Scalable analytics on fast data

Today's streaming applications demand increasingly high event throughput rates and are often subject to strict latency constraints. To allow for more complex workloads, such as window-based aggregations, streaming systems need to support stateful event processing. This introduces new challenges for streaming engines as the state needs to be maintained in a consistent and durable manner and simultaneously accessed by complex queries for real-time analytics. Modern streaming systems, such as Apache Flink, do not allow for efficiently exposing the state to analytical queries. Thus, data engineers are forced to keep the state in external data stores, which significantly increases the latencies until events become visible to analytical queries. Proprietary solutions have been created to meet data freshness constraints. These solutions are expensive, error-prone, and difficult to maintain. Main-memory database systems, such as HyPer, achieve extremely low query response times while maintaining high update rates, which makes them well-suited for analytical streaming workloads. In this article, we explore extensions to database systems to match the performance and usability of streaming systems.