Market time series data is stored in a completely different way. In fact, I would say it is prepared rather than stored. Each data item only needs to be written once and after that never needs to be modified or changed. Data items can be written sequentially, there is no need to insert anything in the middle. It needs no ACID functionality at all. They have little to no references out to any other data. The time series is effectively its own thing.

It should be useful for data storage and transmission. We can always convert data during upload or download in OpenML's client APIs. For instance, people may upload a Python pandas dataframe to OpenML, and later get the same dataframe back, without realizing or caring how the data was stored in the meantime. If people want to store the data locally, they can download it in the format they like (e.g. a memory-mapped format like Arrow/Feather for fast reading or TFRecords for people using TensorFlow). Additional code can facilitate such conversions.

t’s start with a quick primer/refresher on what functional programming is about, from t