Case Study: Provectus improves data quality and pipeline observability with Great Expectations

How Provectus uses GX to monitor data pipelines, validate accuracy, and produce observable test results

Provectus, an AI consultancy and solutions provider, needed a way to keep data clean, reliable, and observable as it aggregated information from multiple internal sources into data lakes for analytics and AI work. To address recurring issues such as duplicates, empty values, and incorrect formats, the team used Great Expectations to improve data quality assurance and reduce the technical debt created by repeated pipeline fixes.

Great Expectations helped Provectus validate data accuracy and monitor pipelines by combining GX with AWS services, Allure, Pandas Profiling, and custom reporting adapters. The solution produced observable test results, sent alerts to Slack, and enabled comparison of producer and consumer data to verify correctness across systems. As a result, Provectus improved data quality, increased pipeline observability, and gave both engineers and business users easier access to trustworthy data insights.

Provectus

Andrew Khakhariev

Provectus