Data lifecycle management involves the automated management of data, database objects, and product versions. It aims to archive and clean up data, reduce storage costs, and maintain system performance.
Background information
The digitalization of businesses has led to an exponential increase in data volume, making it costly to store all data. Unmanaged data storage not only wastes resources but also slows down system performance. Therefore, data can be archived to other target databases to address the impact of increased online data on query performance. For more information, see Data archiving.
After archiving data from the source database to the target database, you can delete the data from the source database to improve query performance and reduce online storage costs. For more information, see Data cleaning