Rating: 
Summary: The best since Kimball's
Review: A serious textbook about Data warehouse real world issues. It provides material not covered by other textbook, the laterial about meta data in particular. A must read
Rating:
Summary: Good coverages of basics - for managers and non DBAs
Review: Data Warehousing covers a lot of territory, but does not go into depth. If you know this in advance it sets your expectations that this book is more of an educational tool for managers than a "how-to" for data architects and DBAs. I recommend The Data Warehouse Lifecycle Toolkit by Ralph Kimball for those who are seeking an in-depth technical treatment of the subject. This book will give you a solid foundation of the basics, expose the issues and provide a high-level process for planning and implementing a data warehouse. It is divided into sections, the first three covering people, process and technology. Section One starts with an overview enterprise IT architectures, how data warehousing fits into the scheme of things, and associated business and technical perspectives. I like the way the authors emphasize business perspectives, which is a consistent thread throughout the book. They use a framework called "InfoMotion", which covers all of the requirements, but (to me) is too wrapped-up in "consultant-speak". For example, they litter this section with nonsense such as "InfoMotion = Information/Data * motion. While it makes perfect sense from a conceptual viewpoint, there is no way to compute it, so why express it as a formula? Parenthetically, data is easy to quantify; measuring information is difficult, but can be done. The motion part of the equation is plain silliness because there is no basis given for measurement. But I am nitpicking here. You are next introduced to data warehouse concepts. This gives a foundation that is complete and covers all key elements, such as reports, definitions of data warehouse and data mart and operational data stores. I thought this was an excellent introduction. Also included is a brief piece on cost/benefit and return on investment. It was short and hit all of the key points, but would have fit better in the prior discussion of the business perspective. The next section addresses the people part of a data warehousing project, begining with the project sponsor. Answers to some incisive questions are given in this part, such as "how will the data warehouse affect decision-making processes?", "how will it improve financial, marketing and operations processes?" and similar business-focused questions. These draw your attention to the real reasons for data warehousing. This section moves naturally into project management considerations, and exposes some common problems like defining project scope, underestimating time and project overhead or factoring the operational support issues after the data warehouse is rolled out and in production. One of the best parts of this section is how the authors counter common problems and risks with advice on how to eliminate or mitigate them. I liked the approach to measuring results, which gives some sound key performance indications that you can use to baseline some total cost of ownership drivers after the data warehouse is in production. This section continues with roles and responsibilities of the project team. The authors have crafted a sound team structure that consists of business and technical representatives who are overseen by a steering committee. This is an excellent approach. I thought the inclusion of users from various business domains was one of the key strengths, because these people know the data's value to the business a lot better than the technical side of the team. On the other hand, I thought it was naive of the authors to state that this group would be required 80% of the time during the project. While I fully agree with this estimate, it is nearly impossible in practice. I wish the authors would have shared how they sold the business side on making an 80% commitment of their best and brightest. As this section moves into the actual project there are some things I loved about their approach: breaking the project into four parallel tracks and the proposed rollout strategy. These give you a good understanding of the scope and magnitude of a typical data warehouse project. Section 4 covers technology, and gets a little too technical for a business user in some places, but is just right for an IT manager who is not a DBA or data architect. I liked the discussion of metadata, why normalization is not appropriate for data warehousing, and the treatment of fact and dimension tables. The final section discusses maintenance requirements once the data warehouse is in production. This prepares you for the realities of managing these systems. I wish the authors would have addressed some of the workload and scheduling issues that are a part of the territory - refreshing the warehouse is going to require a fine balancing act that is going to affect maintenance windows, other production jobs and a plethora of other production headaches if not planned for in advance. Overall this is a good book for the audience I cited above. I strongly recommend anyone considering a data warehouse to also read Improving Data Warehouse and Business Information Quality by Larry P. English.
