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: {total_passed}/{total_tables} 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.total_expectations}") report.append(f"- Failed: {result.failed_expectations}")

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)

tables_to_validate = { "orders": "orders_suite", "customers": "customers_suite", "products": "products_suite", }

results = pipeline.run_all(tables_to_validate) report = pipeline.generate_report(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