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Agile Data Warehousing for the Enterprise: A Guide for Solution Architects and Project Leaders, by Ralph Hughes
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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
- Sales Rank: #478870 in eBooks
- Published on: 2015-09-19
- Released on: 2015-09-19
- Format: Kindle eBook
Review
"Agile Data Warehousing, as outlined in Ralph’s multiple books, has transformed the way our BI team manages projects.� In just two years, we are delivering releases in iterations three times more frequently on five times the number of concurrent projects, while improving application quality and the enterprise nature of our solutions." --Mark Giesbrecht, Senior Manager, Business Intelligence, major Canadian railroad
"Agile Data Warehouse for the Enterprise is a must read for any data professional tasked with delivering enterprise reporting and analytics in the nimble, speed-to-value environment that we find ourselves today.� It marries agile methodology with data warehousing best practices to create a blueprint to deliver value fast." --Nik Green, Director of Business Intelligence for multi-billion dollar food retailer
"This comprehensive guide provides a solid and complete foundation for agile EDW development including revolutionary new paradigms.� I especially like the ‘out of the box’ thinking, practicality of the techniques, and the research and case studies backing up the validity of the proposed approaches.� Recommend reading for all DW/BI technical leaders." --Len Silverston, CEO of Universal Data Models and author of the Data Model Resource Book series
"If you’re still programming data warehouses by hand, you're wasting 90 percent of your time and money.� Ralph’s guide for project leaders not only outlines the automated, metadata-driven development we've been practicing for years in the Netherlands, but also links that practice to agile requirements, coding, and quality assurance.� It is a unique book, a must read." --Ronald Damhof, DW/BI Consultant to the Dutch Central Bank
"The industry has struggled to bring the mechanics and benefits of Agile to the data warehousing and business intelligence communities. Ralph’s works in this area is timely and important to assist in driving success within your enterprise and project teams. Following the guidance found in this book will help you deliver value at a rapid pace." --Tom Hammergren, President of Balanced Insight and author of Data Warehousing: Building the Corporate Knowledge Base
"Ralph has customized the agile development methodology for the unique needs of business intelligence.� My project teams were able to quickly understand and apply his approach without losing time to adapting generic Scrum techniques to the rigors of data integration and analytics." --Ron Lewis, program manager at a Fortune 1000 financial services company
"As a friend of Ralph and fellow Agile DW/BI author, I am very excited about this book. Ralph’s extensive research into what works; his hands-on application of those techniques; and his well-organized method of presenting information, are combined in this game changing book for data warehouse practitioners seeking greater agility." --Ken Collier PhD, author of Agile Analytics, Addison-Wesley, 2011
"This book presents a thorough approach to building quality into enterprise data warehouses, mitigating risk by applying proven models for testing, and detailing alternatives so that you can experiment to find the alternatives that work best for your team. These aren’t� mere off the cuff ideas, but proven techniques to overcoming the huge challenges of EDW by applying agile principles, illustrated with stories from the trenches." --Lisa Crispin, Co-Author of More Agile Testing: Learning Journeys for the Whole Team, and Agile Testing: A Practical Guide for Testers and Agile Teams
"“Ralph’s notion of ‘80/20 specifications’ for data warehousing projects really worked, saving our business partners the pain of doing an exhaustive requirements specification up-front.� This approach got the team developing the most important features first, letting the product owner fill in details on topics as each one came up during development." --Naveen Thalanki, Project Manager for a Fortune 500 company
"Agile Data Warehousing for the Enterprise is a “how to” book with innovative method and process components such as hyper data modeling and an iterative sub-release value cycle.� Ralph provides a clear outline of the concepts, methods, and frameworks you'll need to assemble a world-class BI/DW program of your own." --Hans Hultgren, author of Modeling the Agile Data Warehouse with Data Vault and BI/DW industry advisor
"One of TDWI’s most popular faculty members, Ralph packs this book with detailed direction and experience-driven insights about how to overcome the most challenging aspects of agile for business intelligence and data warehousing applications.� He has become an important thought leader for organizations seeking to increase the agility, quality, and ROI of their BI/DW programs." --David Stodder, Director of Research for Business Intelligence, The Data Warehousing Institute
"Ralph's book goes way beyond just agile programming―it illustrates an iterative approach to the full development life cycle and is particularly relevant to issues of data quality that we focus on at DAMA.� The hyper modeling techniques in particular will allow teams to avoid the death trap of producing big, risky application designs up-front before a project's requirements are fully known." -- Ken Dunn, President of DAMA’s Houston chapter
"Agile techniques and Ralph’s adaptations for enterprise data warehousing have dramatically improved our ability to understand business needs and to then plan, track, and deliver upon those needs for large and small projects. This book provides a thorough treatment of the vital leadership practices for agile projects including requirements, architecture, and quality assurance." -- Richard Tench, Manager, Information Delivery, major Canadian insurance company
From the Back Cover
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.
About the Author
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.
Most helpful customer reviews
1 of 1 people found the following review helpful.
