/checking-freshness

name: checking-freshness
description: Quick data freshness check. Use when the user asks if data is up to date, when a table was last updated, if data is stale, or needs to verify data currency before using it.

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 4 hours | Data is current | | **Stale** | 4-24 hours | May be outdated, check if expected | | **Very Stale** | 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:

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:

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