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Synchronization process between a server and two clients. Data synchronization is the process of establishing consistency between source and target data stores, and the continuous harmonization of the data over time. It is fundamental to a wide variety of applications, including file synchronization and mobile device synchronization.
Data integration refers to the process of combining, sharing, or synchronizing data from multiple sources to provide users with a unified view. [1] There are a wide range of possible applications for data integration, from commercial (such as when a business merges multiple databases) to scientific (combining research data from different bioinformatics repositories).
SymmetricDS is database independent, data synchronization software. It uses web and database technologies to replicate tables between relational databases in near real time. The software was designed to scale for a large number of databases, work across low-bandwidth connections, and withstand periods of network outage.
SymmetricDS is open source software for database and file synchronization with Multi-master replication, filtered synchronization, and transformation capabilities. [2] It is designed to scale for a large number of nodes, work across low-bandwidth connections, and withstand periods of network outage. [3]
Synchronization overheads can significantly impact performance in parallel computing environments, where merging data from multiple processes can incur costs substantially higher—often by two or more orders of magnitude—than processing the same data on a single thread, primarily due to the additional overhead of inter-process communication ...
Microsoft Sync Framework is a data synchronization platform from Microsoft that can be used to synchronize data across multiple data stores. Sync Framework includes a transport-agnostic architecture, into which data store-specific synchronization providers, modelled on the ADO.NET data provider API, can be plugged in. Sync Framework can be used for offline access to data, by working against a ...
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This is often a stop-the-world process that traverses a whole table and rewrites it with the last version of each data item. PostgreSQL can use this approach with its VACUUM FREEZE process. Other databases split the storage blocks into two parts: the data part and an undo log. The data part always keeps the last committed version.