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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.
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 ...
Each standby database applies (replays) the arrived redo, resulting in synchronization with the primary database. [ 5 ] If a database crashes, the recovery process has to apply all transactions, both uncommitted as well as committed, to the data-files on disk, using the information in the redo log files.
Weak ordering classifies memory operations into two categories: data operations and synchronization operations. To enforce program order, a programmer needs to find at least one synchronisation operation in a program. Synchronization operations signal the processor to make sure it has completed and seen all previous operations done by all ...
The PACELC theorem, introduced in 2010, [8] builds on CAP by stating that even in the absence of partitioning, there is another trade-off between latency and consistency. PACELC means, if partition (P) happens, the trade-off is between availability (A) and consistency (C); Else (E), the trade-off is between latency (L) and consistency (C).
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).
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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.