business Process Decoder Ring 137
4.9 Relationship between Strategic Business Initiatives and Business Processes 138
Wrapping Up the Requirements 139
4.10 The Bottom-Up Misnomer 140
4.11 Think Dimensionally (Beyond Data Modeling) 144
4.12 Using the Dimensional Model to Validate Business Requirements 145
5 Data Architecture 147
Making the Case for Dimensional Modeling 147
5.1 Is ER Modeling Hazardous to DSS? 147
5.2 A Dimensional Modeling Manifesto 151
5.3 There are No Guarantees 159
Enterprise Data Warehouse Bus Architecture 163
5.4 Divide and Conquer 163
5.5 The Matrix 166
5.6 The Matrix: Revisited 170
5.7 Drill Down into a Detailed Bus Matrix 174
Agile Project Considerations 176
5.8 Relating to Agile Methodologies 176
5.9 Is Agile Enterprise Data Warehousing an Oxymoron? 177
5.10 Going Agile? Start with the Bus Matrix 179
5.11 Conformed Dimensions as the Foundation for Agile Data Warehousing 180
Integration Instead of Centralization 181
5.12 Integration for Real People 181
5.13 Build a Ready-to-Go Resource for Enterprise Dimensions 185
5.14 Data Stewardship 101: The First Step to Quality and Consistency 186
5.15 To Be or Not To Be Centralized 189
Contrast with the Corporate Information Factory 192
5.16 Differences of Opinion 193
5.17 Much Ado about Nothing 198
5.18 Don't Support Business Intelligence with a Normalized EDW 199
5.19 Complementing 3NF EDWs with Dimensional Presentation Areas 201
6 Dimensional Modeling Fundamentals 203
Basics of Dimensional Modeling 203
6.1 Fact Tables and Dimension Tables 203
6.2 Drilling Down, Up, and Across 207
6.3 The Soul of the Data Warehouse, Part One: Drilling Down 210
6.4 The Soul of the Data Warehouse, Part Two: Drilling Across 213
6.5 The Soul of the Data Warehouse, Part Three: Handling Time 216
6.6 Graceful Modifications to Existing Fact and Dimension Tables 219
Dos and Don'ts 220
6.7 Kimball's Ten Essential Rules of Dimensional Modeling 221
6.8 What Not to Do 223
Myths about Dimensional Modeling 226
6.9 Dangerous Preconceptions 226
6.10 Fables and Facts 228
7 Dimensional Modeling Tasks and Responsibilities 233
Design Activities 233
7.1 Letting the Users Sleep 233
7.2 Practical Steps for Designing a Dimensional Model 240
7.3 Staffing the Dimensional Modeling Team 243
7.4 Involve Business Representatives in Dimensional Modeling 244
7.5 Managing Large Dimensional Design Teams 246
7.6 Use a Design Charter to Keep Dimensional Modeling Activities on Track 248
7.7 The Naming Game 249
7.8 What's in a Name? 250
7.9 When is the Dimensional Design Done? 253
Design Review Activities 254
7.10 Design Review Dos and Don'ts 255
7.11 Fistful of Flaws 257
7.12 Rating Your Dimensional Data Warehouse 260
8 Fact Table Core Concepts 267
Granularity 267
8.1 Declaring the Grain 267
8.2 Keep to the Grain in Dimensional Modeling 270
8.3 Warning: Summary Data May Be Hazardous to Your Health 272
8.4 No Detail Too Small 273
Types of Fact Tables 276
8.5 Fundamental Grains 277
8.6 Modeling a Pipeline with an Accumulating Snapshot 280
8.7 Combining Periodic and Accumulating Snapshots 282
8.8 Complementary Fact Table Types 284
8.9 Modeling Time Spans 286
8.10 A Rolling Prediction of the Future, Now and in the Past 289
8.11 Timespan Accumulating Snapshot Fact Tables 293
8.12 Is it a Dimension, a Fact, or Both? 294
8.13 Factless Fact Tables 295
8.14 Factless Fact Tables? Sounds Like Jumbo Shrimp? 298
