Measurement / schema design

Hello there,

We do monitor one particular type of an asset that during its operation makes a movement from unlocked to locked position. All over this movement we do collect by means of a current sensor sample everything from 150 to 500 measurements over an entire movement period. We do like to consider all together as one event and would like to store this event as one time series instance within time series database. Even 1500 measurements for one operation appears to happen as of failure.
Now given above we tried different approaches for schema design. At very first we stored all measurements as one string with coma delimiter. It worked nice in python code where we do a simple split to generate a array of floats representing current or effect measured at that particular point of time during movement operation. These days we started with testing whether Grafana or similar tools might help us to achieve our goals even quicker. Suddenly we realized that Grafana could not unpack and split the string in order to reason on measurement over time.
Now we wonder whether we should design schema with something like 500 measurements something like tags [‘turnoutAre’, ‘direction’], {mp1: FLOAT, mp2: FLOAT, …, mp500: float} and so forth.
Any answer is appreciated.
Thanks in advance

That is how I would model the data too, with each measurement being a field.