Implement data quality validation with Great Expectations, dbt tests, and data contracts. Use when building data quality pipelines, implementing validation rules, or establishing data contracts.
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Production patterns for implementing data quality with Great Expectations, dbt tests, and data contracts to ensure reliable data pipelines.
| 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 |
/\
/ \ Integration Tests (cross-table)
/────\
/ \ Unit Tests (single column)
/────────\
/ \ Schema Tests (structure)
/────────────\
# Install
pip install great_expectations
# Initialize project
great_expectations init
# Create datasource
great_expectations datasource new
# 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 pattern documentation lives in references/details.md. Read that file when the navigation tier above is insufficient.
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)
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)
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
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Set up and use 1Password CLI (op). Use when installing the CLI, enabling desktop app integration, signing in (single or multi-account), or reading/injecting/running secrets via op.
CLI to manage emails via IMAP/SMTP. Use `himalaya` to list, read, write, reply, forward, search, and organize emails from the terminal. Supports multiple accounts and message composition with MML (MIME Meta Language).