> ## Documentation Index > Fetch the complete documentation index at: https://docs.elementary-data.com/llms.txt > Use this file to discover all available pages before exploring further. # event_freshness_anomalies Let our Slack chatbot create the anomaly test you need. `elementary.event_freshness_anomalies` Monitors the freshness of event data over time, as the expected time it takes each event to load - that is, the time between when the event actually occurs (the `event timestamp`), and when it is loaded to the database (the `update timestamp`). This test complements the `freshness_anomalies` test and is primarily intended for data that is updated in a continuous / streaming fashion. The test can work in a couple of modes: * If only an `event_timestamp_column` is supplied, the test measures over time the difference between the current timestamp ("now") and the most recent event timestamp. * If both an `event_timestamp_column` and an `update_timestamp_column` are provided, the test will measure over time the difference between these two columns. ### Test configuration *Required configuration: `event_timestamp_column`* *Default configuration: `anomaly_direction: spike` to alert only on delays.* 
  
    data\_tests:
      -- elementary.event\_freshness\_anomalies:
        arguments:
          event\_timestamp\_column: column name
          update\_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
          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]
```yml Models theme={null} models: - name: < model name > data_tests: - elementary.event_freshness_anomalies: arguments: event_timestamp_column: < timestamp column > # Mandatory update_timestamp_column: < timestamp column > # Optional where_expression: < sql expression > time_bucket: # Daily by default period: < time period > count: < number of periods > ``` ```yml Models example theme={null} models: - name: login_events data_tests: - elementary.event_freshness_anomalies: arguments: event_timestamp_column: "occurred_at" update_timestamp_column: "updated_at" config: # optional - use tags to run elementary tests on a dedicated run tags: ["elementary"] # optional - change severity severity: warn ```