Rating:
Summary: Good coverages of basics - for managers and non DBAs
Review: Data Warehousing covers a lot of territory, but does not go into depth. If you know this in advance it sets your expectations that this book is more of an educational tool for managers than a "how-to" for data architects and DBAs. I recommend The Data Warehouse Lifecycle Toolkit by Ralph Kimball for those who are seeking an in-depth technical treatment of the subject. This book will give you a solid foundation of the basics, expose the issues and provide a high-level process for planning and implementing a data warehouse. It is divided into sections, the first three covering people, process and technology. Section One starts with an overview enterprise IT architectures, how data warehousing fits into the scheme of things, and associated business and technical perspectives. I like the way the authors emphasize business perspectives, which is a consistent thread throughout the book. They use a framework called "InfoMotion", which covers all of the requirements, but (to me) is too wrapped-up in "consultant-speak". For example, they litter this section with nonsense such as "InfoMotion = Information/Data * motion. While it makes perfect sense from a conceptual viewpoint, there is no way to compute it, so why express it as a formula? Parenthetically, data is easy to quantify; measuring information is difficult, but can be done. The motion part of the equation is plain silliness because there is no basis given for measurement. But I am nitpicking here. You are next introduced to data warehouse concepts. This gives a foundation that is complete and covers all key elements, such as reports, definitions of data warehouse and data mart and operational data stores. I thought this was an excellent introduction. Also included is a brief piece on cost/benefit and return on investment. It was short and hit all of the key points, but would have fit better in the prior discussion of the business perspective. The next section addresses the people part of a data warehousing project, begining with the project sponsor. Answers to some incisive questions are given in this part, such as "how will the data warehouse affect decision-making processes?", "how will it improve financial, marketing and operations processes?" and similar business-focused questions. These draw your attention to the real reasons for data warehousing. This section moves naturally into project management considerations, and exposes some common problems like defining project scope, underestimating time and project overhead or factoring the operational support issues after the data warehouse is rolled out and in production. One of the best parts of this section is how the authors counter common problems and risks with advice on how to eliminate or mitigate them. I liked the approach to measuring results, which gives some sound key performance indications that you can use to baseline some total cost of ownership drivers after the data warehouse is in production. This section continues with roles and responsibilities of the project team. The authors have crafted a sound team structure that consists of business and technical representatives who are overseen by a steering committee. This is an excellent approach. I thought the inclusion of users from various business domains was one of the key strengths, because these people know the data's value to the business a lot better than the technical side of the team. On the other hand, I thought it was naive of the authors to state that this group would be required 80% of the time during the project. While I fully agree with this estimate, it is nearly impossible in practice. I wish the authors would have shared how they sold the business side on making an 80% commitment of their best and brightest. As this section moves into the actual project there are some things I loved about their approach: breaking the project into four parallel tracks and the proposed rollout strategy. These give you a good understanding of the scope and magnitude of a typical data warehouse project. Section 4 covers technology, and gets a little too technical for a business user in some places, but is just right for an IT manager who is not a DBA or data architect. I liked the discussion of metadata, why normalization is not appropriate for data warehousing, and the treatment of fact and dimension tables. The final section discusses maintenance requirements once the data warehouse is in production. This prepares you for the realities of managing these systems. I wish the authors would have addressed some of the workload and scheduling issues that are a part of the territory - refreshing the warehouse is going to require a fine balancing act that is going to affect maintenance windows, other production jobs and a plethora of other production headaches if not planned for in advance. Overall this is a good book for the audience I cited above. I strongly recommend anyone considering a data warehouse to also read Improving Data Warehouse and Business Information Quality by Larry P. English.
