Product Analytics. Part 1 Cleaning data: filtering out empty values

144. Cleaning data: filtering out empty values

We’ve talked about how important it is to validate data before doing any reports. S**t in – S**t out.

With JSON columns it’s quite easy to make a mistake, we’ve already seen how filtering for empty JSON values could be misleading. Compare these 2 queries:

SELECT COUNT(*) FROM web_analytics.pageviews WHERE custom_parameters IS NOT NULL 
SELECT COUNT(*) FROM web_analytics.pageviews WHERE custom_parameters::text != '{}'

Quite different numbers, right? Imagine we’d calculate conversion rate or something with these numbers, could be very dangerous.

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Anatoli Makarevich, author of SQL Habit About SQL Habit

Hi, it’s Anatoli, the author of SQL Habit. 👋

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-- Type your query here, for example this one -- lists all records from users table: SELECT * FROM users
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