Great Expectations Deequ And Cuallee

Overview

Use this skill when the team wants structured quality enforcement through a data-quality framework instead of ad hoc checks. It helps agents align contracts with expectation suites, route failures by severity, and produce reviewable validation evidence.

When to Use

• implementing Great Expectations expectation suites or checkpoints
• adding Deequ analyzers and constraints to Spark pipelines
• using Cuallee for lightweight PySpark or Pandas validation
• standardizing reusable quality checks across datasets
• integrating framework-based quality gates into CI/CD or publish workflows
• producing validation evidence for audits or release gates

Do not use this when a few inline assertions are sufficient or when the team has no plan to reuse checks across datasets.

Workflow