Rating:
Summary: Good coverages of basics - for managers and non DBAs
Review: Data Warehousing covers a lot of territory, but does not go into depth. If you know this in advance it sets your expectations that this book is more of an educational tool for managers than a "how-to" for data architects and DBAs. I recommend The Data Warehouse Lifecycle Toolkit by Ralph Kimball for those who are seeking an in-depth technical treatment of the subject. This book will give you a solid foundation of the basics, expose the issues and provide a high-level process for planning and implementing a data warehouse. It is divided into sections, the first three covering people, process and technology. Section One starts with an overview enterprise IT architectures, how data warehousing fits into the scheme of things, and associated business and technical perspectives. I like the way the authors emphasize business perspectives, which is a consistent thread throughout the book. They use a framework called "InfoMotion", which covers all of the requirements, but (to me) is too wrapped-up in "consultant-speak". For example, they litter this section with nonsense such as "InfoMotion = Information/Data * motion. While it makes perfect sense from a conceptual viewpoint, there is no way to compute it, so why express it as a formula? Parenthetically, data is easy to quantify; measuring information is difficult, but can be done. The motion part of the equation is plain silliness because there is no basis given for measurement. But I am nitpicking here. You are next introduced to data warehouse concepts. This gives a foundation that is complete and covers all key elements, such as reports, definitions of data warehouse and data mart and operational data stores. I thought this was an excellent introduction. Also included is a brief piece on cost/benefit and return on investment. It was short and hit all of the key points, but would have fit better in the prior discussion of the business perspective. The next section addresses the people part of a data warehousing project, begining with the project sponsor. Answers to some incisive questions are given in this part, such as "how will the data warehouse affect decision-making processes?", "how will it improve financial, marketing and operations processes?" and similar business-focused questions. These draw your attention to the real reasons for data warehousing. This section moves naturally into project management considerations, and exposes some common problems like defining project scope, underestimating time and project overhead or factoring the operational support issues after the data warehouse is rolled out and in production. One of the best parts of this section is how the authors counter common problems and risks with advice on how to eliminate or mitigate them. I liked the approach to measuring results, which gives some sound key performance indications that you can use to baseline some total cost of ownership drivers after the data warehouse is in production. This section continues with roles and responsibilities of the project team. The authors have crafted a sound team structure that consists of business and technical representatives who are overseen by a steering committee. This is an excellent approach. I thought the inclusion of users from various business domains was one of the key strengths, because these people know the data's value to the business a lot better than the technical side of the team. On the other hand, I thought it was naive of the authors to state that this group would be required 80% of the time during the project. While I fully agree with this estimate, it is nearly impossible in practice. I wish the authors would have shared how they sold the business side on making an 80% commitment of their best and brightest. As this section moves into the actual project there are some things I loved about their approach: breaking the project into four parallel tracks and the proposed rollout strategy. These give you a good understanding of the scope and magnitude of a typical data warehouse project. Section 4 covers technology, and gets a little too technical for a business user in some places, but is just right for an IT manager who is not a DBA or data architect. I liked the discussion of metadata, why normalization is not appropriate for data warehousing, and the treatment of fact and dimension tables. The final section discusses maintenance requirements once the data warehouse is in production. This prepares you for the realities of managing these systems. I wish the authors would have addressed some of the workload and scheduling issues that are a part of the territory - refreshing the warehouse is going to require a fine balancing act that is going to affect maintenance windows, other production jobs and a plethora of other production headaches if not planned for in advance. Overall this is a good book for the audience I cited above. I strongly recommend anyone considering a data warehouse to also read Improving Data Warehouse and Business Information Quality by Larry P. English.
Rating: 
Summary: Solid Overview Reference for Project Managers & Analysts
Review: Dispite the 300+ pages, I was able to finish this book in a day. It provides clear and concise information on how to manage a data warehousing project, caveats & pitfalls, and differences between DW technologies & strategies. The first half of the book focuses on step-by-step DW project management and methodology. The second half focuses on technologies and concepts. However, I felt that the second half of the book was not as strong as the first (probably because technology changes constantly). If you want a book that gives you a step-by-step task list that can be easily transferred to a MS Project plan, then this is the book you want!
