A data warehouse is typically much larger than an OLTP system. Data warehouses over 10's of terabytes are not uncommon and the largest data warehouses grow to orders of magnitude larger. Thus, scalability is a particularly important consideration for data warehouse backup and recovery.
A data warehouse often has lower availability requirements than an OLTP system. While data warehouses are critical to businesses, there is also a significant cost associated with the ability to recover multiple terabytes in a few hours compared to recovering in a day. Some organizations may determine that in the unlikely event of a failure requiring the recovery of a significant portion of the data warehouse, they may tolerate an outage of a day or more if they can save significant expenditures in backup hardware and storage.
A data warehouse is typically updated through a controlled process called the ETL (Extract, Transform, Load) process, unlike in OLTP systems where users are modifying data themselves. Because the data modifications are done in a controlled process, the updates to a data warehouse are often known and reproducible from sources other than redo logs.
A data warehouse contains historical information, and often, significant portions of the older data in a data warehouse are static. For example, a data warehouse may track five years of historical sales data. While the most recent year of data may still be subject to modifications (due to returns, restatements, and so on), the last four years of data may be entirely static. The advantage of static data is that it does not need to be backed up frequently.
These four characteristics are key considerations when devising a backup and recovery strategy that is optimized for data warehouses.