Case Study: Patreon reduces data outages by 80% and accelerates data democratization with Datafold

Patreon reduces data incidents and expands data democratization with Datafold

Patreon, the creator platform serving 200k+ creators and 8 million patrons, faced growing data-quality and reliability challenges as it scaled and prepared for potential public scrutiny. With a small data team of 13 (one data engineer and 12 data scientists), Patreon struggled with pipeline breaks, lengthy PR hunts to find removed columns, and risky migrations (like adding multi-currency payments) that touched systems powering 80% of company data. To address these risks, Patreon evaluated alternatives and chose Datafold, initially deploying Datafold’s Data Diff to monitor changes between Redshift and production.

Datafold’s Data Diff plus its catalog and column-level lineage features were up and running quickly and gave Patreon clear, actionable visibility into pipeline changes and dependencies. As a result, Patreon significantly reduced incidents and sped onboarding — achieving an 80% decrease in data outages, a 50% increase in documented tables, and 50% faster new-hire onboarding — while improving data democratization and stakeholder confidence across the company.

Patreon

Maura Church

Director of Data Science