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Base Schema Grain: Orders by order line Approximate rows per month: 300,000 Aggregate Schema Grain: Orders by Month, Product and Salesrep Approximate rows per month: 1000 MONTH Conforms with Day PRODUCT ORDER_FACTS_ AGGREGATE month_key product_key salesrep_key . . . order_dollars . . . SALESREP
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Summary of order_facts: day: Summarized by month customer: Summarized for all customers order_line: Summarized for all order lines
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An aggregate star pre-summarizes order data
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Using this aggregate fact table, determining order_dollars by product for a particular month requires accessing roughly 1/300th the number of rows that must be accessed when using the base fact table. This translates into significantly less work for the database work that includes reading information from disks, joining fact table rows to dimension table rows, grouping data by product, and adding together the individual transactions. The improvement in response time is not necessarily 300 but will be significant. TiP An aggregate table improves response time by reducing the number of rows that must be accessed by the DBMS when responding to a query. Like the derived schemas from 14, the aggregate star does not provide this performance benefit for free. The aggregate tables must be loaded and maintained, resulting in additional work for ETL developers and database administrators. Software tools are available that help automate part or all of this additional work, but even these tools will require configuration, testing, and maintenance.
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Describing the Aggregate: Grain
The easiest way to describe an aggregate schema is to describe the grain of the fact table, as opposed to trying to describe what is being summarized. This is best understood by looking back at the example. Order_facts_aggregate was described as summarizing facts about the orders process by month, product, and salesrep. This simple statement of grain describes the aggregate fact table concisely, by indicating the dimensionality of the aggregate schema: month, product, and salesrep. TiP Define an aggregate schema by stating its grain. Trying to describe order_facts_aggregate in terms of what it summarizes can leave you tongue-tied. One might speak of the aggregate as summarizing the facts across customers and across order lines, or by stating that it summarizes the facts for all customers and for all order lines. This manner of defining the aggregate works within these dimensions but will leave you tongue-tied when it comes to dimensions that are partially summarized. Order_facts_aggregate includes a month dimension a partially summarized version of the day dimension. Stating that the aggregate summarizes facts across days is true but not very specific; a quarterly summary would also fit that definition. Saying it summarizes data for all days is not quite right either, since it groups days into months.
Conformance
An aggregate table must provide the same query results as the original schema. From a design perspective, the easiest way to guarantee this is to require that the aggregate star conform to the base star. This ensures semantic consistency across any common attributes, and equivalent content. It will also be necessary to enforce this conformance as part of the ETL process. In an aggregate schema, the conformance requirement applies both to dimension attributes and to facts. Dimensional conformance has already been discussed. As you learned in 5, two dimension tables conform if they are the same in structure and content, or if the attributes of one are a subset of the other and share common structure and content.
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By guaranteeing the same structure and content of common dimension attributes, the principle of conformance ensures that each replica represents the same thing. Like dimensions, facts can conform. As with conformed dimensions, conformed facts share a common structure and content. Structural equivalence requires that the facts should have the same name, business meaning, and data type. Content equivalence requires that they return the same information when aggregated to the same level of detail. The facts in an aggregate schema conform with those of the base schema. This ensures that the aggregate facts can be used interchangeably with the original facts, while guaranteeing consistent results. TiP The facts and dimensions of an aggregate schema conform to those of the base schema. This guarantees consistent results. Following these guidelines, the dimension tables associated with order_facts_aggregate in Figure 15-1 must conform with those in the base star. Salesperson and product will be the same tables, or identical replicas. The month table will be a conformed rollup of day, as depicted in Figure 5-5. This guarantees that dimensions from the base and aggregate star can be used interchangeably. The facts in order_facts_aggregate must also conform to the base star. Only one fact is shown: order_dollars. It must have the same name and meaning as it does in the base star. It should share the same data type, although it may require greater scale, since it is likely to hold larger numbers. Content must also be the same. Query results that include this fact should return the same results as those from the base fact table. Order dollars by month, for example, should produce exactly the same results from each star. Structural conformance can be controlled at design time, but the conformance of content must be enforced at load time. One way to ensure consistent representation of data in the base and aggregate schema is to use the base star as the source for the aggregate data. This option will be explored later in this chapter.
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