By Adam Machanic
This booklet bargains a special point of view on database improvement concerns. It first provides and explains complex innovations then demonstrates their use in numerous company eventualities. The target is specialist mastery of either ideas and their program. the focal point all through is on specialist improvement of real-world database functions.
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Security is, however, also one of the most complex areas, and complexity can hide flaws that a trained attacker can easily exploit. Complex security schemes can also have a huge impact on whether a given piece of software is testable and maintainable. From a testing standpoint, a developer needs to consider whether a given security scheme will create too many variables to make testing feasible. For instance, if users are divided into groups and each group has distinct security privileges, should each set of tests be run for each group of users?
The Great Balancing Act When it comes down to it, the real goal of software development is to sell software to customers. But this means producing working software that customers will want to use, in addition to software that can be easily fixed or extended as time and needs progress. When developing a piece of software, there are hard limits on how much can actually be done. No project has a limitless quantity of time or money, so sacrifices must often be made in one area in order to allow for a higher-priority requirement in another.
For example, if you were designing a database to support a banking application, you might be presented with a business rule that states that certain types of accounts are not allowed to be overdrawn. In order to properly enforce this rule for both the current application and all possible future applications, it must be implemented centrally, at the level of the data itself. If the data is guaranteed to be consistent, applications must only worry about what to do with the data. qxd 4/20/07 10:31 AM Page 11 CHAPTER 1 ■ SOFTWARE DEVELOPMENT METHODOLOGIES FOR THE DATABASE WORLD As a general guideline, you should try to implement as many data rules as necessary in order to avoid the possibility of data quality problems.