What are some uses of logistic regression at scale? Many libraries that scale linear and logistic regression assume a tall-skinny design matrix (many samples, few features), but I don't understand why you would need billions of samples if your data has 250 features.
In what scenarios would more data help? It seems like, instead of using more computational resources, you could simply sub-sample the data and achieve comparable accuracy in scenarios where the feature count is relatively small.
When feature count is high, say 100k, would having samples on the order of billions help? How do we decide how many samples are needed for a given modeling problem?
Perhaps more data helps with imbalanced data? e.g. when performing binary logistic regression for outlier detection, where the occurrence of positive samples is very small.
Any help here would be greatly appreciated.

Your general premise is correct: you don’t need more data than you need. But also, as noticed in the other answer and comments, it may be more tricky than you’re assuming. First of all you need large enough, high-quality sample of data. Using more data than is needed for the desired precision would be wasteful. On another hand, logistic regression is a pretty fast and cheap model to train, at least as compared with many other machine learning models, so you wouldn’t usually care that much. You want the libraries to be scalable for cases where it’s needed, but this doesn’t mean that you always need to push as much data as you can to the algorithm.