Rating:
Summary: Solid Overview Reference for Project Managers & Analysts
Review: Dispite the 300+ pages, I was able to finish this book in a day. It provides clear and concise information on how to manage a data warehousing project, caveats & pitfalls, and differences between DW technologies & strategies. The first half of the book focuses on step-by-step DW project management and methodology. The second half focuses on technologies and concepts. However, I felt that the second half of the book was not as strong as the first (probably because technology changes constantly). If you want a book that gives you a step-by-step task list that can be easily transferred to a MS Project plan, then this is the book you want!
Rating:
Summary: Solid Overview Reference for Project Managers & Analysts
Review: Dispite the 300+ pages, I was able to finish this book in a day. It provides clear and concise information on how to manage a data warehousing project, caveats & pitfalls, and differences between DW technologies & strategies. The first half of the book focuses on step-by-step DW project management and methodology. The second half focuses on technologies and concepts. However, I felt that the second half of the book was not as strong as the first (probably because technology changes constantly). If you want a book that gives you a step-by-step task list that can be easily transferred to a MS Project plan, then this is the book you want!
Rating:
Summary: Its a must for project managers
Review: Excellent book that explains all the steps necessary to implement successful warehouse project. It approaches it from organizational point of view as oppossed to technical. It will complement many technical warehousing books out there.
Rating: 
Summary: A Note on the CD-ROM
Review: Greetings! To anyone thinking of buying this book, I'd like to add that it comes with a CD-ROM that has trial versions of two data warehousing software products -- a warehouse front-end tool called R/olapXL, and a data warehouse back-end tool called Warehouse Designer. R/olapXL is a Relational Online Analytical Processing (ROLAP) tool that runs on top of Microsoft Excel for Windows 95. It allows business users to draw data directly into Excel spreadsheets from any dimensional data warehouse or data mart that resides on an ODBC-compliant database. Users interact through a GUI interface and do not need to know SQL. The trial version comes with a sample database that users can play with to get a feel of R/olapXL's functionality. Warehouse Designer is intended for Warehouse Administrators. It allows DBAs to define the structure of the warehouse on a GUI front-end, then generates the appropriate scripts to create the warehouse database schema. (Note: the script generation facility is disabled in this trial version.) Both tools run on Windows 95, and are tightly integrated. Warehouse Designer produces the metadata that R/olapXL needs to properly access a data warehouse or data mart. We'd love to hear from people who've read the book, or have tried using the tools. Do drop us a note through info@intranetsys.com!
Rating:
Summary: Great introduction for technical and non-technical readers
Review: This is a good introduction to data warehousing for business process owners, project managers and service delivery and support professionals. Like all books in the Enterprise Computing series this one follows the people-process-technology pattern, with a focus on business value. The authors start by showing how data warehouses fit into the context of IT architecture, and how this relates to fulfilling business needs. This is followed by a clearly presented section on concepts that will be easily understood by non-technical readers, especially business process owner who are exploring the benefits and advantages of data warehousing. Scope and complexity of designing, implementing and deploying a data warehouse are discussed in detail in Section II, starting with some excellent material for developing a business case and determining the cost/benefit ratio of a data warehouse initiative. Information in this section is also useful for planning a data warehouse project because it provides low-level details on roles and responsibilities. A key point here is the way the project is structured with both technical and business resources. I like this approach because it involves all of the major stakeholders and IT customers from the beginning instead of the more common practice of waiting until the last minute to involve the business. This approach will go a long way towards making a data warehouse project a success and ensuring that the business gets what it really needs instead of what IT thinks the business needs. The technology section of this book is an excellent description of data structures, meta data and topics that need to be understood in view of the large difference between a data warehouse and an online transaction processing system. I learned a lot from this section and appreciated the way the information was clearly presented. I also liked the fact that the authors included a section on production and maintenance. Other books stop short of this important milestone in a development life cycle, which leaves a lot of unaccounted for issues. This section completes the total picture of a data warehousing initiative and sets realistic expectations for the true costs, resources and effort required to implement and maintain a data warehouse throughout its entire life cycle. This is a nicely done book that is accessible to both technical and non-technical readers, and is one of the best resources with which to get up-to-speed on data warehousing without getting bogged down with too many technical details.
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