# Table grain quick validation with SQL


I was doing some exploratory data analysis on a number of tables I didn’t have much information about and, unfortunately, didn’t know their grain.

I needed a quick way to validate my assumptions about the table grain, identify contradicting observations (rows), and check for duplicates at the same time.

Therefore I decided to use a combination of TO\_JSON\_STRING and FARM\_FINGERPRINT. The first creates a JSON representation of the entire row (given a table alias), while the second converts the resulting string into a INT64 hash.

By comparing the total number of rows in a group against the distinct count of these fingerprints, we can determine whether the proposed grain is correct and whether there are duplicates in the data.

This was a quick exercise but use this with care. Depending on your SQL implementation, data volumes and context, results may vary.

![](https://miro.medium.com/v2/resize:fit:700/0*r6BJhPYZrb7ivJg5 align="left")

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