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An alternative perspective on facts and dimensions can be found in Mastering Data Warehouse Design by Claudia Imhoff, Nicholas Galemmo, and Jonathan Geiger (Wiley, 2003). This book introduces facts and dimensions in 5, and provides examples of data delivery processes that move data from the enterprise data warehouse into a dimensional data mart. All of the books mentioned here provide brief descriptions of OLAP and cubes. For in-depth coverage of the multidimensional database, the cube, and online analytical processing, see OLAP Solutions, Second Edition by Erik Thomsen (Wiley, 2002).
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A Fact Table for Each Process
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Multiple Stars
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32 4
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A Fact Table for Each Process
It is rare to find a subject area that can be fully described by a single fact table. It is impossible to find an enterprise that can be covered by a single fact table. In almost every practical application, multiple fact tables will be necessary. As a general rule of thumb, dimensional designs include a single fact table for each process to be studied. This allows each process to be analyzed individually, without undue complications that result from designs where a single fact table covers multiple processes. This chapter presents techniques you can use to determine when you are dealing with multiple processes, and explains the implications of not describing them in separate fact tables. While analysis of individual processes is useful, some of the most powerful analytics cross process boundaries. In a dimensional environment, this will require combining information from more than one fact table. This chapter looks at what happens when this is done incorrectly, and provides a two-step process to ensure accurate results. This process is called drilling across. You will learn that there are multiple ways in which query and reporting tools handle this process, each with its own advantages and disadvantages. Finally, you will learn what to do when the available tools cannot drill across.
Fact Tables and Business Processes
Dimensional models describe how people measure their world. As previous chapters have emphasized, each star schema contains a fact table that is home to measurements describing a particular process. The measurements, or facts, are given context by their related dimensions. The grain of the fact table describes the level of detail at which the facts are recorded. A simple rule of thumb governs the distribution of facts across fact tables: TiP To be studied individually, each process should have its own fact table. When designers follow this guideline, users are able to study each individual process without undue complication. In a few moments, we will begin looking at what some of those complications might be. First, it is necessary to address the ambiguity of the word process.
PART II
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Some readers may be struggling with the guideline just given. What exactly is a process Those steeped in the world of information engineering may be familiar with the concept of process modeling, a lesser-known companion to entity-relationship modeling. While the entity-relationship model is used to describe information, the process model is used to describe business activity. Just as the entity-relationship guides the database design of an operational system, the process model guides design of the functional components. Here s the rub: process models involve functional decomposition. That is to say: one process can be broken down into several subprocesses. For example, the sales process may be broken down into subprocesses for order entry, shipment, invoicing, and returns management. If we try to apply the guideline stated earlier, we run into a complication: sales seems to be a process, but it also seems to be made up of other processes. Does the study of sales require multiple fact tables, or just one Rather than use the concepts of process modeling to drive star schema development, two simple tests can be used to separate measurements into multiple fact tables. TiP For a given pair of facts, ask these questions: 1. Do these facts occur simultaneously 2. Are these facts available at the same level of detail (or grain) If the answer to either of these questions is no, the facts represent different processes. When two facts do not describe events at the same point in time, or are not specified at the same grain, they describe different processes. For example, consider measurements such as quantity ordered and quantity shipped. Orders and shipments do not necessarily occur simultaneously. When an order is placed, information about shipments has yet to be determined. Shipment information is finalized later. Quantity ordered and quantity shipped also fail to share the same level of detail or grain. Shipment quantities are associated with specific shippers, while order quantities are not. In this case, quantity ordered and quantity shipped failed both tests. Orders and shipments are two separate processes. If there will be people who want to analyze either process on its own, it will necessitate multiple fact tables. To understand why, we will look at these examples in more detail. NoTe While it is important that each process that will be studied individually has its own fact table, it is not the case that every fact table should correspond to one process. Multiple-process fact tables can be useful when comparing processes. They are derived from other fact tables, and may contain aggregated data. For example, sales analysis may be supported by creating a star that summarizes multiple stars: proposals, orders, shipments, and returns. Examples will be explored in 14, Derived Schemas. If there is no desire to study the processes individually, fact tables for the individual processes may be omitted. This may lead to difficulty if a user decides to focus on one fact, as you are about to learn.
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