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By allowing the concept of conformance to guide aggregate design, we guarantee consistent results from the summary, while keeping the process of rewriting queries as simple as possible This same guideline has one additional advantage: it helps keep the ETL process simple as well Construction of conformed dimensions was discussed in 5 If a dimension table is shared by the base star and aggregate, only one version exists; no additional work is required at ETL time If the aggregate uses an identical copy of the original dimension, this can be handled via simple replication In the case of a conformed rollup, such as the month table in Figure 15-1, some additional processing will be needed The rollup will summarize attributes of the base dimension table, but it has its own surrogate key.

The SQLBIS database server was created to service BNAs trading web site, Brokerage Information System (BIS). On the database server are several databases (or data marts) that are used by BIS and the BIS Trading Area to look up account and application access as well as cross-references and other information. The ORDERDB database was created exclusively to support the WebSphere applications. In development, the tables and views were created by developers and migrated to production by the database administration (DBA) group.

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As you learned in 5, the best way to load a conformed rollup is to use the base dimension table as its source This eliminates any redundant processing that would be required if the rollup dimension were loaded from the original source More importantly, it eliminates the possibility that processing rules will be applied inconsistently This guarantees consistent structure and content, the twin requirements of conformance The process can be streamlined by adding housekeeping columns to the base dimension that indicate the date the row was created and the date the row was last modified These dates can be used by the process that loads the conformed rollup to identify changes The development of ETL routines for aggregate fact tables is guided by the same principle Sourcing the aggregate from the base star guarantees the conformance that is required, both in terms of structure and in terms of content.

This section describes the case study trading application using text and diagrams (mostly UML diagrams). This task is not unlike what you as a SCEA Part 2 test taker must accomplish. As you saw in 3, UML diagrams and use cases

TiP Source aggregate tables from the base schema to guarantee consistent structure and content Following these guidelines, the overall dependency of table load routines during the ETL process might look like this: 1 Load base dimensions 2 Load conformed rollups 3 Load base fact table 4 Load aggregate fact table Actual dependencies are slightly more relaxed than this For example, after base dimensions have been loaded, it is possible to load the base fact table Unfortunately, there is a complication..

can replace what was formerly called the functional requirements in an application development scenario Moreover, UML is an adopted and widely accepted standard used to describe business processing UML provides benefits to architects and enterprises by facilitating the construction of robust and maintainable business models, which can support the entire software development life cycle (SDLC) The use case model describes the target functionality of a new application A use case represents a unit of interaction between an actor and some function A use case diagram describes an interaction between an actor and the system It presents a collection of use cases and actors and typically specifies or characterizes the functionality and behavior of an enterprise application interacting with one or more external actors The users and any system that may interact with the system are the actors.

USE master EXEC sp_serveroption '(local)\USA', 'lazy schema validation', 'true' EXEC sp_serveroption '(local)\World', 'lazy schema validation', 'true'

One potential roadblock stands in the way of this simple workflow: the type 1 change. When base data and aggregates are loaded sequentially, the occurrence of a type 1 change can force the need to reload the aggregate completely, rather than update it incrementally. To understand why this happens, consider a very simple example. Suppose an aggregate of order_facts contains rows that summarize orders by product manager, which is a type 1 attribute. This aggregate will call for a simple conformed rollup of the product dimension, which includes only the column product_manager.

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