data-quality-frameworks

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")
)

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

Patterns

Pattern 1: Great Expectations Suite

# expectations/orders_suite.py
import great_expectations as gx
from great_expectations.core import ExpectationSuite
from great_expectations.core.expectation_configuration import ExpectationConfiguration

def build_orders_suite() -> ExpectationSuite:
    """Build comprehensive orders expectation suite"""

    suite = ExpectationSuite(expectation_suite_name="orders_suite")

    # Schema expectations
    suite.add_expectation(ExpectationConfiguration(
        expectation_type="expect_table_columns_to_match_set",
        kwargs={
            "column_set": ["order_id", "customer_id", "amount", "status", "created_at"],
            "exact_match": False  # Allow additional columns
        }
    ))

    # Primary key
    suite.add_expectation(ExpectationConfiguration(
        expectation_type="expect_column_values_to_not_be_null",
        kwargs={"column": "order_id"}
    ))
    suite.add_expectation(ExpectationConfiguration(
        expectation_type="expect_column_values_to_be_unique",
        kwargs={"column": "order_id"}
    ))

    # Foreign key
    suite.add_expectation(ExpectationConfiguration(
        expectation_type="expect_column_values_to_not_be_null",
        kwargs={"column": "customer_id"}
    ))

    # Categorical values
    suite.add_expectation(ExpectationConfiguration(
        expectation_type="expect_column_values_to_be_in_set",
        kwargs={
            "column": "status",
            "value_set": ["pending", "processing", "shipped", "delivered", "cancelled"]
        }
    ))

    # Numeric ranges
    suite.add_expectation(ExpectationConfiguration(
        expectation_type="expect_column_values_to_be_between",
        kwargs={
            "column": "amount",
            "min_value": 0,
            "max_value": 100000,
            "strict_min": True  # amount > 0
        }
    ))

    # Date validity
    suite.add_expectation(ExpectationConfiguration(
        expectation_type="expect_column_values_to_be_dateutil_parseable",
        kwargs={"column": "created_at"}
    ))

    # Freshness - data should be recent
    suite.add_expectation(ExpectationConfiguration(
        expectation_type="expect_column_max_to_be_between",
        kwargs={
            "column": "created_at",
            "min_value": {"$PARAMETER": "now - timedelta(days=1)"},
            "max_value": {"$PARAMETER": "now"}
        }
    ))

    # Row count sanity
    suite.add_expectation(ExpectationConfiguration(
        expectation_type="expect_table_row_count_to_be_between",
        kwargs={
            "min_value": 1000,  # Expect at least 1000 rows
            "max_value": 10000000
        }
    ))

    # Statistical expectations
    suite.add_expectation(ExpectationConfiguration(
        expectation_type="expect_column_mean_to_be_between",
        kwargs={
            "column": "amount",
            "min_value": 50,
            "max_value": 500
        }
    ))

    return suite

Pattern 2: Great Expectations Checkpoint

# great_expectations/checkpoints/orders_checkpoint.yml
name: orders_checkpoint
config_version: 1.0
class_name: Checkpoint
run_name_template: "%Y%m%d-%H%M%S-orders-validation"

validations:
  - batch_request:
      datasource_name: warehouse
      data_connector_name: default_inferred_data_connector_name
      data_asset_name: orders
      data_connector_query:
        index: -1 # Latest batch
    expectation_suite_name: orders_suite

action_list:
  - name: store_validation_result
    action:
      class_name: StoreValidationResultAction

  - name: store_evaluation_parameters
    action:
      class_name: StoreEvaluationParametersAction

  - name: update_data_docs
    action:
      class_name: UpdateDataDocsAction

  # Slack notification on failure
  - name: send_slack_notification
    action:
      class_name: SlackNotificationAction
      slack_webhook: ${SLACK_WEBHOOK}
      notify_on: failure
      renderer:
        module_name: great_expectations.render.renderer.slack_renderer
        class_name: SlackRenderer

# Run checkpoint
import great_expectations as gx

context = gx.get_context()
result = context.run_checkpoint(checkpoint_name="orders_checkpoint")

if not result.success:
    failed_expectations = [
        r for r in result.run_results.values()
        if not r.success
    ]
    raise ValueError(f"Data quality check failed: {failed_expectations}")

Pattern 3: dbt Data Tests

# models/marts/core/_core__models.yml
version: 2

models:
  - name: fct_orders
    description: Order fact table
    tests:
      # Table-level tests
      - dbt_utils.recency:
          datepart: day
          field: created_at
          interval: 1
      - dbt_utils.at_least_one
      - dbt_utils.expression_is_true:
          expression: "total_amount >= 0"

    columns:
      - name: order_id
        description: Primary key
        tests:
          - unique
          - not_null

      - name: customer_id
        description: Foreign key to dim_customers
        tests:
          - not_null
          - relationships:
              to: ref('dim_customers')
              field: customer_id

      - name: order_status
        tests:
          - accepted_values:
              values:
                ["pending", "processing", "shipped", "delivered", "cancelled"]

      - name: total_amount
        tests:
          - not_null
          - dbt_utils.expression_is_true:
              expression: ">= 0"

      - name: created_at
        tests:
          - not_null
          - dbt_utils.expression_is_true:
              expression: "<= current_timestamp"

  - name: dim_customers
    columns:
      - name: customer_id
        tests:
          - unique
          - not_null

      - name: email
        tests:
          - unique
          - not_null
          # Custom regex test
          - dbt_utils.expression_is_true:
              expression: "email ~ '^[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\\.[A-Za-z]{2,}$'"

Pattern 4: Custom dbt Tests

-- tests/generic/test_row_count_in_range.sql
{% test row_count_in_range(model, min_count, max_count) %}

with row_count as (
    select count(*) as cnt from {{ model }}
)

select cnt
from row_count
where cnt < {{ min_count }} or cnt > {{ max_count }}

{% endtest %}

-- Usage in schema.yml:
-- tests:
--   - row_count_in_range:
--       min_count: 1000
--       max_count: 10000000

-- tests/generic/test_sequential_values.sql
{% test sequential_values(model, column_name, interval=1) %}

with lagged as (
    select
        {{ column_name }},
        lag({{ column_name }}) over (order by {{ column_name }}) as prev_value
    from {{ model }}
)

select *
from lagged
where {{ column_name }} - prev_value != {{ interval }}
  and prev_value is not null

{% endtest %}

-- tests/singular/assert_orders_customers_match.sql
-- Singular test: specific business rule

with orders_customers as (
    select distinct customer_id from {{ ref('fct_orders') }}
),

dim_customers as (
    select customer_id from {{ ref('dim_customers') }}
),

orphaned_orders as (
    select o.customer_id
    from orders_customers o
    left join dim_customers c using (customer_id)
    where c.customer_id is null
)

select * from orphaned_orders
-- Test passes if this returns 0 rows

Pattern 5: Data Contracts

# contracts/orders_contract.yaml
apiVersion: datacontract.com/v1.0.0
kind: DataContract
metadata:
  name: orders
  version: 1.0.0
  owner: data-platform-team
  contact: [email protected]

info:
  title: Orders Data Contract
  description: Contract for order event data from the ecommerce platform
  purpose: Analytics, reporting, and ML features

servers:
  production:
   

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