Column anomalies
The elementary.column_anomalies
test executes column-level monitors and anomaly detection on a specific column. It checks the data type of the column and only executes monitors that are relevant to it.
How it works
The test analyzes the specified column in the table. It can analyze as many columns as you specify.
Based on the data type of the column, it applies relevant monitors.
You can specify which monitors to run using the
column_anomalies
parameter.
Default Monitors by Data Type
Data quality metric | Column Type |
---|---|
| any |
| any |
| string |
| string |
| string |
| string |
| string |
| numeric |
| numeric |
| numeric |
| numeric |
| numeric |
| numeric |
| numeric |
Opt-in monitors by type:
Data quality metric | Column Type |
---|---|
| numeric |
Test configuration
Important Notes
No mandatory configuration, however, it is highly recommended to configure a
timestamp_column
.Use
column_anomalies
to specify which monitors to run (if not specified, all default monitors will run).The
where_expression
can be used to filter the data being tested.If no timestamp is configured, Elementary will monitor without time filtering.
Tags can be used to run elementary tests on a dedicated run.
You can configure the test at the model level or at the column level.
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