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.

Building on IBM-lead work on adding encryption to Apache Parquet files, we have implemented a prototype of how an end-to-end PayHowYouDrive (PHYD) solution, offering the power of Apache Spark analytics while protecting the privacy rights of the data subjects, can be securely implemented on a public cloud.

Protecting sensitive business and personal information is a cornerstone requirement when enterprises and organizations move to the cloud. Many aspects of this requirement are already handled at various levels. Data-at-rest can be secured in cloud stores by encrypting it before persisting the data to storage, while data-in-flight is transmitted using protected channels such as TLS and HTTPS. Data-in-use, processed in cloud compute nodes, is the most vulnerable link in the end-to-end information flow, since the process memory can be accessed by malicious privileged software or system administrators.

Enterprises and non-profit organizations work with sensitive commercial or personal information, stored in an encrypted form due to business confidentiality requirements, GDPR regulations [4] and other reasons. Unfortunately, a straightforward encryption doesn't work well for modern columnar data formats, such as Apache Parquet [2], that are leveraged by analytic frameworks for acceleration of data ingest and processing.

Enterprise data centers increasingly adopt a cloud-like architecture that enables the execution of multiple workloads on a shared pool of resources, reduces the data center footprint and drives down the costs. A number of cluster resource managers have appeared over the last few years, aimed at providing a uniform technology-neutral resource representation and management substrate. Examples include Apache YARN, Google Borg and Omega, Apache Mesos, and IBM Platform EGO.