Sarah from Finance receives Excel files quarterly. But her real nightmare isn't the validation—it's what happens next. The sales data needs to flow into their new CRM system, which expects integers for customer IDs, proper decimals for amounts, and standardized dates. Half the migration fails silently. Customer "00123" becomes null. Amounts like "$1,234.50" crash the import. Dates in "12/25/2023" format get rejected.
Sound familiar? You're not just validating data—you're gambling with migration success.
The "What If" Moment
What if you could know, with mathematical precision, whether your messy Excel data can be safely converted to proper database types? What if you could identify exactly which records will cause migration failures before you even start the process?
This frustration led me to build ValidateLite, but not just another CSV validator. I built something different: a data conversion prediction engine that answers the question every data engineer asks: "Will this migration actually work?"
Beyond Basic Data Validation for Excel
Traditional CSV validator tools check syntax. ValidateLite's schema command goes deeper—it validates whether your data can transform from its current messy state into the clean types your systems actually need.
Here's what makes it revolutionary: the Desired Type feature.
vlite schema --conn messy_data.csv --rules conversion_schema.jsonSample schema (conversion_schema.json):
{
"rules": [
{
"field": "user_id",
"type": "string",
"desired_type": "integer",
"required": true
},
{
"field": "salary",
"type": "string",
"desired_type": "float(10,2)",
"required": true
},
{
"field": "join_date",
"type": "string",
"desired_type": "datetime('YYYY-MM-DD HH:MI:SS')",
"required": true
}
]
}This isn't just data validation for Excel files—it's migration feasibility analysis. ValidateLite tells you exactly which records can be converted successfully and which ones will break your ETL pipeline.
The Conversion Prediction Engine
The magic happens in ValidateLite's type compatibility analysis. Instead of discovering conversion failures during migration, you get a detailed preview:
Type Conversion Analysis Report
===============================
Field: user_id (string → integer)
Status: âś… COMPATIBLE (500/500 records can convert)
Field: salary (string → float(10,2))
Status: ⚠️ PARTIAL (487/500 records can convert)
Issues: 13 records contain currency symbols
Field: join_date (string → datetime)
Status: ❌ INCOMPATIBLE (156/500 records fail)
Issues: Multiple date formats detectedThis is what Sarah needed all along—not just validation, but conversion certainty.
Schema-Driven Validation Architecture
ValidateLite's schema command operates on a fundamentally different principle than traditional CSV validator approaches. While basic tools check individual rules, the schema command validates entire data transformation workflows.
Multi-table conversion validation:
{
"tables": [
{
"name": "customers",
"fields": [
{
"field": "customer_id",
"type": "string(50)",
"desired_type": "integer",
"required": true
},
{
"field": "registration_date",
"type": "string",
"desired_type": "date('YYYY-MM-DD')",
"required": true
}
]
},
{
"name": "orders",
"fields": [
{
"field": "amount",
"type": "string",
"desired_type": "float(12,2)",
"required": true
}
]
}
]
}Run comprehensive data validation for Excel and database migrations across multiple tables:
vlite schema --conn "mysql://user:pass@host:3306/legacy_db" --rules migration_analysis.jsonThe system analyzes conversion feasibility across your entire data model, identifying bottlenecks before they impact production.
Precision Type Definition System
ValidateLite's type system provides surgical precision for conversion validation:
String type definitions:
string(100)- Maximum 100 charactersstring(10,50)- Length between 10-50 characters
Decimal type definitions:
float(10,2)- Precision 10, scale 2 decimal placesfloat(15,4)- High-precision financial calculations
DateTime type definitions:
datetime('YYYY-MM-DD HH:MI:SS')- Specific format requirementsdate('MM/DD/YYYY')- Regional date format validation
This precision lets you validate not just whether data converts, but whether it fits your exact schema requirements.
Real-World Migration Scenarios
Legacy system modernization:
Your legacy database stores everything as VARCHAR. Before migrating to a modern schema, ValidateLite's schema command analyzes conversion success rates:
{
"rules": [
{
"field": "legacy_amount",
"type": "string",
"desired_type": "float(12,2)",
"required": true
},
{
"field": "legacy_timestamp",
"type": "string",
"desired_type": "datetime('YYYY-MM-DD HH:MI:SS')",
"required": true
}
]
}Excel to database migration:
Financial reports come as Excel files but need to populate PostgreSQL tables. The schema command validates whether Excel data matches database column constraints:
vlite schema --conn quarterly_report.xlsx --table "Revenue" --rules postgres_schema.jsonAPI data integration:
External APIs send data in various formats. Before building integration pipelines, validate whether incoming data can be safely converted to your internal types.
