I think your question is too general to answer effectively. In theory just about any data structure can be implemented on disk but in practice access times are

I think your question is too general to answer effectively. In theory just about any data structure can be implemented on disk but in practice access times are likely to be too slow for that to be a usable solution in most cases. As far as I know databases typically use a few specific data structures which are optimized for this sort of application, such as B-trees. Terabytes of data for a decision tree ? I've heard of deep learning models with billions of parameters but I find it surprising that you would need anywhere near that much storage for a decision tree. EDIT: Moreover if you do need that much storage, you are almost certainly going to need multiple nodes to process that data reasonably quickly so you would almost certainly need a way to partition the dataset across nodes rather than having everything stored in a single file. The company probably most known for developing that kind of architecture is Google and I think there is a fair amount of information available online on their architecture. That might be a good place to look to get you started. EDIT2: And how would you create such a large decision tree in the first place ? Are you sure that's the best type of model for your problem ? It seems to me it would likely massively overfit just about any dataset you trained it on.