~~Title: Replication Overview~~
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<html><font color=#990000 size="+2"><b>Change Capture + Replicatoin Overview</b></font></html>

In database parlance, [[wp> Change_data_capture|change data capture]] (CDC) is a set of software design patterns used to determine (and track)  data that has changed so that action can be taken using the changed data. Also, Change data capture (CDC) is an approach to data integration that is based on the identification, capture and delivery of the changes made to enterprise data sources.

CDC solutions occur most 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.

=== Data Replication ===

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 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. The former is the source, the latter is the target. It is possible that the source and target are the same system physically, but that would not change the design pattern logically.

Not uncommonly, multiple CDC solutions can exist in a single system.

=== Technology Motivation ===

Historically, change capture and data replication have been introduced by relational data management system (RDBMS) vendors as part of an integrated solution for creating multiple, transactionally consistent copies of data. As the database industry evolved to include NoSQL and non-transactional data management systems, vendors introduced features for creating consistent copies of data that can be accessed in a co-located fashion across geographies. In both cases this accelerated queries by offloading processing from the main system of record database, making applications more resilient, fault tolerant and highly available in the process.  

More recently, non-traditional, alternative data management systems (ADMS) sometimes referd to as data fabrics have made use of change capture and data replication to provide continuously available data as a service.  While the primary motiviation of data fabric providers like StreamScape focused on fault tolerance and availability, separating the data copy features into distinct disciplines introduced new capabilities that could be exploited for real-time data analysis and decision support systems. 