Data marts are beginning to emerge from the shadow of their all-encompassing, enterprise-scale cousins, data warehouses, as a more timely and cost-effective approach to organizing and accessing critical business data. Easier, quicker, and less expensive to design and implement, data marts can be designed to meet the specific needs ental concepts and guides you step-by-step through the entire data mart process, from the initial planning stages through implementation and maintenance. With an emphasis on "success," the author draws from his many years of real-world experience to reveal potential pitfalls and offer helpful tips that can make the difference between success and failure in the implementation of a working data mart. You will find thorough coverage of available technologies, fundamental design, specific methodologies, and industry trends. The two major types of data marts are covered: the subset data mart and the increasingly popular incremental data mart, with a special emphasis placed on integration issues.The book also details each step involved in the creation of a data mart: identifying business drivers, forming a team, surveying users, choosing among tools and design options, working with meta data, incorporating company culture into implementation strategy, and even training and support. In the process, you will learn about the different types of data mart designs and architectures, virtual data warehouses, data islands, decision dupport system tools, and much more. Acknowledging that plans don't always proceed smoothly, the author "reality checks"--highlighted sections that discuss what happens when data mart theory meets the reality of the workplace, where sponsors disappear, budgets shrink, and specifications change midstream. 0201183803B04062001

�ber die Autorin bzw. den Autor:

Douglas Hackney is President of Enterprise Group, Ltd. and is a monthly columnist for Data Management Review. He has contributed to Computer World and speaks regularly at industry events, including DCI's Data Warehousing Conference, Bill Inmon's Data Warehousing Conference, and the Data Warehouse Institute.

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