Sumeh v3.0 offers unified data quality API across 14 engines with built-in row splitting

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Sumeh is an open-source Python library designed to run data validation rules against datasets using a single, consistent API across 14 engines, including Pandas, PySpark, BigQuery, Snowflake, and DuckDB. Unlike platforms such as Great Expectations or Soda, Sumeh positions itself as a lightweight library focused solely on validation without requiring cloud services or proprietary configuration formats. A key feature is its ability to split validated data into clean and quarantine rows in a single scan, avoiding the double-pass overhead seen in competing tools. On distributed engines like PySpark, the row-level bifurcation is built entirely from lazy Column expressions, meaning data is never pulled to the driver — addressing a known memory risk in tools like Deequ. Version 3.0, released this month, builds on the v2.0 architectural rewrite and introduces new functionality while maintaining full backward compatibility for existing v2.x users.

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