Yun-Shiuan Chuang, Sameer Narendran, Nikunj Harlalka, Alexander Cheung, Sizhe Gao, Siddharth Suresh, Junjie Hu, Timothy T. Rogers. Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing. 2025.

The requirements of Internet of Things (IoT) workloads are unique in the database space. While significant effort has been spent over the last decade rearchitecting OLTP and Analytics workloads for the public cloud, little has been done to rearchitect IoT workloads for the cloud. In this paper we present IBM Db2 Event Store ™ , a cloud-native database system designed specifically for IoT workloads, which require extremely high-speed ingest, efficient and open data storage, and near real-time analytics. Additionally, by leveraging the Db2 SQL compiler, optimizer and runtime, developed and refined over the last 30 years, we demonstrate that rearchitecting for the public cloud doesn't require rewriting all components. Reusing components that have been built out and optimized for decades dramatically reduced the development effort and immediately provided rich SQL support and excellent run-time query performance.