Correction at 23:30: Cthulhu.jl was written by Valentin Churavy. We present our use of Julia to build a new kind of cloud-native relational database management system for knowledge graphs. We motivate the need for a database that excels in supporting workloads that mix data management, machine learning, and graph analytics. We explain how Julia's unique features enabled us to build a high-performance database with less time and effort. We also discuss how we contribute to and leverage the Julia ecosystem. After a brief overview of knowledge graphs and the relational paradigm we describe the foundational design principles and implementation philosophy of the RelationalAI system: Leverage: how we use cloud computers and modern compilers to reduce engineering effort Brains and brawn: how we improve the asymptotic performance of our system with new algorithms that exploit problem structure Aggressive simplification and consolidation: how we build on 5 core capabilities that compose elegantly in ways that cover a broad range of use cases. We then review the first 3 of these 5 core capabilities (the query compiler, incremental maintenance, immutable structures) and touch on the remaining 2 (worst-case optimal joins and semantic optimization) Time Stamps: 00:00 Welcome! 00:10 Help us add time stamps or captions to this video! See the description for details. Want to help add timestamps to our YouTube videos to help with discoverability? Find out more here: https://github.com/JuliaCommunity/YouTubeVideoTimestamps Interested in improving the auto generated captions? Get involved here: https://github.com/JuliaCommunity/YouTubeVideoSubtitles