Loading and cleaning CSVs into a database with quality checks

The scenario takes a CSV, uses AI to detect and normalize columns and types, checks quality, loads clean rows into the database and reports errors.

  • Columns and types are normalized automatically
  • Clean data loads straight into the database
  • Problem rows get an error report
  • A ready notification lands in your chat
Webhook
Set it up for yourselfScriptera

How it works

To connect Postgres and pachca, you don't need a developer: a ready-made scenario links them in minutes.

  1. Starts when: CSV Upload Webhook
  2. Then: Workflow Configuration
  3. Check: Check File Type
  4. If yes: Extract CSV Data
  5. Then: Schema Inference & Header Normalization
  6. Then: Apply Normalization & Type Coercion
  7. Then: Validate Data Quality
  8. Then: Prepare Clean CSV Output
  9. Then: Insert into Postgres
  10. Then: Send Notification
  11. Then: Generate Error Report
  12. Then: Log to Google Sheets
  13. If no: Error - Unsupported File Type
  14. Then: Anthropic Chat Model
  15. Then: Structured Output Parser

You can launch this Postgres + pachca integration in Scriptera: describe the task in plain words — the scenario is built, launched and monitored for you.

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