- name: < model name >
      - elementary.freshness_anomalies:
          timestamp_column: < timestamp column > # Mandatory
          where_expression: < sql expression >
          time_bucket: # Daily by default
            period: < time period >
            count: < number of periods >


Monitors the freshness of your table over time, as the expected time between data updates.

Upon running the test, your data is split into time buckets (daily by default, configurable with the time bucket field), and then we compute the maximum freshness value per bucket for the last training_period (by default 14 days).

The test then compares the freshness of each bucket within the detection period (last 2 days by default, controlled by the detection_period var), and compares it to the freshness of the previous time buckets. If there were any anomalies during the detection period, the test will fail.

Test configuration

Required configuration: timestamp_column Default configuration: anomaly_direction: spike to alert only on delays.

tests:   — elementary.freshness_anomalies:     timestamp_column: column name     where_expression: sql expression     anomaly_sensitivity: int     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     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]