DATA OPS

Weekly orphaned stale-table cleanup review

Weekly, finds warehouse tables that have been stale far beyond their SLA with no downstream consumers.

CategoryData Ops
Enginesim
Difficultyintermediate
Triggerschedule
Steps6
Setup~15 min

How it runs

The automated pipeline, trigger to output.

  • TriggerWeekly cleanup schedule
  • ActionFind tables stale past SLA windowGoogle BigQueryBigQuery
  • ActionCheck lineage + access logs for consumersGoogle BigQueryBigQuery
  • LogicRank orphaned decommission candidates
  • ActionCreate review page with evidenceNotionNotion
  • OutputLink review to platform teamSlack

What it does

Finds the cost-and-clutter problem hiding behind freshness alerts: tables that stopped updating long ago and that nothing downstream reads. It cross-references staleness against lineage consumption and query history, then compiles a decommission-candidate list for human review instead of auto-deleting anything.

When to use it

Use it for periodic warehouse hygiene when stale-table alerts accumulate for assets nobody actually depends on. It converts alert fatigue into a deliberate cleanup decision with an audit trail.

How it works

  1. 1A weekly schedule starts the review.
  2. 2A BigQuery query identifies tables stale well past their SLA window.
  3. 3A second query checks lineage edges and recent access logs to find which stale tables have zero downstream consumers and no recent reads.
  4. 4A logic step ranks candidates by storage footprint and days-since-last-read.
  5. 5A Notion page is created listing each candidate with evidence and an owner approval checkbox.
  6. 6A Slack message links the review page to the data platform team.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect BigQueryDatasets, queries, schemas.
  2. 2
    Connect NotionPages, databases, comments.
  3. 3
    Connect SlackChannels, DMs, threads, mentions.
  4. 4
    Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
  5. 5
    Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
  6. 6
    Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.

Run this workflow in your colony.

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