data-quality-frameworks

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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

Dimension	Description	Example 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

bash

# Install
pip install great_expectations

# Initialize project
great_expectations init

# Create datasource
great_expectations datasource new

python

# great_expectations/checkpoints/daily_validation.yml
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.

Summary: {totalpassed}/{totaltables} tables passed")

report.append("")

for table, result in results.items(): status = "✅" if result.passed else "❌" report.append(f"### {status} {table}") report.append(f"- Expectations: {result.totalexpectations}") report.append(f"- Failed: {result.failedexpectations}")

if not result.passed: report.append("- Failed checks:") for detail in result.details: if not detail["success"]: report.append(f" - {detail['expectation']}: {detail['observed_value']}") report.append("")

return "\n".join(report)

Usage

context = gx.get_context() pipeline = DataQualityPipeline(context)

tablestovalidate = { "orders": "orderssuite", "customers": "customerssuite", "products": "products_suite", }

results = pipeline.runall(tablestovalidate) report = pipeline.generatereport(results)

Fail pipeline if any table failed

if not all(r.passed for r in results.values()): print(report) raise ValueError("Data quality checks failed!")


## 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