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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.
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.
In the Oracle RDBMS environment, redo logs comprise files in a proprietary format which log a history of all changes made to the database. Each redo log file consists of redo records. A redo record, also called a redo entry, holds a group of change vectors, each of which describes or represents a change made to a single block in the database.
In multithreaded computing, the ABA problem occurs during synchronization, when a location is read twice, has the same value for both reads, and the read value being the same twice is used to conclude that nothing has happened in the interim; however, another thread can execute between the two reads and change the value, do other work, then change the value back, thus fooling the first thread ...
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).
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 ...
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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).