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Data Push versus Metric Pull: Competing Paradigms for Data Warehouse Design and their Implications
Abstract
Although data warehousing theory and technology have been around for well over ten years, it may well be the next really hot technology. How can it be that a technology sleeps for a decade and then begins to move rapidly to the foreground? There can be several answers to this question. It could be that the technology had not yet caught up to the theory. It could be that computer technology ten years ago did not have the capacity to delivery what the theory promised. Or it could be that the ideas and the products were just ahead of their time. All of these answers are true to some extent. But the real answer, I believe, is that data warehousing is in the process of undergoing a radical theoretical shift and that paradigmatic shift will reposition data warehousing to meet demands of the future. This past summer I taught a course in data warehousing. Since it is a new course, and I only get to try out new courses in the summer, I have only been able to teach this course three times so far. Nonetheless, I have already noticed that there are two distinct, and largely incompatible, views of the nature of a data warehouse. A prospective student, who had several years of industry experience in data warehousing but little theoretical insight came by my office, one day, to find out more about the course I would be teaching. “Are you an Inmonite or a Kimballite,” she inquired, reducing the possibilities to the core issues. “Well, I suppose if you put it that way,” I replied, “I would have to classify myself as a Kimballite.”
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