FirstEigen’s DataBuck provides an autonomous, scalable framework for validating data in AWS S3, Azure ADLS, and GCP data lakes, preventing them from turning into data swamps. Instead of rigid, rules-based tools like Deequ and Great Expectations, DataBuck uses ML-driven, self-learning checks to automatically profile datasets, discover data quality rules, and continuously monitor hundreds of assets without manual rule maintenance. It detects anomalies, drift, and missing coverage, delivers clear Data Trust Scores, and integrates with modern cloud stacks, giving teams reliable, analytics-ready data with far less effort.

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