8.15 What Didn't Happen 299
8.16 Factless Fact Tables for Simplification 302
Parent-Child Fact Tables 304
8.17 Managing Your Parents 304
8.18 Patterns to Avoid When Modeling Header/Line Item Transactions 307
Fact Table Keys and Degenerate Dimensions 309
8.19 Fact Table Surrogate Keys 309
8.20 Reader Suggestions on Fact Table Surrogate Keys 310
8.21 Another Look at Degenerate Dimensions 312
8.22 Creating a Reference Dimension for Infrequently Accessed Degenerates 313
Miscellaneous Fact Table Design Patterns 314
8.23 Put Your Fact Tables on a Diet 314
8.24 Keeping Text Out of the Fact Table 316
8.25 Dealing with Nulls in a Dimensional Model 317
8.26 Modeling Data as Both a Fact and Dimension Attribute 318
8.27 When a Fact Table Can Be Used as a Dimension Table 319
8.28 Sparse Facts and Facts with Short Lifetimes 321
8.29 Pivoting the Fact Table with a Fact Dimension 323
8.30 Accumulating Snapshots for Complex Workflows 324
9 Dimension Table Core Concepts 327
Dimension Table Keys 327
9.1 Surrogate Keys 327
9.2 Keep Your Keys Simple 331
9.3 Durable "Super-Natural" Keys 333
Date and Time Dimension Considerations 334
9.4 It's Time for Time 335
9.5 Surrogate Keys for the Time Dimension 337
9.6 Latest Thinking on Time Dimension Tables 339
9.7 Smart Date Keys to Partition Fact Tables 341
9.8 Updating the Date Dimension 342
9.9 Handling All the Dates 343
Miscellaneous Dimension Patterns 345
9.10 Selecting Default Values for Nulls 345
9.11 Data Warehouse Role Models 347
9.12 Mystery Dimensions 350
9.13 De-Clutter with Junk Dimensions 353
9.14 Showing the Correlation between Dimensions 354
9.15 Causal (Not Casual) Dimensions 356
9.16 Resist Abstract Generic Dimensions 359
9.17 Hot-Swappable Dimensions 360
9.18 Accurate Counting with a Dimensional Supplement 361
Slowly Changing Dimensions 363
9.19 Perfectly Partitioning History with Type 2 SCD 363
9.20 Many Alternate Realities 364
9.21 Monster Dimensions 367
9.22 When a Slowly Changing Dimension Speeds Up 370
9.23 When Do Dimensions Become Dangerous? 372
9.24 Slowly Changing Dimensions are Not Always as Easy as 1, 2, and 3 373
9.25 Slowly Changing Dimension Types 0, 4, 5, 6 and 7 378
9.26 Dimension Row Change Reason Attributes 382
10 More Dimension Patterns and Considerations 385
Snowflakes, Outriggers, and Bridges 385
10.1 Snowflakes, Outriggers, and Bridges 385
10.2 A Trio of Interesting Snowflakes 388
10.3 Help for Dimensional Modeling 392
10.4 Managing Bridge Tables 395
10.5 The Keyword Dimension 399
10.6 Potential Bridge (Table) Detours 403
10.7 Alternatives for Multi-Valued Dimensions 405
10.8 Adding a Mini-Dimension to a Bridge Table 407
Dealing with Hierarchies 409
10.9 Maintaining Dimension...
| Erscheinungsjahr: | 2015 |
|---|---|
| Fachbereich: | Datenkommunikation, Netze & Mailboxen |
| Genre: | Informatik |
| Rubrik: | Naturwissenschaften & Technik |
| Medium: | Taschenbuch |
| Inhalt: | 912 S. |
| ISBN-13: | 9781119216315 |
| ISBN-10: | 1119216311 |
| Sprache: | Englisch |
| Einband: | Kartoniert / Broschiert |
| Autor: |
Kimball, Ralph
Ross, Margy |
| Orchester: | Becker, Bob |
| Auflage: | 2nd edition |
| Hersteller: |
Wiley
John Wiley & Sons |
| Maße: | 235 x 191 x 48 mm |
| Von/Mit: | Ralph Kimball (u. a.) |
| Erscheinungsdatum: | 30.12.2015 |
| Gewicht: | 1,669 kg |
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