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Working with a Time Dimension
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There s practically no such thing as an OLAP database without a Time dimension. Often, a Time dimension contains months as the lowest level of detail aggregated into quarters and years. Sometimes, a Time dimension will contain days as the lowest level of detail. On occasion, particularly if you re monitoring a manufacturing operation or Internet activity, you might create a dimension with minutes or even seconds as the lowest level of detail. Whatever the level of detail, a Time dimension has certain unique qualities. For example, time typically occurs in regular intervals. Each hour contains 60 minutes, each day contains 24 hours, each quarter contains 3 months, and each year contains 4 quarters. This repetitive nature of time encourages certain questions, such as, How does this month
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compare to the same month of last year The multidimensional expressions (MDX) language, which you ll learn about in 8, Using MDX, has functions that make it easy to answer this type of question. By flagging certain dimensions as Time dimensions, and certain levels within a dimension as specific units of time, you can make those functions easy to use. Of course, time isn t completely uniform because the 365 days in a year aren t evenly divisible by the 7 days in a week or the 12 months in a year. Some months have 30 days; some have 31, or 28, or occasionally 29. Months begin on different days of the week. Irregularities are a fact of life in Time dimensions, and when working with time, you need to be prepared for both the regularities and the irregularities. One irregularity that frequently arises when dealing with time is that many organizations use a fiscal year where the starting day of the year isn t January 1. As you saw in 3, Analysis Services can build a Time dimension based on a specified date range and add a special calendar to this dimension for Fiscal Year. For greater flexibility, you should use a dimension table from your data warehouse because you include special properties for a date, such as the season for a month if this information is important to analysis over time in your organization.
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As you learned previously in this chapter, the DSV is a very flexible structure that can be modified as you add dimensions and cubes to your project. You can add tables to the DSV at any time. In addition, you can add a Named Query to limit the columns available from a single table or to construct a logical view by combining columns from multiple tables. As another alternative, you can add a derived column, known as a Named Calculation, to a table. Using a Named Query or a Named Calculation gives you the ability to manipulate the data structures for use by Analysis Services even if you don t have permissions to make similar changes at the database level. If you use Analysis Services to generate a Server Time dimension for you based on a range of dates, you need to use a Date/Time column in the fact table to use this dimension. (Creating a Server Time dimension is discussed later in this chapter.) However, it s generally recommended that you use a separate dimension table for dates rather than use a Date/Time column in the fact table. As with other dimension tables, you would then use an integer key to join the date dimension table with the fact table. This difference in data types in the fact table date/time which requires 8 bytes versus integer which requires 4 bytes can have a big impact on the amount of space required to store the fact table in your data warehouse when the fact table contains millions of records. Further, as already noted, you can create a Time dimension table that includes special time-based attributes that your business uses when analyzing data that won t be available if you rely solely on the Server Time dimension. In this procedure, you ll add tables to the DSV.
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