Implementing Data Quality Checks

Ensuring high‑quality data is fundamental to reliable analytics and AI. Below is a practical guide to designing, implementing, and automating data quality checks across your data pipelines.

1. Define Quality Rules

Completeness: No nulls in required columns.
Validity: Values conform to allowed domains.
Uniqueness: Primary keys must be unique.
Consistency: Referential integrity across tables.
Timeliness: Data freshness within SLA.

2. Choose a Framework

Popular open‑source and cloud solutions:

Great Expectations
Deequ (Spark)
Apache Griffin
Azure Data Factory & Azure Purview (for governance)

3. Sample Implementation (Great Expectations)

# Install
pip install great_expectations

# Initialize a project
great_expectations init

# Create a suite
great_expectations suite new my_suite

# Add expectations
import pandas as pd
df = pd.read_csv("sales.csv")
expectation_suite = context.get_expectation_suite("my_suite")
df.expect_column_values_to_not_be_null("order_id")
df.expect_column_values_to_be_in_set("region", ["EMEA","APAC","AMER"])
df.expect_table_row_count_to_be_between(min_value=1000, max_value=1000000)

# Validate
results = context.run_validation_operator(
    "action_list_operator",
    assets_to_validate=[Batch(data=df)]
)
print(results)

4. Automate Checks in CI/CD

Add validation step to pipeline YAML Fail build on critical violations Store validation reports in artifact storage

5. Alerting & Reporting

Integrate with monitoring tools (e.g., Azure Monitor, Datadog) to surface failures. Create a dashboard with key metrics:

Total checks run
Failed checks %
Time to resolve

6. Continuous Improvement

Review recurring failures, evolve rules, and involve data owners to refine expectations.