How to catch CSV import problems before they reach your database

Gabriel Fonseca dos Santos — Sat, 26 Sep 2026 00:44:17 +0000

CSV files can parse successfully and still be unsafe to import.

Uneven rows, missing fields, inconsistent values, and spreadsheet formula markers can all create problems later in a database, CRM, spreadsheet, or ETL pipeline.

I built Tabular Preflight API to add a validation step before the import happens.

The workflow is simple:

upload → preflight check → decision → import or review

The API analyzes a CSV and returns structured findings, scores, policy.failed, and a conservative safe_to_import verdict.

It does not modify the original CSV.

Example

A CSV containing suspicious spreadsheet formula markers can return:

{
  "safe_to_import": false,
  "policy": {
    "failed": true
  },
  "scores": {
    "structure": 100,
    "completeness": 100,
    "consistency": 100,
    "security": 30,
    "overall": 86
  },
  "findings": [
    {
      "code": "FORMULA_INJECTION_RISK",
      "severity": "critical",
      "dimension": "security",
      "occurrences": 2
    }
  ]
}

One important detail: HTTP 200 only means the analysis completed successfully.

It does not mean the CSV is safe to import.

A simple integration rule can look like this:

if not report["safe_to_import"] or report["policy"]["failed"]:
    print("Hold import")
else:
    print("Continue import")

This can be useful for:

CSV upload flows
ETL pipelines
CRM imports
admin dashboards
data ingestion systems

I’m currently looking for feedback from developers who deal with CSV imports and data pipelines.

Free tier available on RapidAPI:

https://rapidapi.com/gabrielfons35/api/tabular-preflight-api

DEV Community: Gabriel Fonseca dos Santos

How to catch CSV import problems before they reach your database

Example