In many application domains such as manufacturing, the integration and continuous processing of real-time sensor data from the Internet of Things (IoT) is crucial to continuously monitor and detect upcoming situations. While continuous processing of events in scalable architectures is already well supported by the existing Big Data tool landscape, building such applications requires technical effort and programming skills, which is often not present in manufacturing companies. To solve this problem, we have developed StreamPipes (https://www.streampipes.org), which aims at simplifying IoT data analytics for domain experts. Originating from a research project, StreamPipes has been publicly released under the Apache License in early 2018. At its core, StreamPipes provides non-technical users with an easy and intuitive way to create stream processing pipelines through a graphical editor. Pipelines consist of reusable pipeline elements, each of them implemented as a microservice using a wrapper for a specific Big Data technology (e.g., Apache Flink, Kafka Streams or lightweight runtimes that can run directly at the edge). We motivate our talk by showing examples we gathered from a number of industry projects during the past years in Industrial IoT domains, present technical details of StreamPipes and show how the tool eases the accessibility of Big Data tools for non-technical users.
Self-Service IoT Data Analytics with StreamPipes Dominik Riemer
September 12, 2019