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 days_back days (by default 14).

The test then compares the freshness of each bucket within the detection period (last 2 days by default, controlled by the backfill_days 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     days_back: int     backfill_days: int     min_training_set_size: int     time_bucket:       period: [hour | day | week | month]       count: int

  - 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 >