数据新鲜度检查工具 - 监控数据更新状态，确保数据可用性 | Astronomer Agents

checking-freshness by astronomer/agents

457 周安装量

294 GitHub Stars

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安装命令

npx skills add https://github.com/astronomer/agents --skill checking-freshness

数据分析开发运维数据处理

🇨🇳中文介绍

数据新鲜度检查

快速判断数据是否足够新鲜可用。

新鲜度检查流程

针对每个待检查的表：

1. 查找时间戳列

寻找指示数据加载或更新时间的列：

_loaded_at、_updated_at、_created_at（常见 ETL 模式）
updated_at、created_at、modified_at（应用时间戳）
load_date、etl_timestamp、ingestion_time

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2. 查询最后更新时间

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. 按时间检查行数

对于定期更新的表，检查近期活动：

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

使用以下等级报告状态：

状态	时间间隔	含义
新鲜	< 4 小时	数据是最新的
陈旧	4-24 小时	可能已过时，检查是否符合预期
非常陈旧	> 24 小时	除非是批处理作业，否则很可能有问题
未知	无时间戳	无法确定新鲜度

检查源管道的 Airflow：

查找 DAG：哪个 DAG 填充此表？使用 af dags list 并查找匹配的名称。
检查 DAG 状态：
- DAG 是否已暂停？使用 af dags get <dag_id>
- 最后一次运行是否失败？使用 af dags stats
- 当前是否有运行正在进行？
必要时进行诊断：如果 DAG 失败，使用 debugging-dags 技能进行调查。

如果您在 Astro 上运行，还可以：

Astro UI 中的 DAG 历史记录：检查部署的 DAG 运行历史记录，查看近期运行及其结果的可视化时间线
用于 SLA 监控的 Astro 警报：配置警报，以便在 DAG 错过预期完成窗口时收到通知，从而在用户报告之前发现数据陈旧问题

Airflow UI：使用 DAG 视图和任务日志来验证上次成功运行以及 SLA 未达标情况

提供清晰、易于浏览的报告：

新鲜度报告
============

表：database.schema.table_name
最后更新：2024-01-15 14:32:00 UTC
时间间隔：2 小时 15 分钟
状态：新鲜

表：database.schema.other_table
最后更新：2024-01-14 03:00:00 UTC
时间间隔：37 小时
状态：非常陈旧
源 DAG：daily_etl_pipeline（失败）
操作：使用 **debugging-dags** 技能进行调查

如果用户只想要是/否答案：

"X 新鲜吗？" -> 检查并用状态 + 一行说明回复
"我能在上午 9 点的会议中使用 X 吗？" -> 检查并给出明确的是/否答案及上下文

2026 年 1 月 23 日

🇺🇸English

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

Weekly Installs

374

Repository

astronomer/agents

GitHub Stars

269

First Seen

Jan 23, 2026

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