Starburst Gets 98.3% Noise Reduction with Endor Labs

Starburst, a data lakehouse built on Trino, faced challenges with their previous software composition analysis (SCA) tool, Rezillion, which struggled with providing accurate false positive data, handling transitive dependencies, and pre-deployment scanning. Seeking an improved solution, Starburst adopted Endor Labs, which offers advanced function-level reachability analysis and pre-deployment scanning, ensuring a comprehensive inventory of both direct and transitive dependencies. This transition led to a 98.3% noise reduction in SCA findings, enhancing the developer experience and allowing Starburst to maintain a secure, efficient data platform. Endor Labs' ease of implementation and strong support were pivotal in its selection, allowing Starburst to better prioritize risks and streamline their workflow, which contributed to its recognition as a SINET16 Innovator Award winner for its cybersecurity innovation.

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