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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.
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).
Thus concurrency control is an essential element for correctness in any system where two database transactions or more, executed with time overlap, can access the same data, e.g., virtually in any general-purpose database system. Consequently, a vast body of related research has been accumulated since database systems emerged in the early 1970s.
For instance, if the same record is changed on two nodes simultaneously, an eager replication system would detect the conflict before confirming the commit and abort one of the transactions. A lazy replication system would allow both transactions to commit and run a conflict resolution during re-synchronization. Conflict resolution methods can ...
Modify: Read database values, and tentatively write changes. Validate : Check whether other transactions have modified data that this transaction has used (read or written). This includes transactions that completed after this transaction's start time, and optionally, transactions that are still active at validation time.
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]
Record linkage (also known as data matching, data linkage, entity resolution, and many other terms) is the task of finding records in a data set that refer to the same entity across different data sources (e.g., data files, books, websites, and databases).
Database scalability is the ability of a database to handle changing demands by adding/removing resources. Databases use a host of techniques to cope. [1] According to Marc Brooker: "a system is scalable in the range where marginal cost of additional workload is nearly constant."