![]() ![]() A common case is importing from one database into another. This is also a more frequently recurring task. If the structures are different, synchronization is more complicated. Source and Destination Have Different Structures The common requirement is to compare data between the testing and production database and import data from the production into the testing database. For example, the data structure in the testing and production environments is very similar. This is very often the case when we use data in various stages of the software development lifecycle. Source and Destination Have Very Similar Structures We will try to describe the most common methods and tools that can be used to solve data synchronization on Microsoft SQL Server databases and try to give some recommendations.īased on the structure of the source and destination (e.g., databases, tables) we can differentiate use cases when structures are similar or different. ![]() We can implement architecture for data synchronization tasks manually, possibly using Microsoft Sync Framework, or we can benefit from already created solutions within tools for managing Microsoft SQL Server. ![]() Implementation and maintenance of data synchronization is such a time consuming process, it can be a full-time job by itself. This results in difficult maintenance and higher expenses. Since there are no standard ways of doing this, besides replication, the implementations of data synchronization are rarely optimal. When a new requirement comes up, database specialists usually have to reimplement the whole synchronization process. In real scenarios, data synchronization consists of many complex tasks, which can take a long time to perform. This task differs from case to case, and even data synchronizations that should be simple at first glance can be complicated, due to the complexity of data structures. There is no unique way or unanimously agreed method for data synchronization.
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