Data Quality Checks
Practical rules and checks that keep pipelines trustworthy.
Why Data Quality Matters
A pipeline can run successfully and still produce wrong results if the underlying data is incomplete, duplicated, or malformed. Data quality checks catch these problems before they reach dashboards and decision-makers.
Common Checks
Typical data quality checks include: verifying row counts fall within an expected range, confirming key columns have no missing values, checking that values fall within expected bounds, and ensuring no unexpected duplicate keys exist.
A Simple Validation Example
def validate(df):
assert df['customer_id'].isnull().sum() == 0, 'Missing customer_id values found'
assert df['order_amount'].min() >= 0, 'Negative order amounts detected'
assert df['order_id'].is_unique, 'Duplicate order_id values found'
print('All data quality checks passed.')Related Tutorials
Keep building your data engineering foundations.
Python for Data Cleaning
Use pandas to handle missing values, duplicates, and inconsistent formats.
Read guide →Building a Simple Data Pipeline
Combine extraction, transformation, and loading into one working example.
Read guide →Airflow Workflow Basics
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