All columns anomalies

Elementary Data - All Columns Anomalies: Detect anomalies across all columns in dbt™ projects with Paradime. Enhance data observability and quality.

The elementary.all_columns_anomalies test executes column-level monitors and anomaly detection on all columns of the table. It checks the data type of each column and only executes monitors that are relevant to it.

How it works

• The test analyzes all columns in the table.

• Based on the data type of each column, it applies relevant monitors.

• You can override default monitors using the column_anomalies parameter.

• Columns can be excluded using exclude_prefix or exclude_regexp parameters.

Default Monitors by Data Type

Data quality metric	Column Type
null_count	any
null_percent	any
min_length	string
max_length	string
average_length	string
missing_count	string
missing_percent	string
min	numeric
max	numeric
average	numeric
zero_count	numeric
zero_percent	numeric
standard_deviation	numeric
variance	numeric

Opt-in monitors by type:

Data quality metric	Column Type
sum	numeric

Models

models:
  - name: < model name >
    config:
      elementary:
        timestamp_column: < timestamp column >
    tests:
      - elementary.all_columns_anomalies:
          column_anomalies: < specific monitors, all if null >
          where_expression: < sql expression >
          time_bucket: # Daily by default
            period: < time period >
            count: < number of periods >

Models example

​Test configuration

No mandatory configuration, however it is highly recommended to configure a timestamp_column.

tests:
  — elementary.all_columns_anomalies:
    timestamp_column: column name
    column_anomalies: column monitors list
    exclude_prefix: string
    exclude_regexp: regex
    where_expression: sql expression
    anomaly_sensitivity: int
    anomaly_direction: [both | spike | drop]
    detection_period:
      period: [hour | day | week | month]
      count: int
    training_period:
      period: [hour | day | week | month]
      count: int
    time_bucket:
      period: [hour | day | week | month]
      count: int
    seasonality: day_of_week
    detection_delay:
      period: [hour | day | week | month]
      count: int
    ignore_small_changes:
      spike_failure_percent_threshold: int
      drop_failure_percent_threshold: int
    anomaly_exclude_metrics: [SQL expression]

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).

• exclude_prefix and exclude_regexp can be used to exclude specific columns from the test.

• The where_expression can be used to filter the data being tested.

• Global sensitivity can be adjusted using the anomaly_sensitivity parameter.

• Tags can be used to run elementary tests on a dedicated run.