For Leaders: Adapt and Endure
By Daniel C. Upton
The book is exceptionally well named and, as always from this author, well written. From the always-important perspective of forward-thinking data project leadership, it closes the loop on agile data warehousing at enterprise scale. Books from others have introduced agile dimensional modeling, mapped agile terminology from application development to business intelligence, or defined new, agile enterprise data warehouse architecture in detail. However, this book, which is targeted to project leaders, describes, with sufficient detail for the target reader, how the most challenging, most agile-resistant pieces of data warehousing -- the data structure itself (data model), ETL coding, testing, and change management – are logically broken down into agile-sized, requirements-scoped chunks of business value while completely avoiding data silos, sustaining an enterprise-wide perspective, and as a big bonus, maximizing adaptability to future change and easy enhancement. Friend, you are not dreaming. Following this method, the scope-creep that inevitably followed traditional best practices, from agile requirements to the very non-agile big data-model design up front, becomes a thing of the past.
HYPER NORMAL MODELS: Known to insiders as Data Vault, the methodology is aptly explained at the level of, if you will, the detail oriented project leader or enterprise architect (vs. a data modeler or engineer) seeking a deep immersion for assessment and implementation oversight. Accordingly, the book describes a cross section of architectural alternatives as the method has evolved in Europe, the U.S., and the land down-under. Among these pathways, two important ones that received less coverage than others in this work are Business Vault and Data Vault 2.0 (DV2).
Business Vault, a layer just-downstream containing rules-based transformed data, either as views or tables, has continued to evolve, as it needed to, such that it actually does provide a nicely standardized method for querying the tables once they are loaded into Hyper Normal structure, either for direct querying or in order to subsequently load a data presentation layer. Those who found writing queries directly from a hyper normal model, and especially those getting started as data modelers or ETL engineers, will benefit greatly from learning more about Business Vault patterns.
Data Vault 2.0 is an innovation in Data Vault, intended to move it from primarily a data modeling methodology into a more modern data warehouse logical architecture that supports increased data loading parallelization and a more loosely-coupled integration with Big Data, in which huge data velocity or volume demands an absolute minimum of target table dependencies. The book “Building a Scalable Data Warehouse with Data Vault 2.0”, by Dan Linstedt and Michael Olschimke covers this innovation with the consistency, the detail, and the excellence that it deserves.
However, in my view, it is THIS book by Hughes that provides that all important data project leadership perspective for a data project leadership framework, if you will. Readers of this book who are intrigued with DV2, and who want to succinct, powerful 'how to' on DV2, should look into Kent Graziano’s “Better Data Modeling: An Introduction to Agile Data Engineering Using Data Vault 2.0” (Kindle), which is a near-perfect complement to this one, guiding data modelers and engineers seeking detailed ‘how to’ on DV2 patterns.
Moreover, this book I'm reviewing now has, in my view, the best top-down (project leadership framework) perspective to date on this exciting methodology. Hughes knows how to communicate and make it not just pallatable, but actually compelling.
HYPER GENERAL MODELS: The author also provides a fine introduction to a methodology for transforming source data directly, running it through a commercial transformation engine, then essentially loading it into dimensions and facts -- a proprietary methodology that is described as being available only through a single business intelligence platform vendor. The method, as I read it, does not simply rely, as I had expected it might, on the clever use of well-known table-generalization, aka ‘Universal Data Model’ principles, with their generalized ‘sub-type / super-type’ tables that, as a very high level explanation, reduce the number of tables essentially by storing records of varying concepts in a single table. Notwithstanding the author’s solid coverage of the hyper-general concepts, his indication -- that the commercial platform that actually executes it leaves some of these logical layers of hyper-generalization away from view from designers using the product – leaves me a bit concerned. However, since I have not used the commercial platform, I will not comment on it further, except to say that this author’s explanation of this intriguing platform was excellent.
Practitioners who fail to understand the insights offered in this book – instead sticking with either the old practice of big-enterprise-design-up-front, or the tactical (requirements driven) design, or no design approach of the inexperienced -- will find that their solutions are too slow to adapt to business change in modern times and will soon gather dust. Data warehouse project leaders who read this book and implement its guidance will know how to keep their solutions, careers, and teams dust-free. Recommended reading.
1 of 1 people found the following review helpful.
If you want to know about real world Agile Data Warehousing, this is the book to read!
By Diana Collins
"Agile" is a hot topic these days, but when it comes to data warehousing, being agile is easy to say, but hard to do. Data warehouse projects often get mired down in trying to answer every question that could possibly be asked, rather than answering the questions that are most critical to the business. Ralph makes a great case in this book for a more business value based approach and provides some solid evidence that traditional tool sets have failed to provide the flexibility required. His presentation of the “hyper generalization” approach is very compelling, both in terms of business value and time/cost savings. Nobody knows this area better than Ralph!
1 of 1 people found the following review helpful.
Ralph does a great job describing Agile Data Warehousing and how to enable it in your enteprise. Recommend!
By Stephen Pace
I've seen Ralph present this material in person at TDWI, and he's exactly right. 60% to 90% of the labor we currently invest in EDWs today could be eliminated through methods described in this book. I particularly liked this discussion of how alternative data modeling techniques can enable agile delivery methods, and I thought the "hyper generalization" approach he presented had particularly large potential for the data warehouse automation space. Recommend!
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