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data-quality-frameworks

wshobson

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SKILL.md

Data Quality Frameworks

Production patterns for implementing data quality with Great Expectations, dbt tests, and data contracts to ensure reliable data pipelines.

When to Use This Skill

  • Implementing data quality checks in pipelines
  • Setting up Great Expectations validation
  • Building comprehensive dbt test suites
  • Establishing data contracts between teams
  • Monitoring data quality metrics
  • Automating data validation in CI/CD

Core Concepts

1. Data Quality Dimensions

DimensionDescriptionExample Check

Completeness

No missing values

expect_column_values_to_not_be_null

Uniqueness

No duplicates

expect_column_values_to_be_unique

Validity

Values in expected range

expect_column_values_to_be_in_set

Accuracy

Data matches reality

Cross-reference validation

Consistency

No contradictions

expect_column_pair_values_A_to_be_greater_than_B

Timeliness

Data is recent

expect_column_max_to_be_between

2. Testing Pyramid for Data

          /\
         /  \     Integration Tests (cross-table)
        /────\
       /      \   Unit Tests (single column)
      /────────\
     /          \ Schema Tests (structure)
    /────────────\

Quick Start

Great Expectations Setup

# Install
pip install great_expectations

# Initialize project
great_expectations init

# Create datasource
great_expectations datasource new
import great_expectations as gx

# Create context
context = gx.get_context()

# Create expectation suite
suite = context.add_expectation_suite("orders_suite")

# Add expectations
suite.add_expectation(
    gx.expectations.ExpectColumnValuesToNotBeNull(column="order_id")
)
suite.add_expectation(
    gx.expectations.ExpectColumnValuesToBeUnique(column="order_id")
)

# Validate
results = context.run_checkpoint(checkpoint_name="daily_orders")

Detailed patterns and worked examples

Detailed pattern documentation lives in references/details.md. Read that file when the navigation tier above is insufficient.

Best Practices

Do's

  • Test early - Validate source data before transformations
  • Test incrementally - Add tests as you find issues
  • Document expectations - Clear descriptions for each test
  • Alert on failures - Integrate with monitoring
  • Version contracts - Track schema changes

Don'ts

  • Don't test everything - Focus on critical columns
  • Don't ignore warnings - They often precede failures
  • Don't skip freshness - Stale data is bad data
  • Don't hardcode thresholds - Use dynamic baselines
  • Don't test in isolation - Test relationships too

Install

Install with chainctl
chainctl skill install public/wshobson/data-quality-frameworks:latest
Get started with chainctl
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Bring Chainguard's hardening pipeline to your first-party skills — custom rulesets, automated remediation, full audit trail.

Provenance

Upstream sourcehttps://github.com/wshobson/agents
Upstream commitbe57c0b2e3c05c528ca6132b87410b385718775f
OCI reference

skills.cgr.dev/public/github.com/wshobson/plugins/data-engineering/skills/data-quality-frameworks:latest

License

MIT

Files

HARDENING.md

LICENSE

SKILL.md

references/details.md


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