Automate DQ validation with ML and reduce cost by 50%


Without effective and comprehensive validation, a Data Lake becomes a data swamp. With the accelerating adoption of AWS S3 as the data lake of choice, the need for autonomously validating data has become critical.

While solutions like Deequ, Griffin, and Great Expectations provide the ability to validate AWS S3 data, these solutions rely on a rule-based approach that is not scalable for 100’s of data assets and are often prone to rules coverage issues.

More importantly, these solutions do provide an easy way to access the audit trail of results.


A scalable solution that can deliver trusted data for tens of 1,000’s of datasets has no option but to leverage AI/ML to autonomously track data and flag data errors.

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