Case Study: Prefect saves 20+ hours per week with Monte Carlo

How Prefect Saved 20 Hours Per Week with Data Observability

Prefect, the creator of the data workflow management system, had a lean two-person data team managing increasingly complex pipelines across BigQuery, Fivetran, dbt, Looker, and internal Prefect flows. As the company scaled, the team struggled to keep up with schema changes, data quality issues, and the time required to root cause incidents, making comprehensive data reliability difficult to maintain without adding headcount.

To address this, Prefect implemented Monte Carlo’s data observability platform for its BigQuery warehouse, using no-code monitoring, ML-powered freshness/volume/schema checks, Slack alerting, and lineage tracking. Monte Carlo helped Prefect save 20+ hours per week, recover 50% of engineering time spent on detection and remediation, and reduce time to deploy data quality tooling by 6–8 months versus building a solution in-house.

Prefect

Dylan Hughes

Senior Software Engineer and Spiritual Data Scientist