What are the various methods related to data sharing through data propagation?

Data Propagation is the allocation of data from one or more source data warehouses to another local access database, according to propagation rules. Data warehouses are required to manage large bulks of data every day. A data warehouse can start with a few information, and starts to increase day by day by constant sharing and receiving from multiple data sources.

As data sharing advances, data warehouse management becomes a major problem. Database management is required to manage the corporate information more effectively and in multiple subsets, arranging and time frames. These data resources are required to be constantly updated and the process of updating contains moving high volumes of records from one system to another and forth and back to a business intelligence system.

It is familiar for data movement of large volumes to be executed in batches within a short period without sacrificing the performance or availability of operation software or data from the warehouse. The larger the volume of information to be changed, the more challenging and complicated the procedure becomes. As such, it becomes the responsibility of the data warehouse management to find means of transforming bulk information more quickly and recognizing and transferring only the data which has changed because of the last data warehouse update.

There are several methods developed to address the problems associated with data sharing through data propagation as follows −

Bulk Extract − In this technique of data propagation, copy management tools or empty utilities are being used to derive all or a subset of the operational relational database. Generally, the extracted information is then transported to the focus database using file transfer protocol (FTP) any other similar techniques. The data which has been extracted can be changed to the format used by the object on the host or object server.

File Compare − This technique is an innovation of the bulk move approach. This phase compares the recently extracted operational data to the past version. After that, a set of incremental change data is generated. The processing of incremental changes is the same as the methods used in bulk extract except that the incremental changes are used as updates to the object server within the scheduled phase. This method is recommended for smaller documents where there are only some data changes.

Change Data Propagation − This technique captures and data the changes to the file as an element of the software change process. Several techniques can be used to execute Change Data Propagation including triggers, log exits, log post-processing, or DBMS extensions. A file of incremental changes is generated to include the captured changes.

After finishing the source transaction, the change data can be transformed and changed to the object database. This kind of data propagation is sometimes referred to as near real-time or continuous propagation and is utilized in keeping the object database synchronized within a very short period of a source system.