![]() In this method, we can use a tool to compare between source and target data. WHERE NOT EXISTS (SELECT * FROM Source WHERE Target.ID = Source.ID) WHERE NOT EXISTS (SELECT * FROM Target WHERE Target.ID = Source.ID) įROM Target INNER JOIN Source ON Target.ID = Source.ID If tables have the same primary key, and the target table doesn’t have an auto-incrementing (identity) primary key, you can execute the following synchronization script. We’ll do synchronization between the table Source, with columns ID and Value, and the table Target, with the same columns. It is hard to maintain-when database structure is changed, it is necessary to modify two or three scripts ( INSERT, UPDATE, and sometimes also DELETE).You can only synchronize data that is available via SQL queries, so you can’t import from sources like CSV and XML files.Creating such a SQL script is quite tedious, because three scripts are usually needed for each table: INSERT, UPDATE, and DELETE.Can be used as an automatic import, even on continuously changed data.The SQL script can be saved into a stored procedure, or run periodically as a job for SQL Server.If the table has indexes, it is very fast.Can be performed by free and open source (FOSS) tools.The most straightforward and tedious solution is to manually write SQL scripts for synchronization. Source and Destination Have the Same or Very Similar Structures Using Manually Created SQL Scripts Synchronization using automatically generated SQL scripts - need commercial product.Synchronization using the data compare method (can be used only when source and target have similar structure).Synchronization using manually created SQL scripts.Regardless of how similar the structures are, we can choose four different ways for solving data synchronization: The method used depends on personal preferences and complexity of the problem you need to solve. Usually, imports need to run automatically on a scheduled basis. The most common case is when a piece of software needs to import data from another piece of software which is maintained by another company. 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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