Hughes | Agile Data Warehousing for the Enterprise | E-Book | www.sack.de
E-Book

E-Book, Englisch, 562 Seiten, Format (B × H): 191 mm x 235 mm

Hughes Agile Data Warehousing for the Enterprise

A Guide for Solution Architects and Project Leaders
1. Auflage 2015
ISBN: 978-0-12-396518-9
Verlag: Academic Press
Format: EPUB
 Kopierschutz: 6 - ePub Watermark

A Guide for Solution Architects and Project Leaders

E-Book, Englisch, 562 Seiten, Format (B × H): 191 mm x 235 mm

ISBN: 978-0-12-396518-9
Verlag: Academic Press
Format: EPUB
 Kopierschutz: 6 - ePub Watermark

Building upon his earlier book that detailed agile data warehousing programming techniques for the Scrum master, Ralph's latest work illustrates the agile interpretations of the remaining software engineering disciplines: - Requirements management benefits from streamlined templates that not only define projects quickly, but ensure nothing essential is overlooked. - Data engineering receives two new 'hyper modeling' techniques, yielding data warehouses that can be easily adapted when requirements change without having to invest in ruinously expensive data-conversion programs.  - Quality assurance advances with not only a stereoscopic top-down and bottom-up planning method, but also the incorporation of the latest in automated test engines.  Use this step-by-step guide to deepen your own application development skills through self-study, show your teammates the world's fastest and most reliable techniques for creating business intelligence systems, or ensure that the IT department working for you is building your next decision support system the right way. - Learn how to quickly define scope and architecture before programming starts - Includes techniques of process and data engineering that enable iterative and incremental delivery - Demonstrates how to plan and execute quality assurance plans and includes a guide to continuous integration and automated regression testing - Presents program management strategies for coordinating multiple agile data mart projects so that over time an enterprise data warehouse emerges - Use the provided 120-day road map to establish a robust, agile data warehousing program

Ralph Hughes, former DW/BI practice manager for a leading global systems integrator, has led numerous BI programs and projects for Fortune 500 companies in aerospace, government, telecom, and pharmaceuticals. A certified Scrum Master and a PMI Project Management Professional, he began developing an agile method for data warehouse 15 years ago, and was the first to publish books on the iterative solutions for business intelligence projects. He is a veteran trainer with the world's leading data warehouse institute and has instructed or coached over 1,000 BI professionals worldwide in the discipline of incremental delivery of large data management systems. A frequent keynote speaker at business intelligence and data management events, he serves as a judge on emerging technologies award panels and program advisory committees of advanced technology conferences. He holds BA and MA degrees from Stanford University where he studied computer modeling and econometric forecasting. A co-inventor of Zuzena, the automated testing engine for data warehouses, he serves as Chief Systems Architect for Ceregenics and consults on agile projects internationally.
Hughes Agile Data Warehousing for the Enterprise jetzt bestellen!

Zielgruppe

<p>data warehousing professionals including architects, designers, data modelers, testers, database administrators, and project managers as well as IT managers, directors, and VPs </p>

Autoren/Hrsg.

Hughes, Ralph

Fachgebiete

Weitere Infos & Material

1- Agile Versus Enterprise Data Warehousing (EDW)

