DATA OPS

BigQuery freshness watchdog with downstream view quarantine

Checks expected partitions against an SLA clock on a schedule, and when a daily partition lands late it flips the dependent BigQuery views into a quarantine state and posts…

CategoryData Ops
Enginesim
Difficultyintermediate
Triggerschedule
Steps5
Setup~15 min

How it runs

The automated pipeline, trigger to output.

  • TriggerEvery 15 min during ingestion window
  • ActionRead latest partition per table from INFORMATION_SCHEMAGoogle BigQueryBigQuery
  • LogicCompare arrival vs SLA, isolate breached tables
  • ActionCREATE OR REPLACE VIEW to quarantine downstream viewsGoogle BigQueryBigQuery
  • OutputPost breach + affected views to data on-call SlackSlack

What it does

It watches the latest landed partition for each tracked BigQuery table, compares its arrival time to a per-table freshness SLA, and when a partition is missing past its deadline it quarantines every downstream view that reads from that table. Quarantine swaps the view definition to return zero rows plus a `_quarantined` flag, so dashboards visibly break instead of silently serving stale numbers.

When to use it

Use it when late-arriving ingestion silently poisons executive dashboards and you would rather fail loud than serve yesterday's data. Best for daily-partitioned fact tables feeding revenue, ops, or marketing reporting.

How it works

  1. 1A schedule fires every 15 minutes during the ingestion window.
  2. 2A BigQuery query reads `INFORMATION_SCHEMA.PARTITIONS` for each tracked table and returns the newest partition timestamp.
  3. 3A logic step compares each timestamp to the table's SLA deadline and isolates the breached tables.
  4. 4For breached tables, a BigQuery action runs `CREATE OR REPLACE VIEW` to quarantine the dependent views.
  5. 5A Slack message posts the breach list, affected views, and minutes-late to the data on-call channel.

Set it up

What you configure once, before turning it on.

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

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