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

1. 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

1. 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

1. 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

1. 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

1. 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