Field note · November 21, 2025
Reading reading_at before trusting it in sensor-telemetry
8,000 rows, all distinct, and the one fact about reading_at that changes how you query it.
Profiling reading_at on sensor-telemetry before anyone builds anything on top of it.
8,000 rows, no nulls, 8,000 distinct values. Reading time, UTC. Irregular spacing.
It runs from 2023-01-01 00:10:00 to 2023-02-28 19:20:00, covering 59 distinct days.
It is a timestamp, not a date, which means every comparison against a bare date is a comparison against midnight. reading_at <= date '2023-06-30' silently excludes almost all of 30 June. Half-open ranges — >= start and < end — avoid the whole class of off-by-one-day bugs and read no worse.
select
count(*) as rows,
count(reading_at) as present,
count(*) - count(reading_at) as nulls,
count(distinct reading_at) as distinct_values
from sensor_telemetry;Check the range before filtering on it. Half the "the dashboard is empty" reports we have seen are a date filter outside the data's actual range, and the query is not wrong so nothing errors.
Where this bites: 59 populated days is what any window function over this column has to work with. A seven-day lag counts rows, not days — so if a day is missing, lag(7) quietly compares against eight days ago and the week-over-week number is wrong in a way that looks plausible.
The grain is one row per sensor reading, which is the context every one of those numbers depends on. None of them survive a change of grain, which is why "profile the column" and "profile the table" are the same job. Full schema, and the CSV, on the dataset page.