anyvali

Create a Python-based data validation tool called 'SchemaGuardian' that leverages the 'anyvali' package for native validation and portable schema interchange. This tool will serve as a robust solution for validating data against predefined schemas, ensuring data integrity and consistency across different platforms and environments.

Step 1: Define the core functionality of SchemaGuardian. It should be able to load and parse schemas from various sources such as JSON files, YAML files, or even direct string inputs. The schemas should be portable, meaning they can be easily shared and used across different systems.

Step 2: Implement a feature that allows users to validate data entries against these schemas. The validation process should cover common data types such as integers, strings, dates, and custom objects. Additionally, it should support nested structures and arrays.

Step 3: Integrate error handling mechanisms into SchemaGuardian. When a data entry fails validation, the tool should provide clear and concise error messages that indicate which part of the schema was not met, along with suggestions on how to correct the issue.

Step 4: Extend SchemaGuardian's capabilities by adding support for conditional validations. For example, if a certain field is present, another field must meet specific criteria. This adds flexibility and power to the validation process.

Step 5: Develop a user-friendly command-line interface (CLI) for SchemaGuardian. Users should be able to easily load schemas, input data for validation, and view results through simple commands.

How 'anyvali' is utilized: The 'anyvali' package will be the backbone of SchemaGuardian, providing the necessary tools for defining, parsing, and validating schemas. Its ability to handle portable schemas makes it ideal for ensuring that the same validation rules can be applied consistently across different systems and platforms. Use 'anyvali' to define complex validation rules, perform type checks, and handle nested structures efficiently.