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December 19th, AM by dcd Mark This Forum Read. View Parent Forum. Search this Forum : Advanced Search. BB code is On. Smilies are On. Forum Rules. All times are GMT The time now is PM. Multiple measure groups with different characteristics can exist in one cube. One of the most common areas of confusion with SSAS and SSAS is the fact that measure groups default to repeating a value for all members of an unrelated dimension.
For example, the AdventureWorks database includes products sold over the Internet and through a reseller channel. For Internet sales, AdventureWorks knows the end customer and operates without a reseller. Therefore, if a user looks at reseller sales and tries to view the customer, the same value repeats for each customer, as you see in Figure 4.
Along the left side is a breakdown of the customer dimension by country. The default setting is to ignore unrelated dimensions, which can cause a value to be repeated for every member of an attribute from a dimension unrelated to the measure group. Fortunately, an easy way exists to change this default behavior, and adjusting it often prevents users from believing that the data they see represents valid values for those customers. The solution is a property on the measure group called IgnoreUnrelatedDimensions.
The default value for IgnoreUnrelatedDimensions is True, which means that the measure group ignores any dimension not tied to it by showing the current value for all members in the unrelated dimension. This is a simple fix that can significantly reduce user confusion.
Aggregation Answers A primary reason for developing cubes is to create pre-calculated aggregations. Aggregations are summarizations of data at any level higher than the grain, which is the lowest level of detail in the fact table. If you record sales per customer per product per day, the total sales per customer for a day or month or quarter would each represent different aggregations e.
In addition, the total sales for all customers for a day or month by product subcategory or product category also represent aggregations. Considering the variety of attributes and levels of hierarchical information available, one of the most straightforward ways to enhance cube performance is to pre-calculate and store these aggregations. Unfortunately, some businesses go through the entire deployment process without ever creating any aggregations.
The Aggregation Wizard organizes aggregations based on either a percentage of total aggregation, storage size, or simply the point at which the designer tells the wizard to stop. One common approach to handling aggregations is to let the wizard create a low percentage of aggregations, such as 20 percent.
Professional SQL Server Analysis Services 2005 with MDX
Then, after the cube is running in production, you can log queries and later run through the Usage Based Optimization UBO wizard. The UBO wizard looks at the actual queries and designs aggregations that support them, meaning that aggregations become far more tuned to actual usage patterns. Another problem you want to avoid is having too many aggregations. Queries can actually suffer as the engine searches through a host of aggregations to try to determine which is best at fulfilling a particular question.
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Some DBAs use the Aggregation Wizard and set it to percent, which tends to slightly improve performance while dramatically increasing physical storage requirements and processing time. Some database pros erroneously assume that if one aggregation is good, thousands must be better, and so they produce every possible aggregation. Not only does this require vast amounts of storage, but it can also stretch processing times into hours or even days.
Processing Options Suppose you need to keep the cube available at all times, but it sometimes goes offline for processing.
To minimize processing time, you can create attribute relationships and tune the number of aggregations to improve query times while still allowing for processing in the allotted time window. Commonsense tips for solving the problem include reducing the number of attributes in the dimensions and partitioning the data. You can also tune specific processing settings.
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The default Processing Order is Parallel, and Maximum parallel tasks is set to Let the server decide. Changing this value can result in better or worse performance. Although a number of values populate the list, you can also manually enter a value, using a trial-and-error approach. Data Source Login Hassles Developers often create data sources that connect to the database with Windows authentication but set the impersonation information to a service account.
Such an account might not have the appropriate permissions to access the data, especially when the project is moved from development to test to production. Worse, data connections are sometimes made with hard-coded usernames and passwords in them, which works well until the password changes.
Normally this happens after midnight on a weekend, causing a developer to spend hours updating all the information. The best practice for connecting to data sources is to use Windows authentication, then impersonate using a domain account created specifically for accessing the source data. This account should have read-only access to the data, except when using data-mining models or writeback. Organizations pursuing business intelligence projects should determine how to deliver data and to whom it should go before designing the relational data warehouse and cubes.
Knowing how you deliver data may drive some cube design decisions involving perspectives, key performance indicators, and more.
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