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Aggregate Generation
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The other half of the invisibility equation deals with the automatic generation and maintenance of aggregates. For some tools, aggregate navigation and generation capabilities are closely bound together, while others separate them or only offer one or the other.
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Generating and Maintaining Aggregates
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Tools that generate aggregate stars or cubes provide a user interface that allows a developer to specify the characteristics of the desired aggregates. Such tools range from a commandline utility to a graphical user interface, and allow the developer to identify the level of summarization for each aggregate. Based on information provided by the designer, the tool then generates an aggregate table or a cube. Relational database products provide this kind of capability in a number of ways. Many such tools support the definition of materialized views or materialized query tables, which can be used in the service of aggregation. Some databases and OLAP products work in a similar way but generate multidimensional cubes. Tools that generate aggregates or cubes are built around a native understanding of the star schema and dimensional data. These products understand such concepts as facts, dimensions, surrogate keys, natural keys, and slow changes. When sufficient information
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15 Aggregates 361
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about a dimensional schema is provided, they can walk the developer through a set of questions and create a cube or aggregate that is automatically maintained by the DBMS. Allowing the DBMS to maintain an aggregate structure sometimes requires surrendering complete control over how the aggregate is maintained or kept up to date. Some warehouse teams decide to forgo this automation in favor of finer or more efficient control, instead opting to build and maintain aggregates as part of the overall ETL process.
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Hierarchies and Aggregates
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As described in 7, an attribute hierarchy represents a known set of master detail relationships among attributes in a dimension table. Just as parent child hierarchies among attributes are not required to drill into data, they are also not required to define a level of summarization. However, many tools that generate aggregate tables or cubes are designed to work with hierarchies. In these cases, there may be value in defining and documenting aggregates around these relationships. Suppose, for example, that natural hierarchies exist among the attributes in the dimension tables of the order_facts star in Figure 15-1. Each product falls within a brand, and each brand falls within a category. Similar hierarchies exist for the attributes of day, salesrep, and customer. The resulting hierarchies are depicted in Figure 15-5. Notice that the diagram also incorporates the degenerate dimension order_line, which contributes to the grain of order_facts. When dimensional hierarchies are expressed in this explicit manner, an aggregate can be defined simply by drawing a circle around the portions of the hierarchies that will be included. As drawn in Figure 15-5, the outlined region represents the grain of the order_ facts_aggregate table from Figure 15-1: orders by month, product, and salesrep. Tools that can generate cubes based on the existence of attribute hierarchies often require some additional information. They require the developer to identify which attributes appear at each level of the hierarchy and optionally require identification of a subset of attributes that will guarantee an instance of the level. For example, brand_code, brand_name, and brand_manager are present at the brand level. A code and name are sufficient to identify a brand; brand_manager is a type 1 attribute that will be overwritten.
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Orders by: ALL DAYS ALL PRODUCTS ALL SALESREPS ALL CUSTOMERS ALL ORDERS
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CATEGORY
REGION
INDUSTRY
ORDER_LINE
MONTH
BRAND
TERRITORY
CUSTOMER
PRODUCT
SALESREP
Figure 15-5
Designing aggregates around attribute hierarchies
Part V
PART V
Performance
Armed with this metadata, the tool is able to determine what can be included in a brandlevel attribute and maintain the aggregate structure accordingly. TiP Summarizing dimensional data does not require an attribute hierarchy, but some tools rely on this kind of relationship to define and generate aggregates. The existence of hierarchies like these may help define an aggregate, but they are not necessary. Brands, for example, might not fall neatly into categories the way Figure 15-5 depicts. This would not prevent either brand or category from being used to define an aggregate of order_facts. Indeed, even a product-level attribute, such as the product s color, might be used to define a summary table. Don t let tools that focus on attribute hierarchies cause you to lose sight of possibilities like this. It may still be possible to generate the aggregate, even if it requires working around a hierarchy-based view of dimensions.
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