I had to deal with such problem, too. But with the slight difference (?) that I had to store a large number of values for the same timestamp. And with the knowledge, that on later reading of the data, only one/few values related to one timestamp is/are needed.
I chose to store the timestamp and every value in dedicated datasets for each. With a "loose" coupling by the the dataset index of the values only. The n-th timestamp in the timestamp dataset relates to all n-th values in the other datasets. This reduces the i/o on reading data - as with the compound solution a complete record is to be read even with only few of the record values being needed.
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