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Unformatted text preview: IBM Software Group White Paper XML: Changing the data warehouse Deliver new levels of business analysis and bring users closer to their data 2 Deliver new levels of business analysis Executive summary Some saw it coming years ago. Others are just beginning to realize what lies ahead. We’re talking about the impact that XML is having on data warehousing environments. For IT leaders building data warehouses that meet the evolving demands of their business environments, integration of XML data into their infrastructures is critical. With XML, organizations can support evolving business reporting and analysis requirements without incurring signifcant database schema changes or rewriting applications. Simultaneously, they can bring their users closer to the information that they need to make accurate business decisions while providing the user with a result set that is similar to a search engine on the Internet. XML has become the preferred data exchange format across many industries. As a result, organizations must fnd ways to effciently manage and manipulate XML within their data warehouses. In fact, leading-edge frms in retail, fnance, healthcare and other industries already have production environments that leverage both relational and XML database technologies. While their implementations vary, early adopters are often striving to promote greater business agility, providing decision makers with more accurate and timely information and improving IT staff productivity. IBM ® DB2 ® pureXML ® makes it possible for organizations to manage XML data as well as relational data. This increases database effciency, improves the user experience and increases their competitive advantage by fully exploiting data interchange standards. Data warehouses and evolving business needs One of the primary goals of data warehousing is to make it as easy as possible for users to get the information that they need when they need it. However, traditional relational schemes can make this diffcult to achieve. Consider a data warehouse in the retail industry that tracks sales information. Using a typical star schema database design, as pictured in Figure 1, a “fact” table might contain sales data by product, region and time period. Such data would typically be joined with data in multiple “dimension” tables to obtain specifc details pertaining to various products, regions and so on. Unfortunately, different products have different attributes, which presents challenges both in database design and in fguring out how to expose the data to users. With a relational-only design, each product attribute must be captured in its own column. But because attributes vary widely from one type of product to another, the result could be an ineffcient, unwieldy product table with thousands of sparsely populated columns. And, as new products and new product attributes are introduced over time, the database schema—and any applications that depend on it—would need to be altered, which can be quite costly. 3...
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This note was uploaded on 06/14/2011 for the course DATABASE - taught by Professor - during the Spring '11 term at Aarhus Universitet.
- Spring '11