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Data Quality (Great Expectations)

🛡️ Data Quality (Great Expectations & Pandera)

In Data Engineering, “Bad data in = Bad decisions out.” Data Quality is the practice of validating your data at every step of the pipeline.

🏗️ 1. Why Data Quality?

  • Schema Drift: When the source database changes a column type.
  • Null Values: When a mandatory field arrives empty.
  • Range Violations: When a “Percentage” field arrives with a value of 150.

🚀 2. Pandera: The Developer’s Choice

Pandera is a lightweight validation library for Pandas and Polars.

import pandera as pa

# Define a schema
schema = pa.DataFrameSchema({
    "age": pa.Column(int, pa.Check.in_range(0, 120)),
    "email": pa.Column(str, pa.Check.str_matches(r".+@.+\..+")),
    "salary": pa.Column(float, pa.Check.greater_than_0())
})

# Validate your DataFrame
validated_df = schema.validate(df)

📦 3. Great Expectations: The Enterprise Solution

Great Expectations is a robust framework for documenting and validating data. It creates interactive HTML reports (“Data Docs”) for your stakeholders.

Key Concepts:

  • Expectations: Assertions like expect_column_values_to_not_be_null.
  • Checkpoints: A specific run of expectations on a batch of data.
  • Validation Results: The outcome of the check (Pass/Fail).

🚦 4. Data Quality Best Practices

  1. Unit Testing: Write tests for your transformation logic.
  2. Integration Testing: Run checks on a sample of production data before full execution.
  3. Alerting: Integrate failed quality checks with Slack or PagerDuty.