Rule-driven schema validation prevents data pipeline failures from schema drift—a simple, lightweight alternative to complex frameworks for reliable data quality.

A data architect's honest account of scrapping a half-built data validation platform to build what users actually want: a lightweight Python CLI tool.

Stop burning cloud budget on Spark for basic data validation. Learn why SQL pushdown is the smarter, faster way to ensure data quality and pipeline efficiency.

Building ValidateLite: A lightweight, zero-config data validation tool for data engineers and analysts. Get reliable data quality checks running in 30 seconds with code-first approach.

A plain-English explanation of what a Lakehouse is - the garage that finally got organized

A plain-English explanation of ETL vs ELT - like prep dinner at home vs prep dinner at the restaurant

How a systematic approach to data discrepancy investigation transforms chaos into clarity - learn the three-step framework for distinguishing missing data from calculation errors

Learn how snapshot tables provide instant time travel capabilities for data recovery. Master the art of protecting production data with simple yet powerful snapshot techniques.

Discover how differential checking acts as your data guardian, preventing disasters through systematic validation and monitoring of data changes.

Discover the five critical dimensions of data quality that transform chaotic reports into trustworthy insights. Learn how to measure and monitor completeness, accuracy, consistency, freshness, and ...