PyDeequ

About PyDeequ

Python API for Deequ, AWS library built on Apache Spark for defining and verifying data quality constraints. Useful for large-scale data processing and quality verification.

Key Features

• 1Port of Amazon Deequ for running data quality checks on Spark DataFrames
• 2Analyzers compute metrics: completeness, uniqueness, mean, correlation
• 3Constraint verification raises failures when metrics fall outside bounds
• 4Anomaly detection by comparing current metrics to historical baselines
• 5Results stored as JSON for downstream monitoring and alerting

How Python Data Engineers Use PyDeequ

Python data engineers use PyDeequ inside PySpark jobs to run statistical data quality checks at scale. Engineers define a `VerificationSuite` with constraints (e.g., completeness of a key column > 0.99), run it against a Spark DataFrame, and act on the results — logging failures, alerting on-call teams, or stopping the pipeline.