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Ralph Kimball has made numerous important contributions to the world of data warehousing, and his top two contributions both relate to dimensional design First, in the 1990s, he was largely responsible for popularizing star schema design Through his writings, Kimball synthesized and systematized a series of techniques that had been in use as early as the 1960s He explained how dimensional design provided an understandable and powerful way to develop analytic databases, and he gave us the terminology that is used throughout this book Second, Kimball developed an enterprise architecture for the data warehouse, built on the concept of dimensional design Sometimes referred to as the bus architecture, it shares many characteristics of Inmon s Corporate Information Factory It allows for an integrated repository of atomic data and relies on dimensional design to support analytics.

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In this book, Kimball s architecture will be referred to as the dimensional data warehouse architecture To those unfamiliar with Kimball s work, this second contribution often comes as a surprise Because he is so closely associated with the star schema, he is often assigned blame for shortcomings associated with any implementation that utilizes a star, regardless of its architecture Other times, the star schema itself is assigned blame In order to sort things out, it is necessary to take a brief look at Kimball s architecture, which is depicted in Figure 2-2 Again, the diagram is somewhat simplified In this case, it has been laid out to highlight similarities to Inmon s architecture Though the diagram in Figure 2-2 appears quite different from that in Figure 2-1, the two architectures actually share many characteristics in common.

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but are minor. Clearly, the weaknesses of NCR are related to the entire stock market. The trading ranges and daily fluctuations can be readily observed on a price pattern chart. The investor should take the time to become familiar with these trading ranges and fluctuations from the preceding few months. Many Internet sources provide daily price charts at no charge. They can be saved on a computer or printed (where allowed). Familiarity with price movements will help the investor differentiate between a normal fluctuation and a breakout to a new trading range. If a lower stock price is within the normal range, it may still be a winner, even if the investor is experiencing a small loss assuming that the initial analysis showed the stock to be a winner in earnings and growth. Therefore, the kind of weakness seen in a normal fluctuation does not indicate that the time to sell out and take a loss has arrived. Exception 2: Market Decline On October 19, 1987, more than 604 million shares were traded on the New York Stock Exchange and 239 million on the American and Nasdaq

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Like the Corporate Information Factory, this architecture begins by assuming a separation of the operational and analytic systems As before, operational systems appear on the far left of the diagram Again, these may incorporate data stores that are relational and nonrelational, and are likely to be numerous Moving to the right, an ETL process consolidates information from the various operational systems, integrates it, and loads it into a single repository If that sounds familiar, it should The Corporate Information Factory has an analogous process The dimensional data warehouse in the center of Figure 2-2 is the end result of the ETL process It is an integrated repository for atomic data Again, that should sound familiar The same definition was given for Inmon s enterprise data warehouse It contains a single view of business activities, as drawn from throughout the enterprise.

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It stores that information in a highly granular, or atomic, format The dimensional data warehouse differs from the enterprise data warehouse in two important ways First, it is designed according to the principles of dimensional modeling It consists of a series of star schemas or cubes, which capture information at the lowest level of.

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