DATA OPS
Daily dbt Freshness SLA Scorecard to Notion
Each morning computes 24-hour SLA attainment per BigQuery dbt model and publishes a ranked scorecard to a Notion page for the data team's standup.
How it runs
The automated pipeline, trigger to output.
- TriggerDaily at 8am (schedule)
- ActionAggregate 24h SLA attainment per modelBigQuery
- LogicRank models, flag below-target attainment
- OutputUpsert scorecard table to Notion standup pageNotion
What it does
Produces a daily freshness scorecard instead of moment-to-moment alerts. It calculates, per watched BigQuery model, how often it met its SLA over the last 24 hours, ranks the worst offenders, and writes a clean table to a Notion page the team reviews at standup.
When to use it
Use this for the recurring reliability conversation — tracking whether freshness is trending better or worse week over week and which models repeatedly miss — rather than for real-time incident response.
How it works
- 1A schedule fires every morning at 8am.
- 2BigQuery aggregates the prior 24 hours of build timestamps into an SLA-attainment percentage per model.
- 3A logic step ranks models by attainment and flags any below the team target.
- 4The flow renders a Markdown scorecard table with attainment, breach count, and worst lag.
- 5It upserts the scorecard onto the team's Notion standup page, replacing yesterday's section.
Set it up
What you configure once, before turning it on.
- 1Connect BigQueryDatasets, queries, schemas.
- 2Connect NotionPages, databases, comments.
- 3Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
- 4Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
- 5Test, 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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