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Rating: 
Summary: Author's description of the book
Review: Dennis and I wrote this book to primarily address the technical issues software engineers experience in building an enterprise data warehouse. This is what we do for a living. The focus is on providing a large number of specific technical techniques to address common design, implementation, and operational management issues. Each technique is then structured around a step by step guide that describes which technique to apply when, and what each deliverable should look like. The book is very heavily weighted towards technical rather than project management. Specifically; the breakdown of the book is: Introduction: 5 pages
Design and Build 130 pages (Technical Architecture, Database Design, Hardware & Software Partitioning, Database Aggregations, Data Marting, Meta Data Design, System Process Managers)
Hardware & Operational Design 83 pages (hardware archtecture, physical layout [parallel technology], database security, backup and recovery, SLA's, operational support)
Capacity Planning, Tuning and Testing 34 pages
Methodology & Project Management 46 pages (delivery process, tasks and deliverables, sizing, project plans)
This is the first book that documents all Tasks & Deliverables (including sizing and project plans), for delivering all the business and technical components of a data warehouse. However, it is self evident that this forms a small percentage of the total content (i.e. under 20%). This book also introduces a significant number of design techniques which (to the best of my knowledge) are not documented in any other publication. An example of this is the Starflake Schema approach, which is a new variant of the Star and Snowflake Schema approaches. For the record, this variant was created over a period of time by Dennis and I, after experiencing significant difficulties in implementing the other techniques on a performant basis (using Oracle with IBM/Sequent/Siemens/Hewlett Packard/Digital hardware architectures). In addition, the bulk of the information in the book (prior to publication) formed the basis for a series of very successful training programmes run in MCI Systemhouse for our own consultants. These folks are implementing data warehouses on a day to day basis. The feedback was and is unequivocally positive, with all the suggestions for additional information/elaboration being included in the finished manuscript. The Data Warehousing Information Center recommends this book as one of top four to be read. "With due respect to all the other fine books on data warehousing and decision support, when read in combination I believe these four books provide a great introduction to and overview of the strategic and tactical issues system developers face. Especially valuable are Inmon's overall overview, Kimball's description of data modeling and query/report tools, Devlin's descriptions of data extraction, cleaning, and loading issues and metadata, and Anahory/Murray's description of what can be done so a system can run efficiently and their description of the main tasks in a data warehouse project." All in all, I encourage all prospective readers to make up your own minds when you read this book.
Sam Anahory Global Director Business Intelligence Practice MCI Systemhouse
Rating: 
Summary: Good and Practical
Review: Purchased Kimbal's book and this one. Found Data Warehousing in the Real World to be good at, not only demonstrating the conceptual pieces of a data warehouse, but also the integral technical and developmental aspects of data warehouse design. Kimbal's book drones on and on about this and that (business) senario, but is only useful if you are modeling that specific senario (and doesn't explain it very well either). Data Warehousing in the Real World goes way beyond modeling and talks about ETL, partitioning, aggregates and other issues in great detail. Recommend it.
Rating:
Summary: Good and Practical
Review: Purchased Kimbal's book and this one. Found Data Warehousing in the Real World to be good at, not only demonstrating the conceptual pieces of a data warehouse, but also the integral technical and developmental aspects of data warehouse design. Kimbal's book drones on and on about this and that (business) senario, but is only useful if you are modeling that specific senario (and doesn't explain it very well either). Data Warehousing in the Real World goes way beyond modeling and talks about ETL, partitioning, aggregates and other issues in great detail. Recommend it.
Rating:
Summary: Good starting for project managers
Review: The book covers all aspects of the DWH from extraction to designing star scheme (could have more detailled example -> see ralph kimballs book) to data marting. Very useful for newcomers (like myself) to set up a project and know what to do and what not to do. Estimates are nice, but be careful about them...
Rating:
Summary: Good starting for project managers
Review: The book covers all aspects of the DWH from extraction to designing star scheme (could have more detailled example -> see ralph kimballs book) to data marting. Very useful for newcomers (like myself) to set up a project and know what to do and what not to do. Estimates are nice, but be careful about them...
Rating: 
Summary: For managers only.
Review: This book is useful only to complete newcomers to data warehousing, particularly managers, although I consider Rob Mattison's or Vidette Poe's books to be better for the latter group. There is little substantive for the hands-on practitioner who has to build a real data warehouse. In fact, this book has very little to say about data warehouses at all - most of the book is generic project management stuff. If you need hard-core information about the design of a real data warehouse, try Ralph Kimball's or Erik Thomsen's books
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