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routine tasks, and configure properties for database objects Importantly, the security model has been improved in Analysis Services so you can limit the administrative tasks that can be performed by specific users to certain databases or even selected database objects Developers of Analysis Services databases now have several new considerations for designing a dimension, a key component of an Analysis Services database Dimensions add context to numerical data under analysis by providing labels to describe the time, place, or people involved in a transaction or event Analysis Services 2000 imposed certain restrictions on the size, structure, and content of dimensions that have been eliminated in Analysis Services 2005 For example, now that dimension data is no longer stored in memory, you can work with very large dimensions without impacting server memory and without developing workarounds for the limit of 64,000 children per parent.

Another significant difference in the structure of dimensions is the shift from a hierarchydriven design to an attribute-driven design If you used member properties in Analysis Services 2000, for example, you ll now use attributes to present this data More importantly, however, this new approach enables queries to support summarizations of values by arbitrary groupings, easily done in SQL queries against relational data but problematic when querying traditional OLAP structures As a result, Analysis Services now blurs the line between relational and multidimensional reporting You also have new options for managing data integrity While ETL processes should prevent data integrity issues in your source data, you can now gracefully accommodate orphaned records in the fact table for which a dimension reference does not yet exist by using a placeholder member.

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By taking advantage of this feature, you can ensure the values in the OLAP database always match the values in the source system If, on the other hand, this behavior is not desirable, you can either force an error or ignore the error If you force an error, you can fix the problem before the database is available for querying While ignoring the error is a valid option, it s not recommended unless you re working on a prototype and are focused on getting the correct design before resolving data integrity issues Dimensions are still organized into a multidimensional structure called a cube, but there are some important conceptual differences between Analysis Services 2000 and Analysis Services 2005 In Analysis Services 2000, a single database can contain multiple cubes Each cube corresponds to one, and only one, fact table in a relational data mart or warehouse.

You can combine multiple cubes by creating a virtual cube when you need to analyze data across one or more fact tables A common use for a virtual cube is to compare.

daily level actual sales with monthly level budgeted sales In Analysis Services 2005, you can still create multiple cubes within one database, but best practice is to create one cube This single cube contains one or more measure groups, each of which corresponds to one fact table Thus, a measure group is analogous to a cube in Analysis Services 2000, and an Analysis Services 2005 cube is analogous to a virtual cube If you had to deal with semiadditive measures in Analysis Services 2000, you ll appreciate the new aggregate functions available in Analysis Services 2005 A semiadditive measure is a numerical value that you can add up across any dimension except time A very commonly encountered semiadditive measure is Inventory Count, a value representing the number of units currently in inventory at a specific point in time.

Because of the nature of multithreaded development, concurrency control is a key in any language. J# contains a robust set of synchronization mechanisms to ensure that access to data elements is performed in a controlled fashion.

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