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One question that needs to be answered early before starting with the data testing process is - Where is the test data going to come from? Is real user data going to be used, or will the data be generated? Either approach is valid, but there are a number of pitfalls and issues with ea-ch. 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 yelt, one-off methods may have to be developed to get the data required. For example, suppose the source data requires modification on the source system before being transferred to the data warehouse system. It is possible that the changes will not have been made on the source system in time for the system test. This could mean that the data you get will be in a different form from the data that the data warehouse will normally receive. This will require transformation programs to be written just to perform the system test.

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Backup recovery also forces you toward a partitioning approach. Backing up or restoring a table containing all online fact data would be a major undertaking. The trick with backup in an environment of this size is to cut down on the amount that has to be backed up on a regular basis. If you have not partitioned, you will be forced to back up the whole fact table. The one exception to this is if the RDBMS allows incremental backup, but even then the whole table may have to be scanned to determine which blocks had changed. To cut down on the backup size all partitions other than the current partition can be marked read-only, and put into a state where they cannot be modified. Then they can be backed up once, although preferably twice, and forgotten. This means that only the current partition is required to be backed up.

Star schemas are a good way of minimizing the set of data that needs to be scanned to respond to a query. Query performance can still be substantially improved, by completely removing large parts of the fact table from the possible set of data that needs to be scanned. This is achieved by breaking up the fact table into partitions, and then requiring the query to scan the partitions that are relevant. There are challenges to overcome when using this kind of partitioning. How will the query know which partition to access? What about accessing a number of table partitions representing a longer time period? These are some questions for data warehouse managers to ponder upon.

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Testing of the database warehouse operational environment is another key set of tests that will have to be performed. There are a number of aspects that need to be tested. Security is difficult to test unless the data warehouse manager has clearly documented what is not allowed. If there is a separate data warehouse security document, testing should be a relatively straightforward matter of extracting the list of disallowed operations, and devising a test for each. This set of tests should be run several times during system testing to ensure that newly added data, aggregations and so on do not cause security breaches. It is important to make sure that the relevant subsequent tests should also be carried out after each recovery operation, to ensure that the data remains secure.

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