By Rick van der Lans
''This ebook for these in enterprise and data administration explains the way to use information virtualization in company intelligence structures a few basic wisdom of information warehousing, enterprise intelligence, and database know-how is assumed.''-- Reference and examine publication News, December 2012
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Extra resources for Data Virtualization for Business Intelligence Systems
It would not make sense to execute this analysis with yesterday’s data. Another application area is credit card fraud detection. A classic form of credit card fraud is when stolen card data are used to purchase products. Each new purchase has to be analyzed to see if it fits the buying pattern of the card owner and whether the purchase makes sense. One of the checks can be whether two purchases in different cities occurred within a short period of time of each other. For example, if a new purchase is made in Boston and a purchase was made in San Francisco just a few seconds earlier, the odds are that this is a case of fraud.
By copying all the data to the staging area, we have safeguarded that this data won’t get lost. l When the data in the staging area has been transformed and copied to a warehouse, it can be deleted. But there might be reasons to keep it. In that case, it’s usually referred to as a persistent staging area. By keeping all that data, the staging area can be used when the data has to be reloaded into the data warehouse. Note that a data staging area might also be relevant to a business intelligence system using no data warehouse but data marts only.
So if production systems do not keep track of historical data, an additional data store is required to make reporting and analytics on historical data possible. l Interference: The queries executed by the reporting tools on the production databases might cause too much interference on the production systems. A query might be so I/O intensive that users entering new data in the production environment feel a performance degradation. In other words, the business users will be blocking the users of the production systems and thus interfering with the production itself.