Our modern world is increasingly dependent on continuous data streams that generate large volumes of data in real time. These streams might come from science experiments, weather stations, business applications, or sensors on a factory shop floor. Many of the software systems that interact with these data streams follow an architecture in which events drive individual components. Continuous data sources (producers) such as sensors trigger events, and various components (consumers) process them. Producers and consumers are decoupled using a middleware layer that handles the distribution of the data, usually in the form of a message broker. This approach reduces complexity, because any number of services can receive and process incoming data streams virtually simultaneously. This flexible architecture gives rise to a new generation of tools that provide users with an easy way to create custom solutions that process data from incoming streams. One example is the open source framework Apache StreamPipes [1].

StreamPipes has been an incubator project at the Apache Software Foundation since November 2019 and is part of a growing number of solutions for the Internet of Things (IoT). The StreamPipes toolbox [2] is aimed at business users with limited technical knowledge. The main goal is to make stream-processing technologies accessible to nonexperts. Various modules are available to connect IoT data streams from a variety of sources, to generate analyses of these data streams, and to examine live or historical data.

StreamPipes offers a variety of connectors and algorithms for analyzing industrial data, with the focus on integrating data from the production and automation environment. But users without access to their own production line can also benefit from StreamPipes: For example, real-time data from publicly available APIs and widely used protocols such as MQTT can be used to connect existing data sources.

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