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

# 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")
)