To add Elementary data anomaly checks on the "Creation Date" field in your

item_branch__parquet_common_refined4

model, you can use the following Elementary tests based on your requirements: ### 1. Check for changes in the average date age You can use the

elementary.column_anomalies

test on the "creation date" column and specify the

average

monitor. This will track the average value of the date (interpreted as a numeric value, e.g., days since epoch) over time and alert on anomalies. ### 2. Monitor the number of records with dates over time Use the

elementary.volume_anomalies

test with

timestamp_column

set to your "creation date" field. This will monitor the row count per time bucket (e.g., per day) for records that have a creation date. ### 3. Check for nulls in the "creation date" field Add a standard

not_null

test on the "creation date" column. --- Below is an example configuration you can add to your dbt model YAML file:

yaml models:   - name: item_branch__parquet_common_refined4     config:       elementary:         timestamp_column: creation_date     tests:       - elementary.volume_anomalies:           timestamp_column: creation_date           anomaly_direction: both           ignore_small_changes:             spike_failure_percent_threshold: 1             drop_failure_percent_threshold: 1           tags: ["elementary", "daily_elementary", "refined4", "common_refined4"]     columns:       - name: "%itembranchskey"         quote: true         tests:           - unique:               config:                 severity: error                 error_if: ">1"                 warn_if: ">1"           - not_null       - name: creation_date         tests:           - not_null           - elementary.column_anomalies:               column_anomalies:                 - average               tags: ["elementary", "daily_elementary", "refined4", "common_refined4"]

Notes: - The

timestamp_column

should be set to your "creation date" field to enable time-bucketed anomaly detection for both volume and column metrics. This is highly recommended for accurate and timely anomaly detection [see details](https://docs.elementary-data.com/data-tests/anomaly-detection-configuration/timestamp-column). - The

average

monitor on a date field will track the average value (e.g., average days since epoch). If you want to track a different metric, you can adjust the

column_anomalies

list accordingly [see supported metrics](https://docs.elementary-data.com/data-tests/anomaly-detection-configuration/column-anomalies). - The

not_null

test will alert if any nulls are present in the "creation date" field. This configuration will provide the checks you described, leveraging Elementary's anomaly detection and dbt's standard data quality tests. References: - [Column anomalies configuration](https://docs.elementary-data.com/data-tests/anomaly-detection-configuration/column-anomalies) - [Volume anomalies test](https://docs.elementary-data.com/data-tests/anomaly-detection-tests/volume-anomalies) - [Timestamp column usage](https://docs.elementary-data.com/data-tests/anomaly-detection-configuration/timestamp-column)