DATA OPS
Daily Warehouse Freshness Scorecard Digest
Each morning, scores the freshness of every monitored Snowflake and BigQuery table, builds a single health digest.
How it runs
The automated pipeline, trigger to output.
- TriggerDaily morning schedule
- ActionCollect Snowflake last-load timesSnowflake
- ActionCollect BigQuery last-load timesBigQuery
- LogicGrade tables + compute health score
- OutputPost freshness scorecard to SlackSlack
What it does
Produces one consolidated freshness scorecard across both warehouses every morning. It checks last-load times for each monitored table in Snowflake and BigQuery, grades each as on-time, late, or stale against its SLA, and rolls the results into a digest showing an overall health percentage plus a sorted list of laggards. The digest lands in Slack as a routine standup artifact rather than a reactive page.
When to use it
Use this when leadership and analysts want a proactive daily pulse on data health instead of finding out about staleness only when a report looks wrong. It complements real-time alerts with a calm, scannable summary.
How it works
- 1A daily morning schedule triggers the run.
- 2A Snowflake query collects last-load times for its monitored tables.
- 3A BigQuery query collects last-load times for its monitored tables.
- 4A logic step grades every table and computes an overall freshness score.
- 5A Slack digest posts the score and the ranked list of late or stale tables.
Set it up
What you configure once, before turning it on.
- 1Connect SnowflakeWarehouses, queries, shares.
- 2Connect BigQueryDatasets, queries, schemas.
- 3Connect SlackChannels, DMs, threads, mentions.
- 4Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
- 5Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
- 6Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.
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This workflow drops into a full company template. Import the org, and this is one of the playbooks its agents run.
Run this workflow in your colony.
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