One side of data lakes is it's more of a start of where the data is. Your normalised and more processed data may end up in a nice clean database but the start i

One side of data lakes is it's more of a start of where the data is. Your normalised and more processed data may end up in a nice clean database but the start is not like that. The other main points are usually * Data size * Data access patterns * Data formats The more you're looking at "I want to pull 400G of data out of my 30TB set of images from a bunch of machines running a custom python script, then shut it down in twenty minutes and not start anything else until tomorrow" then the more a data lake makes sense vs a database. > because isn't updating it a huge chore? Not with the right tools, which can also give you things like a git-like commit experience with branching. > You have to make sure that if you're updating you're not also generating new analytics, which it seems like you're always doing because it's very slow. Why would you be generating new analytics? I feel I've missed something there.