DATA OPS
dbt Freshness Scorecard: Daily SLA Digest to Stakeholders
Each morning compiles a freshness scorecard across all tracked Snowflake marts, computes the prior-day SLA hit rate.
How it runs
The automated pipeline, trigger to output.
- TriggerWeekdays at 8am
- ActionAggregate 24h load-lag history per martSnowflake
- LogicCompute SLA hit rate and rank worst-first
- ActionPost ranked freshness scorecard to SlackSlack
- OutputAppend summary row to Notion reliability logNotion
What it does
Generates a once-daily reliability report instead of per-incident alerts. It pulls freshness history for every tracked mart, calculates how often each met its SLA over the last 24 hours, and produces a ranked scorecard, worst offenders first. The digest goes to Slack for visibility and a summary row is appended to a Notion reliability log for trend tracking over weeks.
When to use it
Use when leadership wants a steady pulse on data reliability rather than a firehose of alerts. It turns scattered freshness signals into a single accountable number the data team can be measured against.
How it works
- 1A schedule fires every weekday at 8am.
- 2A Snowflake query aggregates load-lag history per mart for the trailing 24 hours.
- 3A logic step computes each table's SLA hit rate and ranks them worst-first.
- 4A Slack message posts the ranked scorecard with the overall reliability percentage.
- 5A Notion row is appended to the reliability log to build the historical trend.
Set it up
What you configure once, before turning it on.
- 1Connect SnowflakeWarehouses, queries, shares.
- 2Connect SlackChannels, DMs, threads, mentions.
- 3Connect NotionPages, databases, comments.
- 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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Run this workflow in your colony.
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