DATA OPS

Validate webhook-posted CSV and append clean rows to BigQuery

Accepts a CSV via HTTP webhook, validates it against a column contract, appends valid rows to a BigQuery table, and returns a rejection report listing every bad row to the caller.

CategoryData Ops
Enginesim
Difficultyintermediate
Triggerwebhook
Steps5
Setup~15 min

How it runs

The automated pipeline, trigger to output.

  • TriggerCSV posted to HTTP webhook endpointHTTP webhook
  • ActionParse CSV and validate against column contract
  • LogicPartition into accepted vs rejected rows
  • ActionAppend accepted rows to BigQuery tableGoogle BigQueryBigQuery
  • OutputReturn rejection report in webhook responseHTTP webhook

What it does

Exposes an HTTP endpoint that partners or internal services POST a CSV to. Each upload is validated against a defined column contract, valid rows are appended to a BigQuery table, and the endpoint responds synchronously with a structured report of which rows were rejected and why.

When to use it

Use it when you want a self-serve CSV intake that gives the uploader immediate feedback instead of failing silently downstream. Good for vendor portals, internal tools, or any integration where the sender needs to know exactly which rows to fix and resend.

How it works

  1. 1An HTTP webhook receives a POST containing the CSV payload.
  2. 2The pipeline parses the file and checks headers, types, and constraints (ranges, enums, non-null) row by row.
  3. 3A logic step partitions rows into accepted and rejected, tagging each rejected row with a reason.
  4. 4Accepted rows are streamed into the target BigQuery table via an insert job.
  5. 5The webhook response returns counts plus the full rejected-row report so the caller can correct and retry.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect HTTP webhookTrigger any URL on agent actions.
  2. 2
    Connect BigQueryDatasets, queries, schemas.
  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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