Introduction

DataGuard is a Python library for efficient and flexible data validation, ensuring data integrity with built-in and custom validation rules. Perfect for pipelines, apps, and datasets.

DataGuard is a powerful and flexible Python library designed to streamline data validation processes. Whether you're building data pipelines, developing web applications, or handling complex datasets, DataGuard offers a comprehensive suite of tools to ensure your data is clean, consistent, and reliable.

Key Features

Comprehensive Rule Set:

  • Validate data with a wide range of built-in rules, including checks for required fields, conditional presence, format validation, and more.

  • Examples include rules for ensuring fields are present, validating email formats, checking numeric ranges, and enforcing unique constraints.

Custom Validators:

  • Easily create and integrate custom validation rules tailored to your specific needs.

  • Extend the library with your own validation logic to handle any specific data requirements.

Chainable Validation:

  • Build complex validation logic by chaining multiple rules together for more nuanced data integrity checks.

  • Combine rules like Required, Min, and Email in a single, readable chain to enforce multiple conditions on a single field.

Detailed Error Reporting:

  • Generate clear, actionable error messages that help you quickly identify and resolve data issues.

  • Each validation failure is accompanied by descriptive messages indicating the nature of the error and the affected data fields.

Ease of Use:

  • Designed with simplicity in mind, DataGuard's intuitive API allows you to validate data with minimal code.

  • Quickly set up validations using a declarative syntax that integrates seamlessly into your Python projects.

Highly Extensible:

  • Flexible architecture that integrates seamlessly with other libraries and frameworks, making it ideal for use in a variety of projects.

  • Whether you're working with Flask, Django, or standalone scripts, DataGuard adapts to your environment.

Overview

  • Purpose: Streamline data validation across different types of projects.

  • Flexibility: Offers a robust set of built-in rules and the ability to create custom validators.

  • Integration: Easily integrates with various Python frameworks and environments.

  • User-Friendly: Simplifies data validation with an intuitive and chainable API.

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