Case Study: Checkout.com achieves data reliability at scale with Monte Carlo

How Checkout.com Achieves Data Reliability at Scale with Monte Carlo

Checkout.com, a fast-growing fintech and payments company, needed a better way to ensure data reliability as its data volume and number of pipelines scaled. With thousands of data sets, 1,200+ dbt models, and a decentralized data structure, the team found that manual testing and monitoring were creating bottlenecks, false positives, and limited visibility across domains. To solve this, Checkout.com turned to Monte Carlo for data observability.

Monte Carlo helped Checkout.com automate and scale data quality monitoring with features like domains for data owners, Monitors as Code, a centralized incident UI, ML-based anomaly detection, and integrations with Datadog and PagerDuty. As a result, Checkout.com improved visibility across Snowflake, Airflow, dbt, and Looker, reduced reliance on manual checks, and resolved issues faster with fewer false positives. The company also strengthened confidence in its data across the business, supporting reliable operations at scale.

Checkout.com

Martynas Matimaitis

Senior Data Engineer