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This separation is useful for another reason: Microsoft has a habit of changing data access technologies every three years or so, meaning that it is necessary to rewrite the data access code to keep up (remember DAO, RDO, ADO 1.0, ADO 2.0, ADO.NET, and now LINQ and the ADO.NET Entity Framework ). By isolating the data access code into a specific layer, the impact of these changes is limited to a smaller part of the application. Data access mechanisms are typically implemented as a set of services; each service is a procedure that the business logic calls to retrieve, insert, update, or delete data. Although these services are often constructed using objects, it s important to recognize that the designs for an effective Data Access layer are really quite procedural in nature. Attempts to force more object-oriented designs for relational database access often result in increased complexity or decreased performance. I think the best approach is to implement the data access as a set of methods, but encapsulate those methods within objects to keep them organized logically.
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If you re using an object database instead of a relational database, then of course the data access code may be very object-oriented. Few of us get such an opportunity, however, because almost all data is stored in relational databases.
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Sometimes the Data Access layer can be as simple as a series of methods that use ADO.NET directly to retrieve or store data. In other circumstances, the Data Access layer is more complex, providing a more abstract or even metadata-driven way to get at data. In these cases, the Data Access layer can contain a lot of complex code to provide this more abstract data access scheme. The framework created in this book doesn t restrict how you implement your Data Access layer. The examples in the book use LINQ to SQL, but you could also use ADO.NET or use some other metadata-driven Data Access layer directly if you prefer. Another common role for the Data Access layer is to provide mapping between the objectoriented business logic and the relational data in a data store. A good object-oriented model is almost never the same as a good relational database model. Objects often contain data from multiple tables, or even from multiple databases; or conversely, multiple objects in the model can represent a single table. The process of taking the data from the tables in a relational model and getting it into the object-oriented model is called object-relational mapping (ORM), and I ll have more to say on the subject in 2.
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Finally, there s the Data Storage and Management layer. Database servers such as SQL Server and Oracle often handle these tasks, but increasingly, other applications may provide this functionality, too, via technologies such as XML-based services. What s key about this layer is that it handles the physical creation, retrieval, update, and deletion of data. This is different from the Data Access layer, which requests the creation, retrieval, update, and deletion of data. The Data Storage and Management layer actually implements these operations within the context of a database or a set of files, and so on. The business logic (via the Data Access layer) invokes the Data Storage and Management layer, but the layer often includes additional logic to validate the data and its relationship to other data. Sometimes, this is true relational data modeling from a database; other times, it s the application of business logic from an external application. What this means is that a typical Data Storage and Management layer will include business logic that is also implemented in the Business layer. This time the replication is unavoidable because relational databases are designed to enforce data integrity, and that s just another form of business logic. In summary, whether you re using stored procedures in SQL Server, or service calls to another application, you typically handle data storage and management by creating a set of services or
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