Reading flight_date before trusting it in flight-delays

Profiling flight_date on flight-delays before anyone builds anything on top of it.

8,000 rows, no nulls, 365 distinct values. Scheduled date of departure.

It runs from 2023-01-01 to 2023-12-31, covering 365 distinct days.

It is a date rather than a timestamp, so there is no time zone to get wrong here. That is worth noticing, because the moment a column like this gains a time component, every daily aggregate silently shifts for anyone not in UTC.

select
  count(*)                      as rows,
  count(flight_date)            as present,
  count(*) - count(flight_date) as nulls,
  count(distinct flight_date)   as distinct_values
from flight_delays;

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: 365 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 scheduled flight, 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.