great-expectations-deequ-and-cuallee

name	great-expectations-deequ-and-cuallee
description	Guides agents through data-quality frameworks such as Great Expectations, Deequ, and Cuallee. Use when implementing framework-based validation suites, reusable checks, or evidence-driven data-quality enforcement.

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

1. Ground the validation suite in a real contract.

   • start from the dataset contract: grain, freshness, allowed nulls, value ranges
   • do not invent checks that are not tied to a documented expectation
   • map each contract field to one or more framework checks
   • classify checks by severity: critical (blocks publish), warning (alert only), informational

2. Choose the right framework for the runtime context.

   • Great Expectations: best for warehouse/lake validation with rich documentation output
   • Deequ: best for Spark pipelines with compile-time constraint definitions
   • Cuallee: best for lightweight validation in PySpark or Pandas without heavy setup
   • consider execution environment: batch, streaming micro-batch, or CI tests
   • avoid framework lock-in by keeping contract definitions separate from framework syntax

3. Design expectation suites with maintenance in mind.

   • organize expectations by dataset and domain, not by framework capability
   • keep suites small and focused — one suite per dataset or model output
   • parameterize thresholds so they can be adjusted without code changes
   • version suites alongside the pipeline code that produces the data
   • document when and why each expectation was added

4. Integrate validation into the pipeline lifecycle.

   • run validation after transformation, before publish or downstream consumption
   • critical failures must block publish and trigger alerts
   • warning failures must be logged and visible but do not block
   • produce structured evidence output (JSON, HTML, or YAML) for each run
   • store validation results for historical trending and audit

5. Make evidence reviewable and actionable.

   • validation output should be human-readable, not just pass/fail
   • include row counts, failure percentages, and sample failing records
   • route evidence to dashboards, Slack, or incident workflows based on severity
   • link validation results to the specific pipeline run and data partition

6. Plan for expectation evolution and false positives.

   • as data changes, expectations need updates — budget for this work
   • define a process for reviewing and tuning noisy expectations
   • distinguish true data issues from expectation drift
   • retire expectations that no longer match the active contract

Common Rationalizations

Rationalization	Reality
"We'll add expectations later when we have time."	Quality frameworks deliver value only when integrated during pipeline development. Retrofitting is harder and often abandoned.
"Every column needs an expectation."	Over-instrumented suites create noise. Expectations should be grounded in contract requirements, not framework capability.
"The framework handles severity for us."	Severity classification requires human judgment about business impact. Frameworks provide mechanics, not decisions.
"Validation passed so the data is correct."	Validation proves conformance to documented expectations. It cannot catch requirements that were never specified.

Red Flags

• expectation suites with no connection to a dataset contract
• all checks are critical severity with no triage or routing plan
• validation output is discarded after each run — no historical evidence
• suites are never updated when contracts change
• framework is chosen based on hype instead of runtime compatibility
• validation runs after publish, not before
• no plan for handling noisy or flapping expectations
• checks exist but nobody reviews failures or trends

Verification

• Every expectation maps to a documented contract field or business rule
• Severity levels are defined: critical blocks publish, warning alerts, informational logs
• Framework choice matches the runtime context (Spark, warehouse, Pandas)
• Validation runs before publish and produces structured, stored evidence
• Evidence output includes row counts, failure rates, and sample records
• There is a process for reviewing, tuning, and evolving expectations over time
• Historical validation results are available for trend analysis and audit