DQOps

Certain data quality requirements influenced by business users may require more complex data quality checks. These data quality checks should be separated and thoroughly analyzed. Below are the most common data quality requirements that should be satisfied by custom data quality checks.

᛫ Custom data formats. Column values must follow a complex pattern that is too complex to parse using regular expressions. These usually involve names that must follow a naming convention.

᛫ Multi-column checks. These data quality checks perform arithmetic operations across different columns. A simple example is a data quality check that verifies that a net_price + tax = total_price.

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