Validating Data with Great Expectations

A practical approach to validating data in PySpark using declarative rules

Introduction

In data pipelines, data quality validation is essential to ensure that downstream transformations, reporting, and analytics operate on reliable inputs.

This article presents a scalable approach to validating data in PySpark using Great Expectations. It focuses on using ephemeral mode, which allows validation rules to be defined and executed e…