Understanding OLAP and Analysis Services in C#.NET

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Understanding OLAP and Analysis Services
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grains for the second square, four grains for the third, and so forth, doubling for each of the 64 squares of the board. Of course, by the time the king s magicians calculated the total amount of rice needed to pay the reward, they realized that had they known the metric system and the distance to the sun it would require a warehouse 3 meters by 5 meters by twice the distance to the sun to pay the reward. In one version of the legend, the king simply solved the problem by cutting off Sessa s head. In another version, the king was more noble and also more clever. He gave Sessa a sack, pointed him to the warehouse and told him to go count out his reward no rush. The problem Sessa gave the king was the result of a geometric progression: When numbers increase geometrically, they get very large very quickly, and the size of a cube increases geometrically with the number of dimensions. That is the problem with OLAP stored using a spreadsheet model. Because any cell in cube space has the potential for being stored physically, data explosion becomes a very real problem that must be managed. The more dimensions you include in the cube, and the more members in each dimension, the greater the data explosion potential. Spreadsheet-based OLAP tools typically have elaborate and complicated techniques for managing data explosion, but even so, they are still very limited in size. Spreadsheet-based OLAP tools are typically associated with financial applications. Most financial applications involve relatively small databases coupled with complex, nonadditive calculations.
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Database model OLAP OLAP tools that store cube data by using a database model behave very differently. They take advantage of the fact that most reporting requires addition, and that addition is an associative operation. For example, when adding the numbers 3, 5, and 7, it doesn t matter whether you add 3 and 5 to get 8 before adding the 7, or whether you add 5 and 7 to get 12 before adding 3. In either case, the final answer is 15. In a purely relational database, you can get fast query results by creating aggregate tables. In an aggregate table, you presummarize values that will be needed in a report. For example, in a fact table that includes thousands of products, five years of daily data, and perhaps several other dimensions, you may have millions of rows in the fact table, requiring many minutes to generate a report by product subcategory and by quarter, even if there are only 50 subcategories and 20 quarters. But if you presummarize the data into an aggregate table that includes only subcategories and quarters, the aggregate table will have at most one thousand rows, and a report requesting totals by subcategory and by quarter will be extremely fast. In fact, because of the associative nature of addition, a report requesting totals by category and by year can use the same aggregate table, again producing the results very quickly.
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Perhaps the biggest benefit of OLAP stored using the database model is the ability to avoid data explosion. Because you need relatively few aggregate tables to provide fast results, you can have much larger cubes with many more dimensions and attributes than by using a spreadsheet model. Perhaps the biggest disadvantage of OLAP stored by using a database model is that there is no inherent way to physically store values that are calculated using nonassociative
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Part I:
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Getting Started with Analysis Services
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operators. An extreme example of a difficult financial calculation is Retained Earnings Since Inception. To calculate this value, you must first calculate Net Income itself a hodgepodge of various additions, subtractions, and multiplications. And you must calculate Net Income for every period back to the beginning of time so that you can sum them together. This is not an associative calculation, so calculating for all of the business units does not make it any easier to calculate the value for the total company. Even OLAP cubes that are stored by using the database model can calculate some nonassociative values very quickly. For example, an Average Selling Price is not an additive value you can t simply add prices together. But to calculate the Average Selling Price for an entire product line, you simply sum the Sales Amount and Sales Quantity across the product line, and then, at the product line level, you divide the total Sales Amount by the total Sales Quantity. Because you are calculating a simple ratio of two additive values, the result is essentially just as fast as retrieving a simple additive value. Database-style OLAP tools are usually associated with sales or similar databases. Sales cubes are often huge both with hundreds of millions of fact-table rows, and with multiple dimensions with many attributes. Sales cubes also often involve additive measures (dollars and units are generally additive) or formulas that can be calculated quickly based on additive values. One of the major benefits of OLAP is the ability to precalculate values so that reports can be rendered very quickly. Different OLAP technologies may have different strengths and weaknesses, but a good OLAP implementation will be much faster than the equivalent relational query whenever highly summarized values are involved.
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