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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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Data synchronization is the process of establishing data consistency between two or more databases, and the subsequent continuous updates to maintain said consistency. Data replication is the process of creating one or more redundant copies of a database for the purpose of fault tolerance or accessibility improvement. That’s why knowing the common methods for performing SQL Server data synchronization is important.ĭata availability and consistency can be achieved by data replication and data synchronization processes. There are many situations where it is useful to have a data set that is available and consistent in more than one directory server. Sharing related information among isolated systems has become increasingly important to organizations, as it allows them to improve the quality and availability of data.