DATA OPS

BigQuery export with size-based routing and dual approval for large pulls

Runs a BigQuery export and routes it by row count: small masked pulls auto-deliver to Dropbox, large pulls require a second data-owner approval in Slack first.

CategoryData Ops
Enginesim
Difficultyadvanced
Triggerwebhook
Steps6
Setup~25 min

How it runs

The automated pipeline, trigger to output.

  • TriggerExport request received via webhookHTTP webhook
  • ActionRun query in BigQueryGoogle BigQueryBigQuery
  • LogicMask PII columns
  • LogicRoute by row count vs threshold; large pulls need approvalSlack
  • ActionWrite masked file to DropboxDropboxDropbox
  • OutputConfirm delivery or log rejection in SlackSlack

What it does

Applies risk-based controls to exports. After masking PII, it checks the result size: small extracts under a threshold are delivered to Dropbox automatically, while large extracts that could constitute a bulk data movement are held for an additional data-owner approval in Slack before release.

When to use it

Use it when your policy treats bulk exports as higher risk than small lookups. It lets routine small pulls flow without friction while ensuring large data movements always get a human gate.

How it works

  1. 1A form webhook receives the export request.
  2. 2BigQuery runs the query and returns the result set.
  3. 3A masking step redacts PII columns.
  4. 4A logic branch compares the row count to the configured threshold.
  5. 5Below threshold: the masked file is written straight to Dropbox.
  6. 6At or above threshold: Slack requests a data-owner approval and only delivers to Dropbox on approval.
  7. 7A final Slack message confirms delivery or records the rejection.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect BigQueryDatasets, queries, schemas.
  2. 2
    Connect DropboxFiles and folders.
  3. 3
    Connect SlackChannels, DMs, threads, mentions.
  4. 4
    Connect HTTP webhookTrigger any URL on agent actions.
  5. 5
    Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
  6. 6
    Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
  7. 7
    Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.

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