There are several methods to create continuous backups of a dataset. The three main backup methods full backup, differential backup, and incremental backup each offer specific advantages and disadvantages. In this article, we provide an overview of them, but you can read more about each type in our in-depth guides on each backup strategy.
Generally, a backup strategy usually includes several types of backups. First, a full backup is created. This is followed by incremental or differential backups and, if necessary, periodic full backups. Different data sets are backed up with varying frequency using the appropriate methods, depending on requirements.
Let’s assume an organization has a current dataset of 100 GB in need of backing up. Let’s further assume that the dataset grows by 1 GB a day. As part of a conventional backup strategy, a full backup is created on the weekend. Furthermore, daily amends are to be backed up by another backup. So all backup methods start with a full backup on Sunday. Subsequently, depending on the backup method, only changes are backed up if necessary.
Let’s compare the three types of data backup. First, we compare the size of daily growth in data with data volumes accruing per backup method. The volume of the full backups corresponds to the volume of the data stock. The volume of a differential backup grows linearly over time as of the last full backup. In contrast, the volume of incremental backups corresponds to the volume of data changed: