Reading channel before trusting it in retail-orders

Profiling channel on retail-orders before anyone builds anything on top of it.

7,000 rows, no nulls, 4 distinct values. web, ios, android, or marketplace.

4 values, and they are not evenly spread: web 39.1%, ios 24.7%, android 19.8%, marketplace 16.4%. The largest takes 39.1% on its own.

Read the distinct list rather than the distinct count. Casing differences and trailing whitespace produce values that look identical in a report and group separately in SQL, and the count will not show you that — select distinct channel order by 1 will, in about a second.

select
  count(*)                  as rows,
  count(channel)            as present,
  count(*) - count(channel) as nulls,
  count(distinct channel)   as distinct_values
from retail_orders;

Low cardinality, stable values — this is a column you can group by without thinking about it, and a reasonable candidate for a chart facet. Check the distinct list, not just the count, because a stray casing variant hides in the count and shows up in the group-by.

Where this bites: a group-by on this column produces 4 rows today. If it is a column an upstream system can add values to, it produces an unknown number tomorrow, and any dashboard laid out for 4 categories reflows without warning. Values arriving is a schema change that no schema check catches.

The grain is one row per order line, 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.