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Data Validation Toolkit
Pydantic models, custom validators, schema evolution patterns, and data quality frameworks for Python applications.
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data-validation-toolkit/
├── LICENSE
├── README.md
├── configs/
│ ├── rules/
│ │ └── business_rules.yaml
│ └── schemas/
│ └── user_schema.yaml
├── examples/
│ └── validate_user_data.py
├── guides/
│ └── data-validation-guide.md
├── src/
│ ├── decorators.py
│ ├── pipeline.py
│ ├── reporters.py
│ └── validators/
│ ├── base.py
│ ├── business_rules.py
│ ├── file_validator.py
│ ├── schema_validator.py
│ └── type_validator.py
└── tests/
└── test_validators.py
📖 Documentation Preview README excerpt
Data Validation Toolkit — Composable Validation for Python
Type-safe, pipeline-driven validation with schema support, business rules, and detailed error reporting.
What You Get
- 5 validator types — base, type, schema, business rules, and file validators
- Composable pipeline — chain validators in sequence with short-circuit or collect-all modes
- YAML-driven schemas — define validation rules in config, not code
- Rich error reports — JSON, plain text, or structured output with field paths
- Function decorators — validate arguments automatically on function call
File Tree
data-validation-toolkit/
├── README.md
├── manifest.json
├── LICENSE
├── src/
│ ├── validators/
│ │ ├── base.py # Abstract base + ValidationError
│ │ ├── type_validator.py # Runtime type checking
│ │ ├── schema_validator.py # YAML-driven schema validation
│ │ ├── business_rules.py # Configurable business rules
│ │ └── file_validator.py # File existence, size, format
│ ├── pipeline.py # Validation pipeline orchestrator
│ ├── reporters.py # Error formatting & reporting
│ └── decorators.py # @validate_args decorator
├── configs/
│ ├── schemas/
│ │ └── user_schema.yaml # Example schema definition
│ └── rules/
│ └── business_rules.yaml # Example business rules
├── examples/
│ └── validate_user_data.py # End-to-end usage example
├── tests/
│ └── test_validators.py # Comprehensive test suite
└── guides/
└── data-validation-guide.md
Getting Started
Basic type validation
from src.validators.type_validator import TypeValidator
validator = TypeValidator({"name": str, "age": int, "active": bool})
errors = validator.validate({"name": "Alice", "age": "thirty", "active": True})
# errors[0].field == "age", errors[0].message == "Expected int, got str"
Schema validation from YAML
from src.validators.schema_validator import SchemaValidator
validator = SchemaValidator.from_yaml("configs/schemas/user_schema.yaml")
*... continues with setup instructions, usage examples, and more.*
📄 Code Sample .py preview
src/validators/base.py"""Base validator and shared error types.
All validators inherit from :class:`Validator` and return a list of
:class:`ValidationError` instances.
"""
from __future__ import annotations
from abc import ABC, abstractmethod
from dataclasses import dataclass, field
from enum import Enum, auto
from typing import Any
class Severity(Enum):
"""Error severity level."""
ERROR = auto()
WARNING = auto()
INFO = auto()
@dataclass(frozen=True)
class ValidationError:
"""A single validation failure.
Attributes:
field: Dot-separated path to the offending field (e.g. ``"address.zip"``).
message: Human-readable description.
code: Machine-readable error code (e.g. ``"type_mismatch"``).
severity: How serious the violation is.
value: The offending value (optional, for diagnostics).
"""
field: str
message: str
code: str = "validation_error"
severity: Severity = Severity.ERROR
value: Any = field(default=None, repr=False)