The Architecture Advantage
ValidateLite's schema command uses smart conversion validation strategies:
String to Numeric Conversion:
- Removes common formatting (spaces, commas, currency symbols)
- Handles scientific notation
- Validates decimal precision and scale
- Checks for overflow conditions
String to DateTime Conversion:
- Attempts multiple common date formats
- Validates actual date values (no February 31st)
- Handles timezone considerations
- Checks for impossible dates
Type Compatibility Matrix:
The system maintains a comprehensive compatibility matrix, understanding which conversions are safe, risky, or impossible.
The Vision: Migration Confidence as a Service
ValidateLite's current CLI capabilities are just the foundation. I'm building toward comprehensive migration planning tools:
Frontend #1: Migration Planning SaaS
Picture a web application where you upload your legacy data files and define your target schema. The platform uses ValidateLite's schema command to generate detailed migration feasibility reports, complete with:
- Conversion success probability for each field
- Sample problematic records with suggested fixes
- Cost estimates for data cleanup efforts
- Timeline projections based on data quality
The web interface would visualize conversion bottlenecks, helping teams prioritize cleanup efforts and plan migration phases.
Frontend #2: Excel Schema Plugin
The ultimate integration: an Excel plugin that brings ValidateLite's schema validation directly into spreadsheet workflows. Data analysts could define desired types visually, see real-time conversion feasibility, and export schema definitions for downstream systems.
The plugin would highlight cells that would fail conversion, suggest data cleanup strategies, and validate schema changes as users modify data—transforming Excel from a data quality liability into a migration planning tool.
Technical Implementation Strategy
Both frontends leverage ValidateLite's core schema validation engine. The web platform sends user data to /api/v1/schema/validate endpoints, while the Excel plugin uses Office.js to read spreadsheet data and validate it against user-defined schemas.
This architecture ensures consistency—whether you're planning a database migration through the web interface or validating Excel data through the plugin, you're using the same conversion logic that powers the CLI.
Why Schema Validation Changes Everything
Most data validation for Excel solutions operate on a simple premise: check if data matches current requirements. ValidateLite's schema command operates on a more sophisticated principle: validate transformation feasibility.
This distinction matters because modern data workflows aren't static. Data constantly moves between systems, formats, and schemas. Traditional CSV validator tools tell you about data today. ValidateLite tells you about data tomorrow.
Predictable means knowing conversion success rates before starting migration projects. No more discovering type conversion failures during production deployments.
Strategic means making informed decisions about data cleanup priorities. Instead of blindly fixing all quality issues, focus on problems that actually block critical conversions.
Scalable means validating entire data transformation pipelines, not just individual files. Ensure your ETL workflows won't break before building them.
The First Principles Approach
Traditional data validation for Excel workflows ask: "Is this data correct?" ValidateLite's schema command asks: "Can this data become what we need it to be?"
This reframe transforms data quality from reactive cleanup to proactive planning. Instead of discovering migration failures during deployment, you identify conversion bottlenecks during design.
The Community Experiment: Beyond CSV Validator Tools
This brings me to the crucial question: does this approach solve real problems you face?
Most CSV validator tools handle syntax checking adequately. But conversion prediction? Schema-based migration planning? Precise type compatibility analysis? This is largely uncharted territory.
I need your experiences to validate this direction. Do you face data migration challenges that existing tools don't address? Would conversion feasibility analysis change how you approach system integrations?
Experience the difference. Install ValidateLite and run the schema command against your real migration data:
pip install validatelite
# Create a simple schema file
echo '{
"rules": [
{
"field": "your_field",
"type": "string",
"desired_type": "integer",
"required": true
}
]
}' > test_schema.json
# Test conversion prediction
vlite schema --conn your_data.csv --rules test_schema.json --verboseDoes it reveal conversion issues you didn't expect? Does the detailed analysis help plan cleanup efforts?
Share your migration stories. What data transformation challenges does this approach not address? Which schema validation features would provide immediate value for your team?
Join the technical development. The GitHub repository showcases the schema command's architecture. The conversion prediction engine uses sophisticated type compatibility analysis—if this technical approach resonates with your experience, contribute improvements or star the project.
The Migration Revolution
Data validation for Excel and CSV files shouldn't be about policing current quality. It should be about enabling future transformations. ValidateLite's schema command represents a shift from reactive validation to proactive conversion planning.
The tools exist. The type system is sophisticated. The conversion prediction works. What remains is community validation that this approach solves real migration challenges.
Your feedback determines whether ValidateLite evolves from a personal solution to an industry standard. Whether that Excel schema plugin gets built. Whether the migration planning SaaS platform launches.
Because Sarah from Finance shouldn't discover migration failures during quarterly imports. She should plan successful conversions before they start.
That's the future ValidateLite's schema command enables. Help validate whether it's a future worth building.