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DataFactory 5.2, from Quest Software, quickly and easily generates accurate production-scale test data for even the most complex relational database designs on virtually any database platform. Testing with data of sufficient quantity and quality is critically important to obtaining accurate load-testing metrics and validating your application s user interface. If your Web application isn t load tested against a database populated with the same scale of data that you d have in production, all your load testing is a waste of time. This results from many reasons, not the least of which is the fact that, in absence of large-scale data sets, database servers often perform table scans to read data because entire tables easily fit into server memory. The indexes that you are trying to tune, therefore, are most likely never accessed during tests, which totally invalidates the most critical of your database s performance metrics, such as join performance. Even more important than index design is the structure of the database itself. Denormalization should be performed only if you have an absolutely compelling reason to do so, and then only to the smallest extent possible. Observing performance under load with production-scale data is the only way to make an informed decision about where to denormalize your database structure, if at all. For information on denormalizing your database, see 8.
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With respect to user-interface design, Web artists and developers often neglect to take the scale of data being presented to the user into consideration and sometimes make wrong interface decisions because of this failure. DataFactory ensures that initial interface design takes into consideration the actual scale of data presented to the user. A browser s select menu, for instance, should not contain a large number of items; otherwise, the browser renders slowly at best or not at all at worst. Using DataFactory early in the development process tells engineers how much data their interfaces must expose and lets those engineers more effectively choose the proper interface elements as a result. We ve been using DataFactory religiously for quite a while now to our great benefit. At less than $600 (US), it was a no-brainer. Our Return on Investment (ROI) was less than one day, as evidenced by our very first load test against a large news service site that we were building. (Return on Investment means how long it takes for the product to pay for itself.) First, a database trace analysis showed that we could almost double the speed of the most critical and heavily used function of the site by eliminating one of the existing database indexes and creating a new one with an unusual collection of columns that we could not have guessed during initial design. Second, we discovered that one of the search criteria that our client wanted on his site could have brought the system to its knees many times a day by retrieving tens of thousands of rows under certain conditions. Three quick adjustments later, the site was FLB (Fast Like Bunny) under load. It would have been technically impossible to discover these potential problems before deployment without the production-scale test data that we could generate in a matter of minutes by using DataFactory. You can (and should) take DataFactory for a test-drive at
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