Data Freshness Check Skill

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', MAX(<timestamp_column>), CURRENT_TIMESTAMP()) as hours_ago,
    TIMESTAMPDIFF('minute', MAX(<timestamp_column>), CURRENT_TIMESTAMP()) as minutes_ago
FROM <table>

3. Check Row Counts by Time

For tables with regular updates, check recent activity:

SELECT
    DATE_TRUNC('day', <timestamp_column>) as day,
    COUNT(*) as row_count
FROM <table>
WHERE <timestamp_column> >= DATEADD('day', -7, CURRENT_DATE())
GROUP BY 1
ORDER BY 1 DESC

Freshness Status

Report status using this scale:

| Status | Age | Meaning | |--------|-----|---------| | Fresh | < 4 hours | Data is current | | Stale | 4-24 hours | May be outdated, check if expected | | Very Stale | > 24 hours | Likely a problem unless batch job | | Unknown | No timestamp | Can't determine freshness |

If Data is Stale

Check Airflow for the source pipeline:

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

FRESHNESS REPORT
================

TABLE: database.schema.table_name
Last Update: 2024-01-15 14:32:00 UTC
Age: 2 hours 15 minutes
Status: Fresh

TABLE: database.schema.other_table
Last Update: 2024-01-14 03:00:00 UTC
Age: 37 hours
Status: Very Stale
Source DAG: daily_etl_pipeline (FAILED)
Action: Investigate with **debugging-dags** skill

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

Agent Skills: Data Freshness Check

Install this agent skill to your local

Skill Files