Page 128 - DCAP603_DATAWARE_HOUSING_AND_DATAMINING

Data Warehousing and Data Mining

notes and target. This works by getting the source data and moving it to a separate location where
transformation takes place.
Advantages
1. Centralized approach assists in scalability
2. Rapid interface development
3. Data staging

4. For large volumes of data some tools have transient data store, where excess data is
processed.
5. The same development environment and tools can be used for all application interfaces, so
there is minimal impact on the source and target systems.
6. It is very useful for large data volumes
Disadvantage
As transformation is done in a centralized location the tools are not scalable.

Data Warehouse and Data Mart Loaders

The Data warehouse and Data mart loaders can be found in either code generator or engine/ hub
forms. The focus is in transforming operational data into a form that can be loaded into a very
specific type of data store. Data aggregation is required so as to transform data in an application
network.
Disadvantages
Warehouse loaders do not have the fault tolerance or performance requirements that make them
viable for linking together a host of operational systems.

Business Model Level products

The various products are:
1. Remote Procedure Calls
2. Stored Procedure Calls
3. Object Request Brokers

4. Transaction Processing Monitors
5. Database Triggers
6. Message Queuing
7. Message Broker

8. Asynchronous RPCs
9. Publish and Subscribe
At business model level two applications can be integrated through the use of function calls
i.e., one application sends data to the other by calling a function over a network. In the PRC
mechanism the source application calls the function of another by specifically naming the target
application and its function.
In the Message Broker the application calls a logical function that it wishes to be executed. The
broker then maps this to a specific function in another application. Neither the source nor the

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