The speaker discusses the use of a graph database in the Kernel CI (Continuous Integration) project, which aims to be the official CI system for the Linux kernel. They explain the challenges in managing a large graph and the difficulties in dealing with test results from various testing systems. They introduce KCI DB as a solution to consolidate test results and issues into one dashboard and report. The speaker highlights the importance of prioritizing and filtering issues based on criteria such as commit history and test trends. They also discuss the limitations and difficulties they faced when using Neo4j as a graph database and propose using Git as a fallback plan. The speaker concludes by mentioning their interest in exploring other graph databases.