CSV Validation Automation

CSV File Validation Automation for Data Engineers

Does your company have data import scripts that nobody owns? Are there data file folder trees with duplicate files and naming nobody understands? Are you tired of spending hours every week debugging CSV files from vendors and writing custom validation scripts that break when partners change their formats?

You are not alone!

Instead of writing custom Python scripts for every data vendor, what if you could easily create new push-button data partner projects that automate every data feed exactly the same way, but with business rules validation to reduce manual effort and errors?

CsvPath Framework reduces CSV processing time up to 80% and catches data quality issues before they reach your database. Here's how it works:

Stop Writing Custom Validation Scripts

Before CsvPath (the old way):

# 47 lines of custom validation code for each vendor
import pandas as pd
import re
from datetime import datetime

def validate_vendor_a_csv(file_path):
    df = pd.read_csv(file_path)
    errors = []
    
    # Check required columns exist
    required_cols = ['customer_id', 'amount', 'date']
    missing_cols = [col for col in required_cols if col not in df.columns]
    if missing_cols:
        errors.append(f"Missing columns: {missing_cols}")
    
    # Validate customer_id format
    invalid_ids = df[~df['customer_id'].str.match(r'^[A-Z]{2}\d{6}$', na=False)]
    if not invalid_ids.empty:
        errors.append(f"Invalid customer IDs on rows: {invalid_ids.index.tolist()}")
    
    # Validate amounts are positive numbers
    invalid_amounts = df[df['amount'] <= 0]
    if not invalid_amounts.empty:
        errors.append(f"Invalid amounts on rows: {invalid_amounts.index.tolist()}")
    
    # Validate date format
    try:
        pd.to_datetime(df['saledate'])
    except:
        errors.append("Invalid date format")
    
    # ... 30+ more lines for edge cases, encoding, duplicates, etc.
    
    if errors:
        raise ValueError("\n".join(errors))
    return df

With CsvPath (the new way):

# 3 lines that work for any vendor
from csvpath import CsvPath  
csvpath = CsvPath()
results = csvpath.collect_paths(pathsname="vendor_rules", filename="customers")

Your validation rules file (vendor_rules):

$[*][
    regex(#customer_id, /^[A-Z]{2}\d{6}$/) and
    #amount > 0 
    date(#saledate, "MM/dd/yyyy")
]

Stop Manually Handling These Common Vendor CSV Problems

• Files with extra spaces, wrong date formats, or missing required fields - CsvPath automatically trims whitespace and validates data types

• Partners who change column order without warning - Rules work regardless of column position, reorder columns easily

• Encoding issues that crash your pandas scripts - Capture and handle issues without pipeline failure

• Data that looks fine but fails downstream validation - Catch schema mismatches before they hit your database

• Inconsistent header names - Map vendor variations to your standard field names

• Files that are sometimes empty or malformed - Graceful error handling with detailed reporting

• Set up CsvPath projects for new data vendors in seconds using the FlightPath Data app

Every Week You Delay Costs More Manual Hours

Many data engineers spend 3-5 hours per week on CSV issues that CsvPath automates in minutes. That's 150+ hours per year, per engineer. Get your first automated CSV validator running in under 10 minutes.

Already Trusted by Data Teams

CsvPath Framework is trusted by data teams from startups to regulated organizations.

• Distributed via PyPI, the Microsoft Store, and the Apple MacOS App Store - enterprise-ready

• 150+ built-in validation functions for common data quality checks

• Open source - dig below the surface, it's all there

• Community and paid support - get help when you need it

Ready to Automate Your CSV Chaos?

Try CsvPath Framework as a Python library or in the FlightPath Data app for MacOS or Windows.

→ Try the 5-Minute Quickstart

→ See More Real Examples (Complete solutions for common vendor scenarios)

→ View Full Documentation (Technical reference and advanced features)

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Questions? Check out our FAQ or join the discussion on GitHub.

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