DATA OPS

Auto-release quarantined BigQuery views when backfill lands

Polls quarantined tables for the missing partition and, the moment a complete backfill is detected.

CategoryData Ops
Enginesim
Difficultyintermediate
Triggerschedule
Steps6
Setup~15 min

How it runs

The automated pipeline, trigger to output.

  • TriggerEvery 5 min while quarantine is active
  • ActionRead quarantine ledger and saved view SQLPostgreSQLPostgres
  • ActionCheck awaited partition existence and row countGoogle BigQueryBigQuery
  • LogicConfirm backfill is present and complete
  • ActionRestore original view definitions to lift quarantineGoogle BigQueryBigQuery
  • OutputMark ledger resolved and post all-clear to SlackSlack

What it does

This is the recovery half of the watchdog. It tracks which tables and views are currently quarantined, repeatedly checks BigQuery for the late partition to arrive, validates that the row count clears a minimum-completeness threshold, then restores the saved original view definitions and clears the quarantine flag.

When to use it

Use it alongside a quarantine workflow so operators never have to manually un-break dashboards after a delayed ingest finally catches up. Ideal when backfills land at unpredictable times overnight.

How it works

  1. 1A schedule fires every 5 minutes while any view is in quarantine.
  2. 2A Postgres action reads the quarantine ledger to get the tables and saved original view SQL.
  3. 3A BigQuery action checks the awaited partition's existence and row count.
  4. 4A logic step confirms the partition is present and complete enough to release.
  5. 5A BigQuery action runs `CREATE OR REPLACE VIEW` with the original definitions to lift quarantine.
  6. 6A Postgres action marks the ledger rows resolved, and a Slack message posts the all-clear with recovery latency.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect BigQueryDatasets, queries, schemas.
  2. 2
    Connect PostgresAny Postgres URL — query, write, migrate.
  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.

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