Case Study: Mayan reduces data merge time by 60% with Bigeye

Mayan uses Bigeye to validate data pipeline merges and deliver self-serve analytics dashboards at scale

Mayan, which delivers self-service analytics dashboards for Amazon sellers, needed a better way to trust and speed up its CI/CD data pipeline testing. Its small data engineering team was spending days debugging dbt job failures and lacked visibility into why merge requests passed or failed, which slowed data model changes and reduced confidence in the data.

Using Bigeye’s data observability platform, Mayan implemented blue-green deployment tests to compare staging and production tables, pinpoint failures quickly, and track merge performance over time. Bigeye helped cut average data model merge time from 4–5 days to 1 day, a 60% reduction, while improving transparency, reducing manual debugging, and freeing the team to focus on higher-value work.

Mayan

Loc Nguyen

Data Engineer