data_freshness_sla

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elementary.data_freshness_sla

Verifies that data in a model was updated before a specified SLA deadline time.

This test checks the maximum timestamp value of a specified column in your data to determine whether the data was actually refreshed before your deadline. Unlike freshness_anomalies (which uses z-score based anomaly detection as a dbt test, or ML-based detection in Elementary Cloud), this test validates against a fixed, explicit SLA time, making it ideal when you have a concrete contractual or operational deadline.

Unlike execution_sla (which only checks if the dbt model ran on time), data_freshness_sla checks whether the actual data is fresh. A pipeline can run successfully but still serve stale data if, for example, an upstream source didn’t update. This test catches that.

Use Case

“Was the data in my model updated before 7 AM Pacific today?”

Test Logic

1. If today is not a scheduled check day → PASS (skip)
2. Query the model for the maximum value of timestamp_column
3. If the max timestamp is from today → PASS (data is fresh)
4. If the SLA deadline hasn’t passed yet → PASS (still time)
5. If the max timestamp is from a previous day → FAIL (DATA_STALE)
6. If no data exists in the table → FAIL (NO_DATA)

Test configuration

Required configuration: timestamp_column, sla_time, timezone

data_tests:
  — elementary.data_freshness_sla:
    arguments:
      timestamp_column: column name # Required - timestamp column to check for freshness
      sla_time: string # Required - e.g., “07:00”, “7am”, “2:30pm”, “14:30”
      timezone: string # Required - IANA timezone name, e.g., “America/Los_Angeles”
      day_of_week: string | array # Optional - Day(s) to check: “Monday” or [“Monday”, “Wednesday”]
      day_of_month: int | array # Optional - Day(s) of month to check: 1 or [1, 15]
      where_expression: sql expression # Optional - filter the data before checking

Models

models:
  - name: < model name >
    data_tests:
      - elementary.data_freshness_sla:
          arguments:
            timestamp_column: < column name > # Required
            sla_time: < deadline time > # Required - e.g., "07:00", "7am", "2:30pm"
            timezone: < IANA timezone > # Required - e.g., "America/Los_Angeles"
            day_of_week: < day or array > # Optional
            day_of_month: < day or array > # Optional
            where_expression: < sql expression > # Optional

Features

• Data-level freshness: Checks actual data timestamps, not just pipeline execution time
• Flexible time formats: Supports "07:00", "7am", "2:30pm", "14:30", and other common formats
• IANA timezone support: Uses standard timezone names like "America/Los_Angeles", "Europe/Amsterdam", etc.
• Automatic DST handling: Uses pytz for timezone conversions with automatic daylight saving time handling
• Database-agnostic: All timezone logic happens at compile time
• Schedule filters: Optional day_of_week and day_of_month parameters to check only specific days
• Filter support: Use where_expression to check freshness of a specific subset of data

Parameters

Parameter	Required	Description
timestamp_column	Yes	Column name containing timestamps to check for freshness
sla_time	Yes	Deadline time (e.g., "07:00", "7am", "2:30pm")
timezone	Yes	IANA timezone name (e.g., "America/Los_Angeles")
day_of_week	No	Day(s) to check: "Monday" or ["Monday", "Wednesday"]
day_of_month	No	Day(s) of month to check: 1 or [1, 15]
where_expression	No	SQL expression to filter the data before checking

Comparison with other freshness tests

Feature	data_freshness_sla	freshness_anomalies	execution_sla
What it checks	Actual data freshness (timestamps in the data)	Actual data freshness (timestamps in the data)	Pipeline execution (did the model run?)
Detection method	Fixed SLA deadline	Z-score (dbt test) / ML (Cloud)	Fixed SLA deadline
Best for	Contractual/operational deadlines on data	Detecting unexpected delays in data updates	Ensuring the pipeline itself ran on time
Works with sources	Yes	Yes	No (models only)

Notes

• The timestamp_column values are assumed to be in UTC (or timezone-naive timestamps that represent UTC). If your data stores local timestamps, the comparison may be incorrect.
• If both day_of_week and day_of_month are set, the test uses OR logic (checks if either matches)
• The test passes if the SLA deadline hasn’t been reached yet, giving your data time to be updated