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Case Study: Mux raises data test coverage from 10% to 95% with Metaplane

Mux Case StudySourced & dated by Case Study Desk
Key facts · TL;DR
Company
Mux
Industry
Technology
Challenge
The only data engineer had limited visibility; existing dbt tests covered only a small number of tables with manual thresholds.
Headline result
Mux's sole data engineer lifted data test coverage from 10% to 95% in a few clicks with Metaplane, saving about 8 hours a week

Key results

10% → 95%
Data test coverage
after adopting Metaplane
8 hrs/wk
Engineering time saved

The challenge

As the sole data engineer at Mux, a video API company, one analytics engineer owned the full stack from warehousing in Snowflake to transforming data in dbt for Looker dashboards used by marketing, sales, customer success, finance, and product. A previously hand-built observability tool took weeks to build yet provided only about 60% coverage, and extending dbt tests across the warehouse required time she did not have. Corrupt data could go unnoticed until stakeholders reported it.

The solution

After finding a reference to Metaplane in dbt's Slack community, she connected her data stack in a few clicks and set up monitoring far more comprehensive than her prior tool, gaining anomaly detection, lineage, and usage analytics in one place.

Metaplane has everything I need in one place. It gives me full visibility into the quality and performance of my data.

MP
Marion Pavillet
Senior Analytics Engineer, Mux

The results, in context

With Metaplane, Mux increased data test coverage from 10% to 95%, and more than 80% of alerts were legitimate issues requiring attention. When a table unexpectedly grew from millions to billions of rows overnight, Metaplane's alert saved an estimated eight hours that day and preserved stakeholder trust; the platform notifies the team about anomalies roughly three times per week.

Products used

Metaplane Metaplane