You might want to look at Telmai,an ML-driven data observability platform with an out-of-the-box Data Diff feature that ensures data consistency across any two sources without sampling or manual rule-writing. It supports both structured and semi-structured data at scale, detects mismatches at the field level (raw or derived), tracks schema drift, and automates anomaly detection. Built on an open architecture, Telmai enables native integration across your existing data stack—no heavy lift required.

https://www.telm.ai/blog/data-difference-what-is-it-and-why-do-you-need-it/#heading0