Validate and Clean Data: Regex → Format JSON → Convert Case
Build a data validation pipeline: test field patterns with regex, format the validated JSON, and normalize text casing. Catch dirty data before it reaches your database.
The Problem
You're importing user data from a CSV or form submission and need to validate email formats, phone numbers, and postal codes before inserting into the database. Then you need to normalize name casing ("alice smith" → "Alice Smith") and verify the JSON payload structure.
Why This Matters
Data quality problems compound over time — invalid emails, inconsistently cased names, and malformed phone numbers cause bugs months later. A validation pipeline catches issues before they enter your system, saving expensive cleanup operations.
Step-by-Step Instructions
Write and test validation patterns
Use the Regex Tester to build patterns for each field type. Email: [a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}. US Phone: \(?\d{3}\)?[-.\s]\d{3}[-.\s]\d{4}. Test against real sample data including edge cases.
Validate the JSON payload structure
Paste the incoming JSON data into the JSON Formatter. Verify required fields are present, data types are correct, and no unexpected null or empty fields appear. Syntax errors in the JSON will be caught immediately.
Normalize text casing
Copy name fields or category labels into the Text Case Converter. Convert user names to Title Case, database enum values to snake_case, and display labels to Sentence case. Consistent casing prevents duplicate entries.
Try It Now — Regex Tester
Open full page →All processing happens in your browser — no data is sent to any server.
Before & After Example
{
"name": "alice SMITH",
"email": "alice@example",
"phone": "555 123 4567",
"role": "Admin User"
}
// email is invalid (no TLD)
// name casing inconsistent
// role should be snake_case for DB
{
"name": "Alice Smith",
"email": "[email protected]",
"phone": "555-123-4567",
"role": "admin_user"
}
// All fields pass validation
// Consistent formatting
Frequently Asked Questions
Should I validate on the frontend or backend?
Both. Frontend validation improves UX by giving immediate feedback. Backend validation is the security boundary — never trust client input. This workflow is for checking your backend validation logic before deploying it.
What's the most reliable email validation regex?
For basic validation: ^[^\s@]+@[^\s@]+\.[^\s@]+$. For RFC 5322 compliance, use a library like email-validator (Python) or validator.js (Node). Pure regex can't cover all valid email formats reliably.
How do I normalize casing in SQL?
Use LOWER() for storage and comparison, INITCAP() (PostgreSQL) for display. For application-level normalization, Python has str.title() and str.lower().
Related Workflows
Try all 3 tools in this workflow
Each tool is free, runs in your browser, and requires no signup.