Metadata-Based Queries in C#.NET

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Metadata-Based Queries
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When you write queries against a relational data source, you use Structured Query Language (SQL). SQL is an excellent language, but it was developed primarily for transaction systems, not for reporting applications. One of the problems with SQL is not the language itself, but the fact that the database provides relatively little information about itself. Information about how the data is stored and structured, and perhaps more importantly, what the data means, is called metadata. Relational databases contain a small amount of metadata, but most of the information about the database has to come from you the person writing the SQL query. An OLAP cube, on the other hand, contains a great deal of metadata. For example, when you create an OLAP cube, you define not only what the measures are, but also how they should be aggregated, what the caption should be, and even how the number should best be formatted. Likewise, in an OLAP cube, when you create a dimension with many attributes, you define which attributes are groupable, and whether any of the groupable attributes should be linked together into a hierarchy. Unfortunately, SQL is not able to take advantage of this metadata as you create queries. Consequently, when you use an OLAP data source, you use a different query language, most likely multidimensional expressions, or MDX. MDX was originally developed by Microsoft,
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Understanding OLAP and Analysis Services
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and many OLAP vendors have their own proprietary query languages. But in 2001, Microsoft, Hyperion, and SAS formed the XML for Analysis (XMLA) council to formulate a common specification for working with OLAP data sources. The query language chosen for the XMLA specification is MDX. Most major OLAP vendors have joined the XMLA council and now have XMLA providers. (For more information about XMLA, check out the council s Web site at www.xmla.org.) In this section, you will be introduced to some of the benefits of MDX as a metadata-based query language. You don t need to try to learn the details of how to write MDX; you ll learn more about MDX specifics in a later chapter. Everything you learn about MDX queries in this book definitely applies to Microsoft Analysis Services. Most of it will also apply to most other OLAP providers, but some of the details may be different. One of the key benefits of a query language that can work with the metadata of an OLAP source is that you can use a general-purpose browser to query a specific data source. For example, with a Microsoft Analysis Services cube, you can choose to use Microsoft client tools such as those included in Microsoft Office, or you can choose tools from any of dozens of other vendors. Any client tool that uses MDX or XMLA can understand your cube and generate meaningful reports without the need for you to create custom queries. In other words, because MDX query statements are based on metadata stored in the OLAP cube, you can probably use a tool that will generate the query for you, and you won t have to write any MDX query statements at all. If you do have a reason for writing custom MDX queries, the metadata makes it much easier than writing SQL queries. As a simple example, in SQL, if you create a query that calculates the total Sales Units for each customer s City, you still need to add a clause to make sure that the cities are sorted properly; but in an MDX query, you simply state that you want the members of the City attribute and you automatically get the default sort order as defined in the metadata. As another example, in a SQL table that contains both Country and City columns, there is nothing to suggest that Cities belong to specific countries, so if you want to show all the cities from Germany, you have to explicitly include the fact the you want to filter by Germany but show cities; in an OLAP cube, where Country is defined as the parent of City, you can specify the query using the expression [Germany].Children. In fact, if you later inserted a Region attribute between Country and City, the MDX query would automatically return the regions in Germany, based on the hierarchical relationships defined in the metadata. These are just a taste of the kind of benefits MDX brings to the area of reporting queries. Many other kinds of reporting queries that are difficult in SQL such as a cross-tabulation that shows the best-selling products as column headings and the best-selling regions as row headings are very simple by using MDX queries. Some reports that are simply impossible in SQL such as nesting multiple layers of attributes as column headings are also very simple by using MDX queries.
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