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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 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]
The younger threads can access data from older threads (but not the other way around), and writes to the same address are based on the thread order. In some cases, dependencies between threads can cause the younger versions to abort. [11] Intel's Transactional Synchronization Extensions (TSX) is available in some of the Skylake processors.
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 UI data binding, data and information objects of the same language, but different logic function are bound together (e.g., Java UI elements to Java objects). [1] In a data binding process, each data change is reflected automatically by the elements that are bound to the data.
OMA Device Management is a device management protocol specified by the Open Mobile Alliance (OMA) Device Management (DM) Working Group and the Data Synchronization (DS) Working Group. [1] The current approved specification of OMA DM is version 1.2.1, [ 2 ] the latest modifications to this version released in June 2008. [ 3 ]
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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 ...