Size of the training, development, and test set

Typically, machine learning practitioners choose the size of the three sets in the ratio of 60:20:20 or 70:15:15. However, there is no hard and fast rule that states that the development and test sets should be of equal size. The following diagram shows the different sizes of the training, development, and test sets:

Another example of the three different sets is as follows:

But what about the scenarios where we have big data to deal with? For example, if we have 10,000,000 records or observations, how would we partition the data? In such a scenario, ML practitioners take most of the data for the training set—as much as 98-99%—and the rest gets divided up for the development and test sets. This is done so that the practitioner can take different kinds of scenarios into account. So, even if we have 1% of data for development and the same for the test test, we will end up with 100,000 records each, and that is a good number.