data-validation

Validate data schemas and quality using Pandera and Great Expectations.

Updated May 9, 2026

Search Tags:#data quality #data validation #schema validation #Great Expectations #Pandera

Ready to run

npx skills add https://github.com/LeandroBenjaminL/lend-ai --skill data-validation-leandrobenjaminl

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: data-validation
Source: https://github.com/LeandroBenjaminL/lend-ai/tree/main/skills/data-validation
Command: npx skills add https://github.com/LeandroBenjaminL/lend-ai --skill data-validation-leandrobenjaminl

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pandera, greatexpectations, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill addresses the challenge of validating data schemas and ensuring data quality, providing a robust solution for maintaining high data integrity across your projects.

Core Features & Use Cases

Data Schema Validation: Validates data against predefined schemas using Pandera and Great Expectations.
Quality Assurance: Assures that the data meets specified quality standards before entering a production pipeline.
Use Case: When integrating data from multiple sources and need to ensure the integrity and reliability of the data for analysis or processing.

Quick Start

Run the 'validate_data' command to check the integrity of your dataset against the 'data-schema.yaml' file.

Frequently Asked Questions about data-validation

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I validate data schemas before processing in production pipelines?▼

Data schema validation checks dataset integrity against predefined schemas using Pandera and Great Expectations to ensure data quality before production. This prevents bad data from entering integration or transformation pipelines.

How does data quality assurance work with Great Expectations and Pandera?▼

Data quality assurance with Great Expectations and Pandera validates datasets against predefined rules in a data-schema.yaml file. Running the validate_data command checks schema adherence and quality standards to maintain data integrity.

Can I use Pandera and Great Expectations together for data integration tasks?▼

Yes, you can use Pandera and Great Expectations together for data integration tasks. They validate schemas and enforce quality standards, ensuring reliable data from multiple sources before analysis or processing.

What is the best way to ensure data integrity when integrating multiple data sources?▼

The best way to ensure data integrity when integrating multiple data sources is validating data against predefined schemas. Using Pandera and Great Expectations confirms data meets specified quality standards before entering production pipelines.

Do I need Pandera and Great Expectations installed to run schema validation checks?▼

Yes, you need Pandera and Great Expectations installed to run schema validation checks. These dependencies are required to execute the validate_data command and enforce data quality standards.

Why does data validation fail when checking datasets against a predefined schema?▼

Data validation fails when datasets do not adhere to the predefined schema rules defined in the data-schema.yaml file. Running validate_data identifies mismatches to prevent unreliable data from entering production pipelines.