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Validate CSV data against custom rules checking for required fields, format compliance, value ranges, and pattern matching. Data validation is critical for preventing bad data from entering systems, databases, and analyses, yet manual validation is impossible with large datasets. This tool enables rule-based validation with support for multiple rule types: required field checks ensure mandatory fields contain values, format validators verify email addresses and phone numbers, range validators check numeric bounds, and pattern validators match regular expressions. Define multiple rules per column and apply them to entire datasets automatically. Detailed error reports specify exact row, column, and violation information enabling targeted corrections. Perfect for pre-import validation, data quality gates, and compliance verification.
Validate CSV data before database or system import, preventing bad data entry and ensuring import success.
Automate data quality checks before release, validating test data meets quality standards and specifications.
Validate data before processing pipelines, catching errors early and preventing downstream failures.
Validate data against compliance rules and regulatory requirements, generating audit documentation.
Validate data against API contracts and system specifications, ensuring compatibility before integration.
Validate business rules like minimum order amounts, valid status values, and data consistency requirements.
Data validation is the systematic process of ensuring that data conforms to defined rules, constraints, and expectations before it enters systems where bad data can cause operational failures, incorrect analysis, or compliance violations. The concept of data integrity constraints is foundational to relational database theory, introduced by Edgar Codd and formalized through entity integrity, referential integrity, and domain integrity constraints. Extending these concepts to CSV files, which lack the built-in constraint enforcement of database systems, requires external validation tools.
Domain constraints define the set of acceptable values for each field. Simple domains specify a data type—integers, dates, email addresses—while more complex domains enumerate valid values (like country codes from ISO 3166) or define value ranges (ages between 0 and 150, percentages between 0 and 100). Regular expression patterns provide flexible domain definitions, matching values against syntactic templates: email addresses must match the RFC 5322 pattern, phone numbers must conform to E.164 or regional formats, and product codes must follow organization-specific schemas.
Required field validation, also known as NOT NULL constraints in database terminology, ensures that mandatory fields contain values. Missing values in critical fields—like customer identifiers, transaction amounts, or dates—can cascade into downstream failures: database imports may reject rows, calculations may produce incorrect results, and reports may omit records. Identifying which fields are mandatory and verifying their completeness is typically the first validation step in any data quality workflow.
Format validation goes beyond type checking to verify that values conform to specific syntactic patterns. A field typed as a date must not only contain a parseable date but must conform to the expected format, such as ISO 8601. Phone numbers must include country codes or follow regional formatting conventions. Postal codes must match the patterns used by the relevant country's postal system. These format requirements are often dictated by downstream systems that expect specific string patterns rather than just semantically valid values.
Cross-field validation examines relationships between columns within the same row. Start dates must precede end dates, subtotal plus tax must equal total, shipping addresses are required when delivery method is "ship," and management approval is required when purchase amounts exceed a threshold. These business rules cannot be validated by examining individual fields in isolation; they require evaluating logical expressions across multiple columns simultaneously. Validation reporting must be actionable, providing sufficient detail for efficient error correction—specifying exact row, column, and violation information rather than simply counting errors, enabling targeted remediation that prioritizes the most impactful corrections.
You can set rules for required fields, data format patterns (email, phone, date), numeric ranges (min/max), text length limits, and allowed value lists. Multiple rules can be applied to a single column.
Yes, the tool includes built-in format validators for common patterns like email addresses, phone numbers, URLs, and dates. These check that values conform to expected formats and flag invalid entries.
The error report lists violations grouped by column and rule type, showing the exact row number, the invalid value, and which rule it violated. You can export this report for further review or action.
The validation rules you define can be exported as a configuration. This allows you to apply the same validation rules to future CSV files, ensuring consistent data quality checks across your workflow.
All processing happens directly in your browser. Your files never leave your device and are never uploaded to any server.