Using historic data has the advantage that it is real data, and as such will probably have the same data skews and biases as the data that the data warehouse has to capture. The problem is how to capture the real data in the first place. As the mechanisms that the data warehouse will use to capture the source data are unlikely to be working yet, one-off methods may have to be developed to get the data required.

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One of the first tasks in developing a test plan is to come up with a test schedule. Most product development methodologies contain metrics for producing time estimates for testing as well as development. From experience, these metrics fall well short of the requirements for testing a data warehouse application. The shortcomings of these metrics are due to the size and complexity of data warehouse systems. For example, a simple problem with a large query can cause it to run inefficiently. However, because of the size of the query it may take a day to shew that it is not going to complete in the desired time-scales. By the time the query has been tuned and re-run several times, many days of testing time may have been lost.

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In system testing, the whole data warehouse application is tested together. The components are tested to ensure that they work properly together, that they do not cause system resource bottlenecks, and so on. Again, because of the size of a data warehouse system, it is usually possible to perform only minimal system testing before the test plan proper can be enacted. So any pre-plan system testing is normally carried out on a radically cut down version of the system environment. Such tests are useful only in highlighting inconsistencies in the data warehouse components, and will give no real indication of performance or resource issues.

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Since system testing can easily be confused with user acceptance testing, it is important to understand what exactly system testing is. System testing is the first time where bought-in products such as management and query tools are tested in the full system environment. This will often be the first real indication of both the usefulness and in particular the overheads of such tools. Isolated testing of these products generally gives no indication of their effects on a system.

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System testing is usually confused with user acceptance testing, which will also tends to be a complete test of the data warehouse application. In user acceptance testing, as the name implies, the users conduct their own tests on the system. The user acceptance testing for a data warehouse is no different from that for any other application.

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