For optimistic locking each row has an independent version number, typically a sequential counter. (Do not confuse this technique with row-level versioning used for optimistic locking. Once the change capture is complete, the reference table is updated with a new version number. When a change capture occurs, all data with the latest version number is considered to have changed. This is stored in a supporting construct such as a reference table. A current version is maintained for the table, or possibly a group of tables. One technique is to mark each changed row with a version number. Timestamps on rows are also frequently used for optimistic locking so this column is often available.ĭatabase designers give tables whose changes must be captured a column that contains a version number. Any row in any table that has a timestamp in that column that is more recent than the last time data was captured is considered to have changed. Names such as LAST_UPDATE, LAST_MODIFIED, etc. Tables whose changes must be captured may have a column that represents the time of last change. Multiple CDC solutions can exist in a single system. It is possible that the source and target are the same system physically, but that would not change the design pattern logically. The former is the source, the latter is the target. In a simplified CDC context, one computer system has data believed to have changed from a previous point in time, and a second computer system needs to take action based on that changed data. System developers can set up CDC mechanisms in a number of ways and in any one or a combination of system layers from application logic down to physical storage. In databases, change data capture ( CDC) is a set of software design patterns used to determine and track the data that has changed so that action can be taken using the changed data.ĬDC is an approach to data integration that is based on the identification, capture and delivery of the changes made to enterprise data sources.ĬDC occurs often in data-warehouse environments since capturing and preserving the state of data across time is one of the core functions of a data warehouse, but CDC can be utilized in any database or data repository system. ( March 2016) ( Learn how and when to remove this template message) Please help to improve this article by introducing more precise citations. This article includes a list of references, related reading or external links, but its sources remain unclear because it lacks inline citations.
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