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Business View Only this portion is visible to a report developer or user Drag items onto canvas to design reports
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Business View Days Date Month Year Customers Name Address Phone Order Info Order Dollars . . . Technical Definition = = day.month = day.year = customer.full_name = customer.address = customer.phone_num = sum (order_facts.order_dollars) . . .
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Semantic Layer Configured in advance by a developer or architect Used by tool to generate SQL
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Figure 16-2 A reporting tool generates SQL
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The semantic layer in Figure 16-2 is defined by a developer. It includes metadata that describes the tables of the orders star and their relationships, as can be seen in the top of the diagram. This is similar to the top portion of the SQL generator from Figure 16-1. However, this view is not exposed to a person building a report. Instead, a business view is incorporated in the semantic layer. It can be seen in the bottom part of the diagram. This is the only part the user will see, and it contains the things they can drag onto a report canvas. The semantic layer also includes information that maps the items in the business view back to specific columns in the database schema. All this information is used to generate SQL. A user, for example, drags onto his or her canvas something called Name and something called Order Dollars. The query generator uses the information in the semantic layer to determine how to fetch this information. It identifies these elements as corresponding to the column called full_name in the customer table, and the sum of the column called order_dollars in the order_facts table. The table relationships are consulted to determine how these tables are joined together, and then an SQL statement can be constructed. Of course, the specific mechanics vary widely by product implementation. Business intelligence products can be used to produce sophisticated semantic layers. Most allow an element in the business view that maps back to a combination of columns; have associated help text or data definitions; are capable of generating complex SQL,
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16 Design and Business Intelligence 373
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including subqueries; are capable of expressing complex query predicates; and can enforce security access rules. All these functions mean that business intelligence tools can be extremely powerful components of the data warehouse architecture. At a minimum, they simplify work for report developers and may enable end users to build their own self-serve reports. Business intelligence tools also add value in additional ways, incorporating an infrastructure for the development, automation, and sharing of reports, charts, widgets, messages, and alerts and for collaboration among users.
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Any tool that generates queries must be configured to do so. This configuration activity is typically carried out by a developer. A basic tool may require the developer to specify tables and joins as part of the report definition, as in Figure 16-1. Tools that incorporate a business view of information, as in Figure 16-2, require that this information be configured as a separate task, prior to the creation of any reports. Regardless of the level of sophistication, all these tools require that someone, somewhere, has provided some information about the database schema, and perhaps set up configuration options that control how SQL is generated. For simplicity s sake, this chapter will refer to all this information as the semantic layer, even though the software industry tends to reserve that term for solutions that include a separate business view of information. This configuration information is often described as metadata because it describes, at least in part, the physical storage structures.
The Limitations of SQL Generators
Query generators are not intelligent. They do not understand the information contained in the database, or what a user wants to see. When called on to generate SQL, they apply a set of heuristics to the user request and available schema information to generate a query. Hopefully, this results in an SQL query that is exactly what the user wants. Generated SQL always follows some standard formats, or templates. The range of possibilities is a function of the product in question. Breadth and accuracy of generated SQL are also influenced by the configuration of the semantic layer. Users will only be able to combine elements from the database into a query or report in ways that are enabled by the semantic layer, as set up by a developer. Taken together, these factors the query generation capabilities of a business intelligence product and its configuration determine what kind of queries can be formulated. For users who rely on the tool to create reports, this effectively limits the ways in which the dimensional model can be utilized. Understanding these limitations is crucial in completing any dimensional design. The limitations of SQL generators fall into two major categories: the inability to generate a desired query and the ability to generate an undesired query. No tool is capable of generating an appropriate query for every situation. Standard features of dimensional models sometimes pose particular difficulties for query generators. Some tools are unable to automate the drill-across process, comparing orders to shipments, for example. If other tools are available, it may be possible to overlook this shortcoming.
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