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Agile Data Warehouse Design: Collaborative Dimensional Modeling, from Whiteboard to Star Schema (Anglais) Broché – 24 novembre 2011
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Description du produit
Présentation de l'éditeur
Agile Data Warehouse Design is a step-by-step guide for capturing data warehousing / business intelligence (DW/BI) requirements and turning them into high performance dimensional models in the most direct way: by modelstorming (data modeling + brainstorming) with BI stakeholders.
The book describes BEAM, an agile approach to dimensional modeling, for improving communication between data warehouse designers, BI stakeholders and the whole DW/BI development team. BEAM provides tools and techniques that will encourage DW/BI designers and developers to move away from their keyboards and entity relationship based tools and model interactively with their colleagues. The result is everyone thinks dimensionally from the outset! Developers understand how to efficiently implement dimensional modeling solutions. Business stakeholders feel ownership of the data warehouse they have created, and can already imagine how they will use it to answer their business questions.
Within this book, you will learn:
- Agile dimensional modeling using Business Event Analysis & Modeling (BEAM)
- Modelstorming: data modeling that is quicker, more inclusive, more productive, and frankly more fun!
- Telling dimensional data stories using the 7Ws (who, what, when, where, how many, why and how)
- Modeling by example not abstraction; using data story themes, not crow’s feet, to describe detail
- Storyboarding the data warehouse to discover conformed dimensions and plan iterative development
- Visual modeling: sketching timelines, charts and grids to model complex process measurement – simply
- Agile design documentation: enhancing star schemas with BEAM dimensional shorthand notation
- Solving difficult DW/BI performance and usability problems with proven dimensional design patterns
Biographie de l'auteur
Lawrence Corr is a data warehouse designer and educator. As Principal of DecisionOne Consulting, he helps clients to review and simplify their data warehouse designs, and advises vendors on visual data modeling techniques. He regularly teaches agile dimensional modeling courses worldwide and has taught dimensional DW/BI skills to thousands of students.
Jim Stagnitto is a data warehouse and master data management architect specializing in the healthcare, financial services, and information service industries. He is the founder of the data warehousing and data mining consulting firm Llumino.
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Commentaires client les plus utiles sur Amazon.com
Unlike some other technical texts, the authors really do walk the reader through the material one step at a time, with no skipping around between topics that many readers complain about elsewhere. Other than the step-by-step aspect of this book, another reason I found this book so well put together are the abundant, well-placed diagrams throughout that greatly add to the material. Although what initially got me interested in this text is the agile process surrounding data warehousing, the chapters on dimensional design patterns for performance, flexibility, and usability compliment well the book that I currently consider the best on the traditional data warehouse star schema, "Star Schema: The Complete Reference" by Christopher Adamson (see my review).
In the chapter covering "who" and "what" design patterns, for example, the authors cover customer dimensions (particularly challenging because of their typical depth, width, and volatility) alongside the mini-dimension pattern, the sensible snowflaking pattern, swappable dimension patterns, customer relationships, and hierarchy maps, as well as employee dimensions alongside the hybrid slowly changing dimension (SCD) view pattern, the previous value attribute pattern, and the multi-valued hierarchy map pattern, followed by product and service dimensions alongside the multi-level dimension pattern and the parts explosion hierarchy map pattern. And the chapter covering "how" design patterns provides one of the best concise explanations of fact table types and fact types.
The one potential issue with this book is that although its goal is agile data warehouse design, getting familiar with the BEAM (business event analysis and modeling) notation used throughout the author discussions might be a bit time prohibitive for teams comprised of more than a few individuals to become accustomed when using for the first time. While the second appendix at the back of the book provides effective short codes for event story and fact table types, dimension table types, general column codes, data types, key types, dimensional attribute types, and event detail and fact column types, some upfront initial team investment will almost certainly be involved for which an agile spike will be needed, and close attention to workability with stakeholders should be assessed. Well recommended overall.
Don't forget to also register at the author's website for very useful tools and templates.
The BEAM* methodology alone was worth the "price of admission". It provides a manner to solicit and capture requirements (using Agile approaches of course) that makes is easier for stakeholders (people who will run and request reports) to articulate requirements. And then, a manner to translate them into Dimensional Models.