checking-freshness

Data Freshness Check

Quickly determine if data is fresh enough to use.

Freshness Check Process

For each table to check:

1. Find the Timestamp Column

Look for columns that indicate when data was loaded or updated:

• _loaded_at, _updated_at, _created_at (common ETL patterns)
• updated_at, created_at, modified_at (application timestamps)
• load_date, etl_timestamp, ingestion_time
• date, event_date, transaction_date (business dates)

Query INFORMATION_SCHEMA.COLUMNS if you need to see column names.

2. Query Last Update Time

SELECT
    MAX(<timestamp_column>) as last_update,
    CURRENT_TIMESTAMP() as current_time,
    TIMESTAMPDIFF('hour'

3. Check Row Counts by Time

For tables with regular updates, check recent activity:

Freshness Status

Report status using this scale:

If Data is Stale

Check Airflow for the source pipeline:

1. Find the DAG: Which DAG populates this table? Use af dags list and look for matching names.

2. Check DAG status:

   • Is the DAG paused? Use af dags get <dag_id>
   • Did the last run fail? Use af dags stats
   • Is a run currently in progress?

3. Diagnose if needed: If the DAG failed, use the debugging-dags skill to investigate.

On Astro

If you're running on Astro, you can also:

• DAG history in the Astro UI: Check the deployment's DAG run history for a visual timeline of recent runs and their outcomes
• Astro alerts for SLA monitoring: Configure alerts to get notified when DAGs miss their expected completion windows, catching staleness before users report it

On OSS Airflow

• Airflow UI: Use the DAGs view and task logs to verify last successful runs and SLA misses

Output Format

Provide a clear, scannable report:

Quick Checks

If user just wants a yes/no answer:

• "Is X fresh?" -> Check and respond with status + one line
• "Can I use X for my 9am meeting?" -> Check and give clear yes/no with context