PySpark Utils Library — A Comprehensive Guide [2026]

PySpark Utils Library Battle-tested utility functions for PySpark data engineering — transformations, data quality, SCD, schema evolution, logging, dedup, and DataFrame diffing. Stop rewriting the same PySpark boilerplate on every project. This library gives you the production-ready building blocks that data engineering teams use daily — fully typed, tested, and documented. What's Inside Module What It Does transformations 15 reusable DataFrame transforms: column cleaning, casting, flattening, pivoting, hashing data_quality Chainable DQ validation framework with structured reports and severity levels scd SCD Type 1 (overwrite) and Type 2 (full history) merge utilities for Delta Lake schema_utils Schema comparison, evolution, DDL conversion, and compatibility checking logging_utils Structured pipeline logging with correlation IDs, metrics, and Delta table sink dedup Window-based, hash-based, and fuzzy deduplication strategies diff DataFrame comparison with row-level, column-level, and s