Also, due to their complexity, they do not scale well and the resulting data is difficult to browse. While this approach performs actual data integration and addresses the problem of poor performance in the federated approach, it requires additional calls to traditional relational databases to integrate descriptive data. While data cleaning is being performed on some of the data sources, it is not being done across all sources or in the same place. This makes it difficult to quickly add new data sources. Because one still needs to make additional relational database calls outside of the memory mapped data structures, there is a performance hit when queries are executed.
Data integration takes place by using the results of one query to link the user to another database.
The problem with this approach is that it does not provide a mechanism to integrate in-house relational databases, nor does itprovide a mechanism to perform data cleaning and transformation for complex data mining. In this approach data is exported from each source application into a Data Staging Area. Here, all of the data is cleaned up, transformed as necessary, and linked with data from other sources. This staging process is key to the success of this approach, and is fully automatable once the scientific logic has been properly specified and implemented. Then, when staging is complete, the data is placed in a unified and integrated central presentation database which is a composition of smaller databases.