To monitor for nulls in your amount field, you should specify the monitors

null_count

and

null_percent

in the

column_anomalies

configuration. These monitors will track both the count and percentage of null values in the column, alerting you to any significant changes or anomalies. Here’s how you can configure it in your dbt model YAML:

yaml columns:   - name: amount     tests:       - elementary.column_anomalies:           column_anomalies: [null_count, null_percent]

What does "which monitors to use" mean? The

column_anomalies

parameter lets you choose which specific data quality metrics (monitors) you want to track for a column. If you leave it blank, Elementary will use the default monitors for the column type. By specifying

[null_count, null_percent]

, you are telling Elementary to only monitor the count and percentage of nulls for the

amount

column, which matches your requirement to ensure there are not too many nulls. You can find the full list of supported monitors and their default behavior in the documentation. For numeric columns like

amount

,

null_count

and

null_percent

are both available and appropriate for your use case [see documentation](https://docs.elementary-data.com/data-tests/anomaly-detection-configuration/column-anomalies) and [column_anomalies test details](https://docs.elementary-data.com/data-tests/anomaly-detection-tests/column-anomalies). Let me know if you need an example for a different column type or want to monitor additional metrics!