Table-based rules

Table-based rules check the overall integrity of a dataset — not just individual columns. These rules are automatically created for every table you connect to the DQC Platform and serve as foundational health indicators.

What are table-based rules?

Unlike column-level rules, table-based rules assess the entire table and ensure it:

• Has been updated recently

• Contains an expected number of rows

• Matches the expected schema structure

Automatically added rules for freshness, volume, and format

Freshness

This rule checks whether the table is updated within an expected time window — for example, once every 24 hours.

• Helps detect pipeline issues or delays

• Alerts you if the table hasn't been updated recently

Set expected update frequency and monitor actual lag

Volume

This rule checks that the table has a valid row count.

• Alerts if the table is unexpectedly empty or too large

• Can help identify failed loads or duplicates

Define a row count range and view trend over time

Format

This rule verifies that the schema structure of the table hasn’t changed unexpectedly.

• Compares expected column names, order, and types

• Triggers issues if schema drift is detected

Detects schema drift that may affect downstream logic

Notes

• Table-based rules are created automatically — no manual setup needed

• These rules are always shown in the Rules tab under each table

• Learn more: Overview of existing rules, Working with the Rules tab, Rule prediction