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CHAPTER 8 DATABASE INTEGRATION
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The tasks we have created were done so with database schema migration and synchronization in mind, although as stated, the manual task will handle any arbitrary SQL. We do need to consider how to handle data synchronization. In the scenario earlier, the task synchronizes the schema for an integration database that may be used for unit-testing purposes, and a database used for system testing, which may contain a different set of data for example, a representation of the production system data. Therefore, the data migration requirements could well be different for each database: Integration database. The integration database may need to be fully refreshed with data for unit testing, or just some standing data such as ISO country codes. Good unit tests should create their own data and then destroy it, but we all know that unit tests come in all varieties. System database. If the system database does contain a representation of the live data, then migration scripts are needed rather than refresh scripts. This is the ideal time to test those scripts for eventual use in the production environment. So how to do this The options are certainly the same the processing of scripts that are manually generated or the use of SQL Data Compare (for instance) to automatically synchronize the data but the decision may be different. Whereas synchronization is what we were trying to achieve with the database schemas, it is not our goal with data migration. Data migration also tends to be a more semantic affair than schema migration, and thus developer decision-making skills could be called for. My feeling is that it is probably best to manually create scripts for data migration and manipulation, using the manual task to run these scripts as part of the delivery process. Tools such as SQL Data Compare could then be used to verify these scripts if desired. Martin Fowler s article on agile databases and Scott Ambler s book Agile Database Techniques (see Further Reading ) describe toolkits of scripts for data migration to speed up and standardize this work. This works by taking an approach to database manipulation similar to one a developer might take to code refactoring: creating specific mechanics for performing specific tasks under specific contexts. So, for example, in code refactoring we may choose to Extract Method and follow a mechanism to perform this refactoring; in database manipulation we may choose to Introduce Trigger following a mechanism to perform this work. The database examples are not quite so rounded as the original work on code refactoring, but there are a lot of ideas and examples in both of these sources. This is a useful practice for a development team. Additionally, in order to make the automation easy given the context of the task we have created, it is a good idea to organize the output of these activities. Table 8-4 shows how this might be done. Table 8-4. Organizing Database Scripts
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Contains the scripts produced by the automated SQL Compare task Contains the scripts for the insertion and refresh of standing data such as lookup Contains scripts for the insertion and refresh of test data if required Contains scripts for the migration (updates, inserts, deletes) of existing production (or production representation) data
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CHAPTER 8 DATABASE INTEGRATION
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With the scripts organized in this way, the task we have created can be used to run the sets of scripts required given the particular database instance. Additionally, we can leverage reusability in the build scripts on a large scale.
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A Process for Database Integration
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Having worked out the practical details for the actual work to be done, let us consider how we can implement database integration into the CI process. First, we return to the Control and Configure versioning tasks we identified at the outset of this chapter.
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