2- Agile Requirements Management Framework

3- Agile Architectural Framework

4- Agile Data Engineering Framework

5- Agile Quality Assurance Framework

6- Agile Program Management Framework

7- Managing Frameworks via Enterprise BI Architecture Groups

List of Figures


Figure 1.1 The negative feedback loop present in most traditionally managed projects. 2
Figure 1.2 The five major components to agile enterprise data warehousing. 4
Figure 1.3 Agile EDW practices switch projects to a positive feedback loop. 4
Figure 1.4 How a team might acquire agile EDW techniques working from the inside out. 8
Figure 2.1 Mind map of generic iterative methods summarized in Chapters 2 and 3. 14
Figure 2.2 A family tree of methods and influences leading to the agile EDW method. 15
Figure 2.3 The traditional waterfall method. 17
Figure 2.4 The Agile manifesto cover page. 17
Figure 2.5 Values and principles of the agile manifesto and Extreme Programming. 18
Figure 2.6 The essence of the Scrum method. 21
Figure 2.7 Typical user story. 22
Figure 2.8 A sample Scrum task board as it would appear in mid-iteration. 24
Figure 2.9 A Scrum burndown chart as it would appear in mid-iteration. 25
Figure 3.1 Lean values, principles, and tools. 33
Figure 3.2 Value-stream analysis of development work for a challenged waterfall project. 34
Figure 3.3 Typical Kanban work board. 42
Figure 3.4 Kanban-style cumulative flow diagram. 43
Figure 3.5 Sample cycle time distribution analysis for a Kanban team. 44
Figure 3.6 Typical stages of “Scrumban”—the transition from Scrum to Kanban. 48
Figure 3.7 Two-tiered Scrumban task board. 48
Figure 3.8 Values and principles of the Rational Unified Process. 50
Figure 3.9 RUP Whale Chart. 51
Figure 3.10 Google Ngram of “Scrum” and “RUP” through 2008. 53
Figure 4.1 Business organizational terms used in this book. 64
Figure 4.2 Business conceptual model. 72
Figure 4.3 Logical data model. 72
Figure 4.4 Physical data model. 73
Figure 4.5 Sample enterprise data warehouse “reference architecture”. 75
Figure 4.6 Zachman framework adapted for an enterprise data warehousing program. 77
Figure 4.7 DAMA’s framework for data management functions. 78
Figure 4.8 Hammergren’s matrix for sequencing DW/BI development work. 79
Figure 5.1 Typical RUP-style whale chart for an agile EDW project. 91
Figure 5.2 Agile EDW user stories result in too many developer stories for one, short Iteration. 92
Figure 5.3 Deriving developer stories from user stories. 94
Figure 5.4 A “current estimate” for an agile data warehousing project. 96
Figure 5.5 Agile data warehousing requires pipelined work specialties. 99
Figure 5.6 Work packages tend to flow diagonally across technical specialties and iterations. 101
Figure 5.7 Cycle time distribution analysis for an agile data warehousing project. 103
Figure 5.8 A current estimate adjusted for observed delivery cycle times. 104
Figure 5.9 Success rates for agile data warehousing teams, by number of agile projects completed, compared to traditional methods. 105
Figure 5.10 Agile’s impact upon key performance indicators for data warehousing development projects. 105
Figure 5.11 Agile data warehousing surveys indicate that practitioners have overcome some challenge areas. 107
Figure 6.1 Relative cost of correcting defects grows by 100 between requirements and promotion into production. 112
Figure 6.2 Incremental delivery mitigates risk by increasing the number of product check points. 113
Figure 6.3 The sources of EDW project risk mitigated with three types of iterations. 115
Figure 6.4 Relative timing for the three types of iterations that Agile EDW employs. 118
Figure 7.1 Mind map of topics addressed in Part III. 126
Figure 7.2 Sample EDW requirements expressed at three levels. 127
Figure 7.3 Waterfall-style requirements management. 131
Figure 7.4 Typical requirements work breakdown for a traditional project. 133
Figure 7.5 As-is business process diagram showing a sample work flow requiring re-engineering. 135
Figure 7.6 To-be business process re-engineered to use EDW to communicate between agents. 136
Figure 7.7 Accuracy vs. precision. 137
Figure 7.8 Standard risk analysis. 140
Figure 7.9 Standard analysis adjusted for dollar value of each type of risk. 141
Figure 7.10 Agile EDW’s requirements management benefits greatly from intersecting value chains. 146
Figure 7.11 Overall agile EDW requirements management plan. 148
Figure 7.12 Enterprise requirements management roles. 150
Figure 8.1 Big picture – decomposing epics into a backlog of stories. 152
Figure 8.2 Immediate business stakeholder formalizing all levels of stories by linking them to the hierarchy among business stakeholders. 153
Figure 8.3 Primary technique for decomposing user stories into developers stories. Note the 25-to-1 multiplier for this project’s user story. 158
Figure 8.4 INVEST and DILBERT’S test. 158
Figure 8.5 Big picture – recompling modules for perceived value. 160
Figure 8.6 Value build-up charts distingishing between delivery environments. 166
Figure 9.1 User modeling example. 171
Figure 9.2 Business...

Hughes, Ralph
Ralph Hughes, former DW/BI practice manager for a leading global systems integrator, has led numerous BI programs and projects for Fortune 500 companies in aerospace, government, telecom, and pharmaceuticals. A certified Scrum Master and a PMI Project Management Professional, he began developing an agile method for data warehouse 15 years ago, and was the first to publish books on the iterative solutions for business intelligence projects. He is a veteran trainer with the world's leading data warehouse institute and has instructed or coached over 1,000 BI professionals worldwide in the discipline of incremental delivery of large data management systems.

A frequent keynote speaker at business intelligence and data management events, he serves as a judge on emerging technologies award panels and program advisory committees of advanced technology conferences. He holds BA and MA degrees from Stanford University where he studied computer modeling and econometric forecasting. A co-inventor of Zuzena, the automated testing engine for data warehouses, he serves as Chief Systems Architect for Ceregenics and consults on agile projects internationally.

Ihre Fragen, Wünsche oder Anmerkungen
Vorname*
Nachname*
Ihre E-Mail-Adresse*
Kundennr.
Ihre Nachricht*
Lediglich mit * gekennzeichnete Felder sind Pflichtfelder.
Wenn Sie die im Kontaktformular eingegebenen Daten durch Klick auf den nachfolgenden Button übersenden, erklären Sie sich damit einverstanden, dass wir Ihr Angaben für die Beantwortung Ihrer Anfrage verwenden. Selbstverständlich werden Ihre Daten vertraulich behandelt und nicht an Dritte weitergegeben. Sie können der Verwendung Ihrer Daten jederzeit widersprechen. Das Datenhandling bei Sack Fachmedien erklären wir Ihnen in unserer Datenschutzerklärung.