Great Expectations Deequ And Cuallee

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

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