Case Study: Tenable Networks improves data quality and reduces pipeline latency with Monte Carlo

How Tenable Executes DataOps with Monte Carlo and Snowflake

Tenable Networks, a cybersecurity company, needed to ensure the high quality and freshness of the data powering its Tenable One Exposure Management Platform. Their challenge was to monitor this data for issues that traditional pipeline alerts might miss, requiring a solution that could provide a comprehensive view of data health across their globally distributed Snowflake accounts to maintain customer trust and support internal development.

The vendor, Monte Carlo, addressed this with its data observability platform. The solution leveraged out-of-the-box automated monitors and custom SQL monitors, deployed as code via YAML files, to track data freshness, failed queries, and long-running processes. This integration, particularly with Snowflake, provided a safety net for deployments and enabled end-to-end pipeline monitoring. As a result, Tenable engineers gained high confidence in their data, and one specific custom monitor helped reduce average data throughput latency in a key pipeline by 33% within three months.

Tenable Networks

Tom Milner

Director of Engineering