Configure freshness alerts - Precisely Data Integrity Suite

Data Integrity Suite
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Spatial Analytics
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Data Integrity Suite
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2000
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2026

Data freshness measures how current and accurate your data is. Stale data can be outdated, incomplete, or inaccurate. Freshness rules generate alerts when data fails to update at expected frequencies, giving you time to refresh data before it is used for decisions. For example, if your sales data should update every three days and the update fails, the system alerts you immediately. You can configure either a confidence based alert or a threshold based alert.

Freshness alerts trigger when data fails to update at the expected frequency. You can choose between two alert types:
  • Confidence based alerts: Trigger based on how certain the system is that data failed to update. After five successful observer runs, the Observability Adaptive Model alerts you when it detects a failure with your desired confidence level. For more information, see Configure rule based alerts.
  • Threshold based alerts: Let you manually set limits to trigger alerts. You define a custom threshold for the expected update frequency. For example, if you set Freshness Frequency to three days, alerts trigger when the table fails to update every three days.

To configure freshness alerts:

  1. Go to Observability > Observers and select + Create Observer.
  2. On the Create Observer page, select your data assets and click Next.
  3. In the Freshness row, click the gear icon in the Configure column.
  4. Choose your alert type and configure settings:
    1. For confidence based alerts: Set your confidence level range by dragging the sliders or entering values in the text boxes.
    2. For threshold based alerts: Click the threshold based Alerts radio button, then in the Freshness Frequency field, enter a number or use the arrows to set the expected update schedule.
      Note: Threshold based alerts always generate as critical alerts. If freshness alerts don't generate as expected, verify the Observer scheduler. The Observer must run as frequently or more frequently than the freshness rule. For example, if freshness frequency is set to every four days but the Observer runs every seven days, it won't detect update failures.
  5. Click Save.

Datasource-specific considerations

Databricks
  • Enable the DESCRIBE HISTORY feature in Delta Lake to capture freshness alerts. This command reads the operation log and returns table history. Databricks stores last modified information for up to 30 days only, so monthly interval alerts generate only for the first 30 days. For more information, see DESCRIBE HISTORY.
  • Only WRITE, UPDATE, and DELETE DML operations calculate the last modified time. Execute this SQL query to retrieve data:

    select max(timestamp) as PRECISELY_VALUE from (DESCRIBE HISTORY <database_name><schema_name><table_name>) where operation='WRITE' or operation='UPDATE' or operation='DELETE'

    Supported Operations

    • WRITE: A new write operation to the table.

    • MERGE: A merge operation to perform upserts (insert, update, delete).

    • DELETE: A delete operation.

    • UPDATE: An update operation.

    • OPTIMIZE: Optimizes the table by performing compaction on small files.

    • VACUUM: Cleans up older data files that are no longer needed.

    Supported Versions for Describe History

    Databricks Runtime 7.x Delta Lake 0.8.x Supports DESCRIBE HISTORY and basic Delta Lake functionality.
    Databricks Runtime 8.x Delta Lake 1.0+ Supports DESCRIBE HISTORY, time travel, and Delta optimizations.
    Databricks Runtime 9.x+ Delta Lake 1.0+ Full support for DESCRIBE HISTORY, time travel, schema evolution, and performance improvements.
Redshift
  • Redshift stores last modified table information for up to seven days only. Freshness alerts generate only for daily and weekly intervals.
  • The Redshift user must be a superuser or have SELECT permission on the SVV_TABLE_INFO table. For more information, see SVV_TABLE_INFO.
  • Only DML operations calculate last modified time. DDL query operations do not generate freshness alerts.
Snowflake
Use the LAST_ALTERED column to get table-level freshness. This supports Add, Delete, and Update operations. Execute this SQL query to retrieve freshness information:

SELECT TO_TIMESTAMP(LAST_ALTERED) AS PRECISELY_VALUE, NULL AS PRECISELY_COMMENT FROM INFORMATION_SCHEMA.TABLES WHERE TABLE_SCHEMA = <schema_name> AND TABLE_NAME = <table_name> UNION ALL SELECT NULL AS PRECISELY_VALUE, 'table or view does not exist' AS PRECISELY_COMMENT WHERE NOT EXISTS (SELECT 1 FROM INFORMATION_SCHEMA.TABLES WHERE TABLE_SCHEMA = <schema_name> AND TABLE_NAME = <table_name>)

The query returns the LAST_ALTERED date, which tracks the freshness metric.