DATA OPS

CSV Feed Validation with Airtable Defect Tracker

On a schedule, pulls the latest partner CSV from Google Drive, validates it against a schema, loads clean rows to Airtable.

CategoryData Ops
Enginesim
Difficultyintermediate
Triggerschedule
Steps7
Setup~15 min

How it runs

The automated pipeline, trigger to output.

  • TriggerScheduled daily run
  • ActionDownload latest partner CSV from Google DriveGoogle DriveGoogle Drive
  • LogicValidate each row against contract schema
  • LogicBranch passing rows from failing rows
  • ActionUpsert clean rows into Airtable data tableAirtableAirtable
  • ActionCreate one defect record per bad row in AirtableAirtableAirtable
  • OutputPost run digest to SlackSlack

What it does

This workflow runs on a schedule, grabs the newest partner CSV from a Google Drive folder, and validates every row against your contract schema. Clean rows are written into an Airtable base; bad rows become individual defect records in a tracker table, tagged with the failure reason and the partner who sent them.

When to use it

Use it when an ops team owns data quality in Airtable and needs a human-friendly worklist of defects rather than a raw file dump. Each defect becomes a trackable record someone can assign, comment on, and close.

How it works

  1. 1A scheduled trigger fires (for example, every morning).
  2. 2The latest CSV in the watched Google Drive folder is downloaded.
  3. 3Each row is validated against the schema — required columns, formats, and allowed values.
  4. 4The flow branches passing rows from failing rows.
  5. 5Passing rows are upserted into the Airtable data table.
  6. 6Each failing row is created as a defect record in Airtable with the rule it broke and the source partner.
  7. 7A digest of the run is posted to Slack for the on-call data steward.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect Google DriveDocs, sheets, slides, files.
  2. 2
    Connect AirtableBases, tables, views, automations.
  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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