  Growing data volumes and velocities in fields such as Industry 4.0 or the
Internet of Things have led to the increased popularity of data stream
processing systems. Enterprises can leverage these developments by enriching
their core business data and analyses with up-to-date streaming data. Comparing
streaming architectures for these complex use cases is challenging, as existing
benchmarks do not cover them. ESPBench is a new enterprise stream processing
benchmark that fills this gap. We present its architecture, the benchmarking
process, and the query workload. We employ ESPBench on three state-of-the-art
stream processing systems, Apache Spark, Apache Flink, and Hazelcast Jet, using
provided query implementations developed with Apache Beam. Our results
highlight the need for the provided ESPBench toolkit that supports benchmark
execution, as it enables query result validation and objective latency
measures.
